Give me a hint
-
+
The NNGraphParam constructor has an argument
cluster.args. This allows to specify arguments passed on to
@@ -3245,7 +3245,7 @@
Give me a hint
-
+
@@ -3275,7 +3275,7 @@ Exercise 2: Cluster annotation
TODO
Exercise 2: Cluster annotation
Give me a hint
-
+
Use the goana() function from the limma
package to identify GO BP terms that are overrepresented in the list of
@@ -3289,7 +3289,7 @@
Give me a hint
-
+
TODO
@@ -3320,7 +3320,7 @@ Exercise 3: Workflow
Show me the solution
-
+
TODO
@@ -5355,7 +5355,7 @@ Exercise 1: Out of memory representation
Give me a hint
-
+
See the HDF5Array function for reading from HDF5 and the
writeHDF5Array function for writing to HDF5 from the HDF5Array
@@ -5369,7 +5369,7 @@
Give me a hint
-
+
TODO
@@ -5397,7 +5397,7 @@ Exercise 2: Parallelization
Give me a hint
-
+
Use the function system.time to obtain the runtime of
each job.
@@ -5410,7 +5410,7 @@ Give me a hint
-
+
TODO
@@ -5440,7 +5440,7 @@ Exercise 3: Conversion to Seurat
Give me a hint
-
+
Use Seurat’s DimPlot function.
@@ -5452,7 +5452,7 @@ Give me a hint
-
+
Use Seurat’s DimPlot function.
@@ -5483,7 +5483,7 @@ ReferencesContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 |
+
Last updated on 2024-04-18 |
Edit this page
@@ -5497,7 +5497,8 @@ Overview
Questions
-- TODO
+- How to obtain comprehensive single-cell reference maps from the
+Human Cell Atlas?
@@ -5507,7 +5508,12 @@ Questions
Objectives
-- TODO
+- Learn about different resources for public single-cell RNA-seq
+data.
+- Learn how to access data from the Human Cell Atlas using the
+CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them
+into a SingleCellExperiment object.
@@ -5571,11 +5577,7 @@ The CuratedAtlasQueryR Project
-ERROR
-
-Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"
-
+
Data Sources in R / Bioconductor
@@ -5616,20 +5618,20 @@ Data Sources in R / Bioconductor
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5644,25 +5646,15 @@ HCA Metadata
+
R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)
-
-OUTPUT
-
-ℹ Downloading 1 file, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet
-
Get a view of the first 10 columns in the metadata with
glimpse
-
+
R
@@ -5675,7 +5667,7 @@ OUTPUT<
Rows: ??
Columns: 10
-Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
$ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG…
$ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", …
$ cell_type <chr> "mature NK T cell", "mature NK T cel…
@@ -5702,7 +5694,7 @@ A note on the piping operator
+
R
@@ -5726,7 +5718,7 @@ Summarizing the metadata
For each distinct tissue and dataset combination, count the number of
datasets by tissue type.
-
+
R
@@ -5738,26 +5730,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
tissue n
<chr> <dbl>
1 renal medulla 6
2 caecum 1
3 ileum 1
- 4 lymph node 2
- 5 transition zone of prostate 2
- 6 peripheral zone of prostate 2
+ 4 transition zone of prostate 2
+ 5 peripheral zone of prostate 2
+ 6 lymph node 2
7 fovea centralis 1
8 adrenal gland 1
- 9 heart left ventricle 7
-10 bone marrow 4
+ 9 blood 17
+10 kidney 8
# ℹ more rows
Columns available in the metadata
-
+
R
@@ -5766,19 +5758,13 @@ R
OUTPUT
-! The `names()` method of <tbl_lazy> is for internal use only.
-ℹ Did you mean `colnames()`?
-
-
-OUTPUT
-
[1] "src" "lazy_query"
Available assays
-
+
R
@@ -5790,26 +5776,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
assay n
<chr> <dbl>
- 1 10x 5' v2 2
- 2 sci-RNA-seq 1
- 3 10x 3' v1 1
- 4 Smart-seq2 1
- 5 10x 3' v3 21
- 6 Slide-seq 4
- 7 scRNA-seq 4
- 8 Seq-Well 2
- 9 10x 3' v2 27
-10 10x 5' v1 7
+ 1 10x 3' v2 27
+ 2 10x 5' v1 7
+ 3 10x 3' v3 21
+ 4 Slide-seq 4
+ 5 10x 5' v2 2
+ 6 sci-RNA-seq 1
+ 7 10x 3' v1 1
+ 8 Smart-seq2 1
+ 9 scRNA-seq 4
+10 Seq-Well 2
# ℹ more rows
Available organisms
-
+
R
@@ -5821,7 +5807,7 @@ ROUTPUT
# Source: SQL [1 x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
organism n
<chr> <dbl>
1 Homo sapiens 63
@@ -5837,7 +5823,7 @@ Download single-cell RNA seq
Query raw counts
-
+
R
@@ -5849,49 +5835,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment()
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.17 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment()
+
+single_cell_counts
OUTPUT
@@ -5915,7 +5861,7 @@ Query counts scaled per million
This is helpful if just few genes are of interest, as they can be
compared across samples.
-
+
R
@@ -5931,42 +5877,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.29 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 1571
metadata(0):
@@ -5984,7 +5894,7 @@ OUTPUT<
Extract only a subset of genes
-
+
R
@@ -5996,38 +5906,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment(assays = "cpm", features = "PUM1")
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 0 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment(assays = "cpm", features = "PUM1")
+
+single_cell_counts
OUTPUT
@@ -6052,7 +5933,7 @@ Extracting counts as a Seurat obje
If needed, the H5 SingleCellExperiment can be converted
into a Seurat object. Note that it may take a long time and use a lot of
memory depending on how many cells you are requesting.
-
+
R
@@ -6081,7 +5962,7 @@ Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Array package.
-
+
R
@@ -6111,9 +5992,9 @@ Exercise 1
Show me the solution
-
+
-
+
R
@@ -6145,9 +6026,9 @@ Exercise 2
Show me the solution
-
+
-
+
R
@@ -6179,9 +6060,9 @@ Exercise 3
Show me the solution
-
+
-
+
R
@@ -6221,9 +6102,9 @@ Exercise 4
Show me the solution
-
+
-
+
R
@@ -6241,41 +6122,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 12
metadata(0):
@@ -6307,15 +6153,6 @@ Key Points
-Acknowledgements
-
-Thank you to Stefano
-Mangiola and his team for developing CuratedAtlasQueryR
-and graciously providing the content from their vignette. Make sure to
-keep an eye out for their publication for proper citation. Their bioRxiv
-paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
Content from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 |
+
Last updated on 2024-04-18 |
Edit this page
Estimated time: 30 minutes
@@ -5505,7 +5505,8 @@ Overview
Questions
-- TODO
+- How to obtain comprehensive single-cell reference maps from the
+Human Cell Atlas?
Questions
Objectives
-- TODO
+- Learn about different resources for public single-cell RNA-seq
+data.
+- Learn how to access data from the Human Cell Atlas using the
+CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them
+into a SingleCellExperiment object.
@@ -5579,11 +5585,7 @@ The CuratedAtlasQueryR Project
-ERROR
-
-Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"
-
+
Data Sources in R / Bioconductor
@@ -5624,20 +5626,20 @@ Data Sources in R / Bioconductor
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5652,25 +5654,15 @@ HCA Metadata
+
R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)
-
-OUTPUT
-
-ℹ Downloading 1 file, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet
-
Get a view of the first 10 columns in the metadata with
glimpse
-
+
R
@@ -5683,7 +5675,7 @@ OUTPUT<
Rows: ??
Columns: 10
-Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
$ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG…
$ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", …
$ cell_type <chr> "mature NK T cell", "mature NK T cel…
@@ -5710,7 +5702,7 @@ A note on the piping operator
+
R
@@ -5734,7 +5726,7 @@ Summarizing the metadata
For each distinct tissue and dataset combination, count the number of
datasets by tissue type.
-
+
R
@@ -5746,26 +5738,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
tissue n
<chr> <dbl>
1 renal medulla 6
2 caecum 1
3 ileum 1
- 4 lymph node 2
- 5 transition zone of prostate 2
- 6 peripheral zone of prostate 2
+ 4 transition zone of prostate 2
+ 5 peripheral zone of prostate 2
+ 6 lymph node 2
7 fovea centralis 1
8 adrenal gland 1
- 9 heart left ventricle 7
-10 bone marrow 4
+ 9 blood 17
+10 kidney 8
# ℹ more rows
Columns available in the metadata
-
+
R
@@ -5774,19 +5766,13 @@ R
OUTPUT
-! The `names()` method of <tbl_lazy> is for internal use only.
-ℹ Did you mean `colnames()`?
-
-
-OUTPUT
-
[1] "src" "lazy_query"
Available assays
-
+
R
@@ -5798,26 +5784,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
assay n
<chr> <dbl>
- 1 10x 5' v2 2
- 2 sci-RNA-seq 1
- 3 10x 3' v1 1
- 4 Smart-seq2 1
- 5 10x 3' v3 21
- 6 Slide-seq 4
- 7 scRNA-seq 4
- 8 Seq-Well 2
- 9 10x 3' v2 27
-10 10x 5' v1 7
+ 1 10x 3' v2 27
+ 2 10x 5' v1 7
+ 3 10x 3' v3 21
+ 4 Slide-seq 4
+ 5 10x 5' v2 2
+ 6 sci-RNA-seq 1
+ 7 10x 3' v1 1
+ 8 Smart-seq2 1
+ 9 scRNA-seq 4
+10 Seq-Well 2
# ℹ more rows
Available organisms
-
+
R
@@ -5829,7 +5815,7 @@ ROUTPUT
# Source: SQL [1 x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
organism n
<chr> <dbl>
1 Homo sapiens 63
@@ -5845,7 +5831,7 @@ Download single-cell RNA seq
Query raw counts
-
+
R
@@ -5857,49 +5843,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment()
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.17 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment()
+
+single_cell_counts
OUTPUT
@@ -5923,7 +5869,7 @@ Query counts scaled per million
This is helpful if just few genes are of interest, as they can be
compared across samples.
-
+
R
@@ -5939,42 +5885,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.29 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 1571
metadata(0):
@@ -5992,7 +5902,7 @@ OUTPUT<
Extract only a subset of genes
-
+
R
@@ -6004,38 +5914,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment(assays = "cpm", features = "PUM1")
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 0 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment(assays = "cpm", features = "PUM1")
+
+single_cell_counts
OUTPUT
@@ -6060,7 +5941,7 @@ Extracting counts as a Seurat obje
If needed, the H5 SingleCellExperiment can be converted
into a Seurat object. Note that it may take a long time and use a lot of
memory depending on how many cells you are requesting.
-
+
R
@@ -6089,7 +5970,7 @@ Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Array package.
-
+
R
@@ -6119,9 +6000,9 @@ Exercise 1
Show me the solution
-
+
-
+
R
@@ -6153,9 +6034,9 @@ Exercise 2
Show me the solution
-
+
-
+
R
@@ -6187,9 +6068,9 @@ Exercise 3
Show me the solution
-
+
-
+
R
@@ -6229,9 +6110,9 @@ Exercise 4
Show me the solution
-
+
-
+
R
@@ -6249,41 +6130,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 12
metadata(0):
@@ -6315,15 +6161,6 @@ Key Points
-Acknowledgements
-
-Thank you to Stefano
-Mangiola and his team for developing CuratedAtlasQueryR
-and graciously providing the content from their vignette. Make sure to
-keep an eye out for their publication for proper citation. Their bioRxiv
-paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
Use the goana() function from the limma
package to identify GO BP terms that are overrepresented in the list of
@@ -3289,7 +3289,7 @@
Give me a hint
-
+
@@ -3320,7 +3320,7 @@ Exercise 3: Workflow
TODO
Exercise 3: Workflow
Show me the solution
-
+
TODO
@@ -5355,7 +5355,7 @@ Exercise 1: Out of memory representation
Give me a hint
-
+
See the HDF5Array function for reading from HDF5 and the
writeHDF5Array function for writing to HDF5 from the HDF5Array
@@ -5369,7 +5369,7 @@
Give me a hint
-
+
TODO
@@ -5397,7 +5397,7 @@ Exercise 2: Parallelization
Give me a hint
-
+
Use the function system.time to obtain the runtime of
each job.
@@ -5410,7 +5410,7 @@ Give me a hint
-
+
TODO
@@ -5440,7 +5440,7 @@ Exercise 3: Conversion to Seurat
Give me a hint
-
+
Use Seurat’s DimPlot function.
@@ -5452,7 +5452,7 @@ Give me a hint
-
+
Use Seurat’s DimPlot function.
@@ -5483,7 +5483,7 @@ ReferencesContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 |
+
Last updated on 2024-04-18 |
Edit this page
@@ -5497,7 +5497,8 @@ Overview
Questions
-- TODO
+- How to obtain comprehensive single-cell reference maps from the
+Human Cell Atlas?
@@ -5507,7 +5508,12 @@ Questions
Objectives
-- TODO
+- Learn about different resources for public single-cell RNA-seq
+data.
+- Learn how to access data from the Human Cell Atlas using the
+CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them
+into a SingleCellExperiment object.
@@ -5571,11 +5577,7 @@ The CuratedAtlasQueryR Project
-ERROR
-
-Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"
-
+Data Sources in R / Bioconductor
@@ -5616,20 +5618,20 @@ Data Sources in R / Bioconductor
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5644,25 +5646,15 @@ HCA Metadata
+
R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)
-
-OUTPUT
-
-ℹ Downloading 1 file, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet
-
Get a view of the first 10 columns in the metadata with
glimpse
-
+
R
@@ -5675,7 +5667,7 @@ OUTPUT<
Rows: ??
Columns: 10
-Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
$ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG…
$ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", …
$ cell_type <chr> "mature NK T cell", "mature NK T cel…
@@ -5702,7 +5694,7 @@ A note on the piping operator
+
R
@@ -5726,7 +5718,7 @@ Summarizing the metadata
For each distinct tissue and dataset combination, count the number of
datasets by tissue type.
-
+
R
@@ -5738,26 +5730,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
tissue n
<chr> <dbl>
1 renal medulla 6
2 caecum 1
3 ileum 1
- 4 lymph node 2
- 5 transition zone of prostate 2
- 6 peripheral zone of prostate 2
+ 4 transition zone of prostate 2
+ 5 peripheral zone of prostate 2
+ 6 lymph node 2
7 fovea centralis 1
8 adrenal gland 1
- 9 heart left ventricle 7
-10 bone marrow 4
+ 9 blood 17
+10 kidney 8
# ℹ more rows
Columns available in the metadata
-
+
R
@@ -5766,19 +5758,13 @@ R
OUTPUT
-! The `names()` method of <tbl_lazy> is for internal use only.
-ℹ Did you mean `colnames()`?
-
-
-OUTPUT
-
[1] "src" "lazy_query"
Available assays
-
+
R
@@ -5790,26 +5776,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
assay n
<chr> <dbl>
- 1 10x 5' v2 2
- 2 sci-RNA-seq 1
- 3 10x 3' v1 1
- 4 Smart-seq2 1
- 5 10x 3' v3 21
- 6 Slide-seq 4
- 7 scRNA-seq 4
- 8 Seq-Well 2
- 9 10x 3' v2 27
-10 10x 5' v1 7
+ 1 10x 3' v2 27
+ 2 10x 5' v1 7
+ 3 10x 3' v3 21
+ 4 Slide-seq 4
+ 5 10x 5' v2 2
+ 6 sci-RNA-seq 1
+ 7 10x 3' v1 1
+ 8 Smart-seq2 1
+ 9 scRNA-seq 4
+10 Seq-Well 2
# ℹ more rows
Available organisms
-
+
R
@@ -5821,7 +5807,7 @@ ROUTPUT
# Source: SQL [1 x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
organism n
<chr> <dbl>
1 Homo sapiens 63
@@ -5837,7 +5823,7 @@ Download single-cell RNA seq
Query raw counts
-
+
R
@@ -5849,49 +5835,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment()
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.17 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment()
+
+single_cell_counts
OUTPUT
@@ -5915,7 +5861,7 @@ Query counts scaled per million
This is helpful if just few genes are of interest, as they can be
compared across samples.
-
+
R
@@ -5931,42 +5877,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.29 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 1571
metadata(0):
@@ -5984,7 +5894,7 @@ OUTPUT<
Extract only a subset of genes
-
+
R
@@ -5996,38 +5906,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment(assays = "cpm", features = "PUM1")
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 0 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment(assays = "cpm", features = "PUM1")
+
+single_cell_counts
OUTPUT
@@ -6052,7 +5933,7 @@ Extracting counts as a Seurat obje
If needed, the H5 SingleCellExperiment can be converted
into a Seurat object. Note that it may take a long time and use a lot of
memory depending on how many cells you are requesting.
-
+
R
@@ -6081,7 +5962,7 @@ Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Array package.
-
+
R
@@ -6111,9 +5992,9 @@ Exercise 1
Show me the solution
-
+
-
+
R
@@ -6145,9 +6026,9 @@ Exercise 2
Show me the solution
-
+
-
+
R
@@ -6179,9 +6060,9 @@ Exercise 3
Show me the solution
-
+
-
+
R
@@ -6221,9 +6102,9 @@ Exercise 4
Show me the solution
-
+
-
+
R
@@ -6241,41 +6122,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 12
metadata(0):
@@ -6307,15 +6153,6 @@ Key Points
-Acknowledgements
-
-Thank you to Stefano
-Mangiola and his team for developing CuratedAtlasQueryR
-and graciously providing the content from their vignette. Make sure to
-keep an eye out for their publication for proper citation. Their bioRxiv
-paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
Content from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 |
+
Last updated on 2024-04-18 |
Edit this page
Estimated time: 30 minutes
@@ -5505,7 +5505,8 @@ Overview
Questions
-- TODO
+- How to obtain comprehensive single-cell reference maps from the
+Human Cell Atlas?
Questions
Objectives
-- TODO
+- Learn about different resources for public single-cell RNA-seq
+data.
+- Learn how to access data from the Human Cell Atlas using the
+CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them
+into a SingleCellExperiment object.
@@ -5579,11 +5585,7 @@ The CuratedAtlasQueryR Project
-ERROR
-
-Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"
-
+Data Sources in R / Bioconductor
@@ -5624,20 +5626,20 @@ Data Sources in R / Bioconductor
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5652,25 +5654,15 @@ HCA Metadata
+
R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)
-
-OUTPUT
-
-ℹ Downloading 1 file, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet
-
Get a view of the first 10 columns in the metadata with
glimpse
-
+
R
@@ -5683,7 +5675,7 @@ OUTPUT<
Rows: ??
Columns: 10
-Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
$ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG…
$ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", …
$ cell_type <chr> "mature NK T cell", "mature NK T cel…
@@ -5710,7 +5702,7 @@ A note on the piping operator
+
R
@@ -5734,7 +5726,7 @@ Summarizing the metadata
For each distinct tissue and dataset combination, count the number of
datasets by tissue type.
-
+
R
@@ -5746,26 +5738,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
tissue n
<chr> <dbl>
1 renal medulla 6
2 caecum 1
3 ileum 1
- 4 lymph node 2
- 5 transition zone of prostate 2
- 6 peripheral zone of prostate 2
+ 4 transition zone of prostate 2
+ 5 peripheral zone of prostate 2
+ 6 lymph node 2
7 fovea centralis 1
8 adrenal gland 1
- 9 heart left ventricle 7
-10 bone marrow 4
+ 9 blood 17
+10 kidney 8
# ℹ more rows
Columns available in the metadata
-
+
R
@@ -5774,19 +5766,13 @@ R
OUTPUT
-! The `names()` method of <tbl_lazy> is for internal use only.
-ℹ Did you mean `colnames()`?
-
-
-OUTPUT
-
[1] "src" "lazy_query"
Available assays
-
+
R
@@ -5798,26 +5784,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
assay n
<chr> <dbl>
- 1 10x 5' v2 2
- 2 sci-RNA-seq 1
- 3 10x 3' v1 1
- 4 Smart-seq2 1
- 5 10x 3' v3 21
- 6 Slide-seq 4
- 7 scRNA-seq 4
- 8 Seq-Well 2
- 9 10x 3' v2 27
-10 10x 5' v1 7
+ 1 10x 3' v2 27
+ 2 10x 5' v1 7
+ 3 10x 3' v3 21
+ 4 Slide-seq 4
+ 5 10x 5' v2 2
+ 6 sci-RNA-seq 1
+ 7 10x 3' v1 1
+ 8 Smart-seq2 1
+ 9 scRNA-seq 4
+10 Seq-Well 2
# ℹ more rows
Available organisms
-
+
R
@@ -5829,7 +5815,7 @@ ROUTPUT
# Source: SQL [1 x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
organism n
<chr> <dbl>
1 Homo sapiens 63
@@ -5845,7 +5831,7 @@ Download single-cell RNA seq
Query raw counts
-
+
R
@@ -5857,49 +5843,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment()
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.17 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment()
+
+single_cell_counts
OUTPUT
@@ -5923,7 +5869,7 @@ Query counts scaled per million
This is helpful if just few genes are of interest, as they can be
compared across samples.
-
+
R
@@ -5939,42 +5885,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.29 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 1571
metadata(0):
@@ -5992,7 +5902,7 @@ OUTPUT<
Extract only a subset of genes
-
+
R
@@ -6004,38 +5914,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment(assays = "cpm", features = "PUM1")
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 0 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment(assays = "cpm", features = "PUM1")
+
+single_cell_counts
OUTPUT
@@ -6060,7 +5941,7 @@ Extracting counts as a Seurat obje
If needed, the H5 SingleCellExperiment can be converted
into a Seurat object. Note that it may take a long time and use a lot of
memory depending on how many cells you are requesting.
-
+
R
@@ -6089,7 +5970,7 @@ Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Array package.
-
+
R
@@ -6119,9 +6000,9 @@ Exercise 1
Show me the solution
-
+
-
+
R
@@ -6153,9 +6034,9 @@ Exercise 2
Show me the solution
-
+
-
+
R
@@ -6187,9 +6068,9 @@ Exercise 3
Show me the solution
-
+
-
+
R
@@ -6229,9 +6110,9 @@ Exercise 4
Show me the solution
-
+
-
+
R
@@ -6249,41 +6130,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 12
metadata(0):
@@ -6315,15 +6161,6 @@ Key Points
-Acknowledgements
-
-Thank you to Stefano
-Mangiola and his team for developing CuratedAtlasQueryR
-and graciously providing the content from their vignette. Make sure to
-keep an eye out for their publication for proper citation. Their bioRxiv
-paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
TODO
Exercise 1: Out of memory representation
Give me a hint
-
+
See the HDF5Array function for reading from HDF5 and the
writeHDF5Array function for writing to HDF5 from the HDF5Array
@@ -5369,7 +5369,7 @@
Give me a hint
-
+
TODO
@@ -5397,7 +5397,7 @@ Exercise 2: Parallelization
Give me a hint
-
+
Use the function system.time to obtain the runtime of
each job.
@@ -5410,7 +5410,7 @@ Give me a hint
-
+
TODO
@@ -5440,7 +5440,7 @@ Exercise 3: Conversion to Seurat
Give me a hint
-
+
Use Seurat’s DimPlot function.
@@ -5452,7 +5452,7 @@ Give me a hint
-
+
Use Seurat’s DimPlot function.
@@ -5483,7 +5483,7 @@ ReferencesContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 |
+
Last updated on 2024-04-18 |
Edit this page
@@ -5497,7 +5497,8 @@ Overview
Questions
-- TODO
+- How to obtain comprehensive single-cell reference maps from the
+Human Cell Atlas?
@@ -5507,7 +5508,12 @@ Questions
Objectives
-- TODO
+- Learn about different resources for public single-cell RNA-seq
+data.
+- Learn how to access data from the Human Cell Atlas using the
+CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them
+into a SingleCellExperiment object.
@@ -5571,11 +5577,7 @@ The CuratedAtlasQueryR Project
-ERROR
-
-Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"
-
+Data Sources in R / Bioconductor
@@ -5616,20 +5618,20 @@ Data Sources in R / Bioconductor
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5644,25 +5646,15 @@ HCA Metadata
+
R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)
-
-OUTPUT
-
-ℹ Downloading 1 file, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet
-
Get a view of the first 10 columns in the metadata with
glimpse
-
+
R
@@ -5675,7 +5667,7 @@ OUTPUT<
Rows: ??
Columns: 10
-Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
$ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG…
$ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", …
$ cell_type <chr> "mature NK T cell", "mature NK T cel…
@@ -5702,7 +5694,7 @@ A note on the piping operator
+
R
@@ -5726,7 +5718,7 @@ Summarizing the metadata
For each distinct tissue and dataset combination, count the number of
datasets by tissue type.
-
+
R
@@ -5738,26 +5730,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
tissue n
<chr> <dbl>
1 renal medulla 6
2 caecum 1
3 ileum 1
- 4 lymph node 2
- 5 transition zone of prostate 2
- 6 peripheral zone of prostate 2
+ 4 transition zone of prostate 2
+ 5 peripheral zone of prostate 2
+ 6 lymph node 2
7 fovea centralis 1
8 adrenal gland 1
- 9 heart left ventricle 7
-10 bone marrow 4
+ 9 blood 17
+10 kidney 8
# ℹ more rows
Columns available in the metadata
-
+
R
@@ -5766,19 +5758,13 @@ R
OUTPUT
-! The `names()` method of <tbl_lazy> is for internal use only.
-ℹ Did you mean `colnames()`?
-
-
-OUTPUT
-
[1] "src" "lazy_query"
Available assays
-
+
R
@@ -5790,26 +5776,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
assay n
<chr> <dbl>
- 1 10x 5' v2 2
- 2 sci-RNA-seq 1
- 3 10x 3' v1 1
- 4 Smart-seq2 1
- 5 10x 3' v3 21
- 6 Slide-seq 4
- 7 scRNA-seq 4
- 8 Seq-Well 2
- 9 10x 3' v2 27
-10 10x 5' v1 7
+ 1 10x 3' v2 27
+ 2 10x 5' v1 7
+ 3 10x 3' v3 21
+ 4 Slide-seq 4
+ 5 10x 5' v2 2
+ 6 sci-RNA-seq 1
+ 7 10x 3' v1 1
+ 8 Smart-seq2 1
+ 9 scRNA-seq 4
+10 Seq-Well 2
# ℹ more rows
Available organisms
-
+
R
@@ -5821,7 +5807,7 @@ ROUTPUT
# Source: SQL [1 x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
organism n
<chr> <dbl>
1 Homo sapiens 63
@@ -5837,7 +5823,7 @@ Download single-cell RNA seq
Query raw counts
-
+
R
@@ -5849,49 +5835,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment()
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.17 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment()
+
+single_cell_counts
OUTPUT
@@ -5915,7 +5861,7 @@ Query counts scaled per million
This is helpful if just few genes are of interest, as they can be
compared across samples.
-
+
R
@@ -5931,42 +5877,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.29 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 1571
metadata(0):
@@ -5984,7 +5894,7 @@ OUTPUT<
Extract only a subset of genes
-
+
R
@@ -5996,38 +5906,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment(assays = "cpm", features = "PUM1")
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 0 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment(assays = "cpm", features = "PUM1")
+
+single_cell_counts
OUTPUT
@@ -6052,7 +5933,7 @@ Extracting counts as a Seurat obje
If needed, the H5 SingleCellExperiment can be converted
into a Seurat object. Note that it may take a long time and use a lot of
memory depending on how many cells you are requesting.
-
+
R
@@ -6081,7 +5962,7 @@ Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Array package.
-
+
R
@@ -6111,9 +5992,9 @@ Exercise 1
Show me the solution
-
+
-
+
R
@@ -6145,9 +6026,9 @@ Exercise 2
Show me the solution
-
+
-
+
R
@@ -6179,9 +6060,9 @@ Exercise 3
Show me the solution
-
+
-
+
R
@@ -6221,9 +6102,9 @@ Exercise 4
Show me the solution
-
+
-
+
R
@@ -6241,41 +6122,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 12
metadata(0):
@@ -6307,15 +6153,6 @@ Key Points
-Acknowledgements
-
-Thank you to Stefano
-Mangiola and his team for developing CuratedAtlasQueryR
-and graciously providing the content from their vignette. Make sure to
-keep an eye out for their publication for proper citation. Their bioRxiv
-paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
Content from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 |
+
Last updated on 2024-04-18 |
Edit this page
Estimated time: 30 minutes
@@ -5505,7 +5505,8 @@ Overview
Questions
-- TODO
+- How to obtain comprehensive single-cell reference maps from the
+Human Cell Atlas?
Questions
Objectives
-- TODO
+- Learn about different resources for public single-cell RNA-seq
+data.
+- Learn how to access data from the Human Cell Atlas using the
+CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them
+into a SingleCellExperiment object.
@@ -5579,11 +5585,7 @@ The CuratedAtlasQueryR Project
-ERROR
-
-Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"
-
+Data Sources in R / Bioconductor
@@ -5624,20 +5626,20 @@ Data Sources in R / Bioconductor
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5652,25 +5654,15 @@ HCA Metadata
+
R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)
-
-OUTPUT
-
-ℹ Downloading 1 file, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet
-
Get a view of the first 10 columns in the metadata with
glimpse
-
+
R
@@ -5683,7 +5675,7 @@ OUTPUT<
Rows: ??
Columns: 10
-Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
$ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG…
$ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", …
$ cell_type <chr> "mature NK T cell", "mature NK T cel…
@@ -5710,7 +5702,7 @@ A note on the piping operator
+
R
@@ -5734,7 +5726,7 @@ Summarizing the metadata
For each distinct tissue and dataset combination, count the number of
datasets by tissue type.
-
+
R
@@ -5746,26 +5738,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
tissue n
<chr> <dbl>
1 renal medulla 6
2 caecum 1
3 ileum 1
- 4 lymph node 2
- 5 transition zone of prostate 2
- 6 peripheral zone of prostate 2
+ 4 transition zone of prostate 2
+ 5 peripheral zone of prostate 2
+ 6 lymph node 2
7 fovea centralis 1
8 adrenal gland 1
- 9 heart left ventricle 7
-10 bone marrow 4
+ 9 blood 17
+10 kidney 8
# ℹ more rows
Columns available in the metadata
-
+
R
@@ -5774,19 +5766,13 @@ R
OUTPUT
-! The `names()` method of <tbl_lazy> is for internal use only.
-ℹ Did you mean `colnames()`?
-
-
-OUTPUT
-
[1] "src" "lazy_query"
Available assays
-
+
R
@@ -5798,26 +5784,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
assay n
<chr> <dbl>
- 1 10x 5' v2 2
- 2 sci-RNA-seq 1
- 3 10x 3' v1 1
- 4 Smart-seq2 1
- 5 10x 3' v3 21
- 6 Slide-seq 4
- 7 scRNA-seq 4
- 8 Seq-Well 2
- 9 10x 3' v2 27
-10 10x 5' v1 7
+ 1 10x 3' v2 27
+ 2 10x 5' v1 7
+ 3 10x 3' v3 21
+ 4 Slide-seq 4
+ 5 10x 5' v2 2
+ 6 sci-RNA-seq 1
+ 7 10x 3' v1 1
+ 8 Smart-seq2 1
+ 9 scRNA-seq 4
+10 Seq-Well 2
# ℹ more rows
Available organisms
-
+
R
@@ -5829,7 +5815,7 @@ ROUTPUT
# Source: SQL [1 x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
organism n
<chr> <dbl>
1 Homo sapiens 63
@@ -5845,7 +5831,7 @@ Download single-cell RNA seq
Query raw counts
-
+
R
@@ -5857,49 +5843,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment()
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.17 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment()
+
+single_cell_counts
OUTPUT
@@ -5923,7 +5869,7 @@ Query counts scaled per million
This is helpful if just few genes are of interest, as they can be
compared across samples.
-
+
R
@@ -5939,42 +5885,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.29 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 1571
metadata(0):
@@ -5992,7 +5902,7 @@ OUTPUT<
Extract only a subset of genes
-
+
R
@@ -6004,38 +5914,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment(assays = "cpm", features = "PUM1")
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 0 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment(assays = "cpm", features = "PUM1")
+
+single_cell_counts
OUTPUT
@@ -6060,7 +5941,7 @@ Extracting counts as a Seurat obje
If needed, the H5 SingleCellExperiment can be converted
into a Seurat object. Note that it may take a long time and use a lot of
memory depending on how many cells you are requesting.
-
+
R
@@ -6089,7 +5970,7 @@ Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Array package.
-
+
R
@@ -6119,9 +6000,9 @@ Exercise 1
Show me the solution
-
+
-
+
R
@@ -6153,9 +6034,9 @@ Exercise 2
Show me the solution
-
+
-
+
R
@@ -6187,9 +6068,9 @@ Exercise 3
Show me the solution
-
+
-
+
R
@@ -6229,9 +6110,9 @@ Exercise 4
Show me the solution
-
+
-
+
R
@@ -6249,41 +6130,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 12
metadata(0):
@@ -6315,15 +6161,6 @@ Key Points
-Acknowledgements
-
-Thank you to Stefano
-Mangiola and his team for developing CuratedAtlasQueryR
-and graciously providing the content from their vignette. Make sure to
-keep an eye out for their publication for proper citation. Their bioRxiv
-paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
See the HDF5Array function for reading from HDF5 and the
writeHDF5Array function for writing to HDF5 from the HDF5Array
@@ -5369,7 +5369,7 @@
Give me a hint
-
+
@@ -5397,7 +5397,7 @@ Exercise 2: Parallelization
TODO
Exercise 2: Parallelization
Give me a hint
-
+
Use the function system.time to obtain the runtime of
each job.
@@ -5410,7 +5410,7 @@ Give me a hint
-
+
TODO
@@ -5440,7 +5440,7 @@ Exercise 3: Conversion to Seurat
Give me a hint
-
+
Use Seurat’s DimPlot function.
@@ -5452,7 +5452,7 @@ Give me a hint
-
+
Use Seurat’s DimPlot function.
@@ -5483,7 +5483,7 @@ ReferencesContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 |
+
Last updated on 2024-04-18 |
Edit this page
@@ -5497,7 +5497,8 @@ Overview
Questions
-- TODO
+- How to obtain comprehensive single-cell reference maps from the
+Human Cell Atlas?
@@ -5507,7 +5508,12 @@ Questions
Objectives
-- TODO
+- Learn about different resources for public single-cell RNA-seq
+data.
+- Learn how to access data from the Human Cell Atlas using the
+CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them
+into a SingleCellExperiment object.
@@ -5571,11 +5577,7 @@ The CuratedAtlasQueryR Project
-ERROR
-
-Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"
-
+Data Sources in R / Bioconductor
@@ -5616,20 +5618,20 @@ Data Sources in R / Bioconductor
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5644,25 +5646,15 @@ HCA Metadata
+
R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)
-
-OUTPUT
-
-ℹ Downloading 1 file, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet
-
Get a view of the first 10 columns in the metadata with
glimpse
-
+
R
@@ -5675,7 +5667,7 @@ OUTPUT<
Rows: ??
Columns: 10
-Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
$ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG…
$ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", …
$ cell_type <chr> "mature NK T cell", "mature NK T cel…
@@ -5702,7 +5694,7 @@ A note on the piping operator
+
R
@@ -5726,7 +5718,7 @@ Summarizing the metadata
For each distinct tissue and dataset combination, count the number of
datasets by tissue type.
-
+
R
@@ -5738,26 +5730,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
tissue n
<chr> <dbl>
1 renal medulla 6
2 caecum 1
3 ileum 1
- 4 lymph node 2
- 5 transition zone of prostate 2
- 6 peripheral zone of prostate 2
+ 4 transition zone of prostate 2
+ 5 peripheral zone of prostate 2
+ 6 lymph node 2
7 fovea centralis 1
8 adrenal gland 1
- 9 heart left ventricle 7
-10 bone marrow 4
+ 9 blood 17
+10 kidney 8
# ℹ more rows
Columns available in the metadata
-
+
R
@@ -5766,19 +5758,13 @@ R
OUTPUT
-! The `names()` method of <tbl_lazy> is for internal use only.
-ℹ Did you mean `colnames()`?
-
-
-OUTPUT
-
[1] "src" "lazy_query"
Available assays
-
+
R
@@ -5790,26 +5776,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
assay n
<chr> <dbl>
- 1 10x 5' v2 2
- 2 sci-RNA-seq 1
- 3 10x 3' v1 1
- 4 Smart-seq2 1
- 5 10x 3' v3 21
- 6 Slide-seq 4
- 7 scRNA-seq 4
- 8 Seq-Well 2
- 9 10x 3' v2 27
-10 10x 5' v1 7
+ 1 10x 3' v2 27
+ 2 10x 5' v1 7
+ 3 10x 3' v3 21
+ 4 Slide-seq 4
+ 5 10x 5' v2 2
+ 6 sci-RNA-seq 1
+ 7 10x 3' v1 1
+ 8 Smart-seq2 1
+ 9 scRNA-seq 4
+10 Seq-Well 2
# ℹ more rows
Available organisms
-
+
R
@@ -5821,7 +5807,7 @@ ROUTPUT
# Source: SQL [1 x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
organism n
<chr> <dbl>
1 Homo sapiens 63
@@ -5837,7 +5823,7 @@ Download single-cell RNA seq
Query raw counts
-
+
R
@@ -5849,49 +5835,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment()
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.17 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment()
+
+single_cell_counts
OUTPUT
@@ -5915,7 +5861,7 @@ Query counts scaled per million
This is helpful if just few genes are of interest, as they can be
compared across samples.
-
+
R
@@ -5931,42 +5877,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.29 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 1571
metadata(0):
@@ -5984,7 +5894,7 @@ OUTPUT<
Extract only a subset of genes
-
+
R
@@ -5996,38 +5906,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment(assays = "cpm", features = "PUM1")
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 0 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment(assays = "cpm", features = "PUM1")
+
+single_cell_counts
OUTPUT
@@ -6052,7 +5933,7 @@ Extracting counts as a Seurat obje
If needed, the H5 SingleCellExperiment can be converted
into a Seurat object. Note that it may take a long time and use a lot of
memory depending on how many cells you are requesting.
-
+
R
@@ -6081,7 +5962,7 @@ Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Array package.
-
+
R
@@ -6111,9 +5992,9 @@ Exercise 1
Show me the solution
-
+
-
+
R
@@ -6145,9 +6026,9 @@ Exercise 2
Show me the solution
-
+
-
+
R
@@ -6179,9 +6060,9 @@ Exercise 3
Show me the solution
-
+
-
+
R
@@ -6221,9 +6102,9 @@ Exercise 4
Show me the solution
-
+
-
+
R
@@ -6241,41 +6122,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 12
metadata(0):
@@ -6307,15 +6153,6 @@ Key Points
-Acknowledgements
-
-Thank you to Stefano
-Mangiola and his team for developing CuratedAtlasQueryR
-and graciously providing the content from their vignette. Make sure to
-keep an eye out for their publication for proper citation. Their bioRxiv
-paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
Content from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 |
+
Last updated on 2024-04-18 |
Edit this page
Estimated time: 30 minutes
@@ -5505,7 +5505,8 @@ Overview
Questions
-- TODO
+- How to obtain comprehensive single-cell reference maps from the
+Human Cell Atlas?
Questions
Objectives
-- TODO
+- Learn about different resources for public single-cell RNA-seq
+data.
+- Learn how to access data from the Human Cell Atlas using the
+CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them
+into a SingleCellExperiment object.
@@ -5579,11 +5585,7 @@ The CuratedAtlasQueryR Project
-ERROR
-
-Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"
-
+Data Sources in R / Bioconductor
@@ -5624,20 +5626,20 @@ Data Sources in R / Bioconductor
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5652,25 +5654,15 @@ HCA Metadata
+
R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)
-
-OUTPUT
-
-ℹ Downloading 1 file, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet
-
Get a view of the first 10 columns in the metadata with
glimpse
-
+
R
@@ -5683,7 +5675,7 @@ OUTPUT<
Rows: ??
Columns: 10
-Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
$ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG…
$ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", …
$ cell_type <chr> "mature NK T cell", "mature NK T cel…
@@ -5710,7 +5702,7 @@ A note on the piping operator
+
R
@@ -5734,7 +5726,7 @@ Summarizing the metadata
For each distinct tissue and dataset combination, count the number of
datasets by tissue type.
-
+
R
@@ -5746,26 +5738,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
tissue n
<chr> <dbl>
1 renal medulla 6
2 caecum 1
3 ileum 1
- 4 lymph node 2
- 5 transition zone of prostate 2
- 6 peripheral zone of prostate 2
+ 4 transition zone of prostate 2
+ 5 peripheral zone of prostate 2
+ 6 lymph node 2
7 fovea centralis 1
8 adrenal gland 1
- 9 heart left ventricle 7
-10 bone marrow 4
+ 9 blood 17
+10 kidney 8
# ℹ more rows
Columns available in the metadata
-
+
R
@@ -5774,19 +5766,13 @@ R
OUTPUT
-! The `names()` method of <tbl_lazy> is for internal use only.
-ℹ Did you mean `colnames()`?
-
-
-OUTPUT
-
[1] "src" "lazy_query"
Available assays
-
+
R
@@ -5798,26 +5784,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
assay n
<chr> <dbl>
- 1 10x 5' v2 2
- 2 sci-RNA-seq 1
- 3 10x 3' v1 1
- 4 Smart-seq2 1
- 5 10x 3' v3 21
- 6 Slide-seq 4
- 7 scRNA-seq 4
- 8 Seq-Well 2
- 9 10x 3' v2 27
-10 10x 5' v1 7
+ 1 10x 3' v2 27
+ 2 10x 5' v1 7
+ 3 10x 3' v3 21
+ 4 Slide-seq 4
+ 5 10x 5' v2 2
+ 6 sci-RNA-seq 1
+ 7 10x 3' v1 1
+ 8 Smart-seq2 1
+ 9 scRNA-seq 4
+10 Seq-Well 2
# ℹ more rows
Available organisms
-
+
R
@@ -5829,7 +5815,7 @@ ROUTPUT
# Source: SQL [1 x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
organism n
<chr> <dbl>
1 Homo sapiens 63
@@ -5845,7 +5831,7 @@ Download single-cell RNA seq
Query raw counts
-
+
R
@@ -5857,49 +5843,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment()
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.17 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment()
+
+single_cell_counts
OUTPUT
@@ -5923,7 +5869,7 @@ Query counts scaled per million
This is helpful if just few genes are of interest, as they can be
compared across samples.
-
+
R
@@ -5939,42 +5885,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.29 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 1571
metadata(0):
@@ -5992,7 +5902,7 @@ OUTPUT<
Extract only a subset of genes
-
+
R
@@ -6004,38 +5914,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment(assays = "cpm", features = "PUM1")
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 0 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment(assays = "cpm", features = "PUM1")
+
+single_cell_counts
OUTPUT
@@ -6060,7 +5941,7 @@ Extracting counts as a Seurat obje
If needed, the H5 SingleCellExperiment can be converted
into a Seurat object. Note that it may take a long time and use a lot of
memory depending on how many cells you are requesting.
-
+
R
@@ -6089,7 +5970,7 @@ Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Array package.
-
+
R
@@ -6119,9 +6000,9 @@ Exercise 1
Show me the solution
-
+
-
+
R
@@ -6153,9 +6034,9 @@ Exercise 2
Show me the solution
-
+
-
+
R
@@ -6187,9 +6068,9 @@ Exercise 3
Show me the solution
-
+
-
+
R
@@ -6229,9 +6110,9 @@ Exercise 4
Show me the solution
-
+
-
+
R
@@ -6249,41 +6130,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 12
metadata(0):
@@ -6315,15 +6161,6 @@ Key Points
-Acknowledgements
-
-Thank you to Stefano
-Mangiola and his team for developing CuratedAtlasQueryR
-and graciously providing the content from their vignette. Make sure to
-keep an eye out for their publication for proper citation. Their bioRxiv
-paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
Use the function system.time to obtain the runtime of
each job.
Give me a hint
-
+
@@ -5440,7 +5440,7 @@ Exercise 3: Conversion to Seurat
TODO
Exercise 3: Conversion to Seurat
Give me a hint
-
+
Use Seurat’s DimPlot function.
@@ -5452,7 +5452,7 @@ Give me a hint
-
+
Use Seurat’s DimPlot function.
@@ -5483,7 +5483,7 @@ ReferencesContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 |
+
Last updated on 2024-04-18 |
Edit this page
@@ -5497,7 +5497,8 @@ Overview
Questions
-- TODO
+- How to obtain comprehensive single-cell reference maps from the
+Human Cell Atlas?
@@ -5507,7 +5508,12 @@ Questions
Objectives
-- TODO
+- Learn about different resources for public single-cell RNA-seq
+data.
+- Learn how to access data from the Human Cell Atlas using the
+CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them
+into a SingleCellExperiment object.
@@ -5571,11 +5577,7 @@ The CuratedAtlasQueryR Project
-ERROR
-
-Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"
-
+Data Sources in R / Bioconductor
@@ -5616,20 +5618,20 @@ Data Sources in R / Bioconductor
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5644,25 +5646,15 @@ HCA Metadata
+
R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)
-
-OUTPUT
-
-ℹ Downloading 1 file, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet
-
Get a view of the first 10 columns in the metadata with
glimpse
-
+
R
@@ -5675,7 +5667,7 @@ OUTPUT<
Rows: ??
Columns: 10
-Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
$ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG…
$ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", …
$ cell_type <chr> "mature NK T cell", "mature NK T cel…
@@ -5702,7 +5694,7 @@ A note on the piping operator
+
R
@@ -5726,7 +5718,7 @@ Summarizing the metadata
For each distinct tissue and dataset combination, count the number of
datasets by tissue type.
-
+
R
@@ -5738,26 +5730,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
tissue n
<chr> <dbl>
1 renal medulla 6
2 caecum 1
3 ileum 1
- 4 lymph node 2
- 5 transition zone of prostate 2
- 6 peripheral zone of prostate 2
+ 4 transition zone of prostate 2
+ 5 peripheral zone of prostate 2
+ 6 lymph node 2
7 fovea centralis 1
8 adrenal gland 1
- 9 heart left ventricle 7
-10 bone marrow 4
+ 9 blood 17
+10 kidney 8
# ℹ more rows
Columns available in the metadata
-
+
R
@@ -5766,19 +5758,13 @@ R
OUTPUT
-! The `names()` method of <tbl_lazy> is for internal use only.
-ℹ Did you mean `colnames()`?
-
-
-OUTPUT
-
[1] "src" "lazy_query"
Available assays
-
+
R
@@ -5790,26 +5776,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
assay n
<chr> <dbl>
- 1 10x 5' v2 2
- 2 sci-RNA-seq 1
- 3 10x 3' v1 1
- 4 Smart-seq2 1
- 5 10x 3' v3 21
- 6 Slide-seq 4
- 7 scRNA-seq 4
- 8 Seq-Well 2
- 9 10x 3' v2 27
-10 10x 5' v1 7
+ 1 10x 3' v2 27
+ 2 10x 5' v1 7
+ 3 10x 3' v3 21
+ 4 Slide-seq 4
+ 5 10x 5' v2 2
+ 6 sci-RNA-seq 1
+ 7 10x 3' v1 1
+ 8 Smart-seq2 1
+ 9 scRNA-seq 4
+10 Seq-Well 2
# ℹ more rows
Available organisms
-
+
R
@@ -5821,7 +5807,7 @@ ROUTPUT
# Source: SQL [1 x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
organism n
<chr> <dbl>
1 Homo sapiens 63
@@ -5837,7 +5823,7 @@ Download single-cell RNA seq
Query raw counts
-
+
R
@@ -5849,49 +5835,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment()
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.17 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment()
+
+single_cell_counts
OUTPUT
@@ -5915,7 +5861,7 @@ Query counts scaled per million
This is helpful if just few genes are of interest, as they can be
compared across samples.
-
+
R
@@ -5931,42 +5877,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.29 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 1571
metadata(0):
@@ -5984,7 +5894,7 @@ OUTPUT<
Extract only a subset of genes
-
+
R
@@ -5996,38 +5906,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment(assays = "cpm", features = "PUM1")
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 0 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment(assays = "cpm", features = "PUM1")
+
+single_cell_counts
OUTPUT
@@ -6052,7 +5933,7 @@ Extracting counts as a Seurat obje
If needed, the H5 SingleCellExperiment can be converted
into a Seurat object. Note that it may take a long time and use a lot of
memory depending on how many cells you are requesting.
-
+
R
@@ -6081,7 +5962,7 @@ Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Array package.
-
+
R
@@ -6111,9 +5992,9 @@ Exercise 1
Show me the solution
-
+
-
+
R
@@ -6145,9 +6026,9 @@ Exercise 2
Show me the solution
-
+
-
+
R
@@ -6179,9 +6060,9 @@ Exercise 3
Show me the solution
-
+
-
+
R
@@ -6221,9 +6102,9 @@ Exercise 4
Show me the solution
-
+
-
+
R
@@ -6241,41 +6122,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 12
metadata(0):
@@ -6307,15 +6153,6 @@ Key Points
-Acknowledgements
-
-Thank you to Stefano
-Mangiola and his team for developing CuratedAtlasQueryR
-and graciously providing the content from their vignette. Make sure to
-keep an eye out for their publication for proper citation. Their bioRxiv
-paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
Content from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 |
+
Last updated on 2024-04-18 |
Edit this page
Estimated time: 30 minutes
@@ -5505,7 +5505,8 @@ Overview
Questions
-- TODO
+- How to obtain comprehensive single-cell reference maps from the
+Human Cell Atlas?
Questions
Objectives
-- TODO
+- Learn about different resources for public single-cell RNA-seq
+data.
+- Learn how to access data from the Human Cell Atlas using the
+CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them
+into a SingleCellExperiment object.
@@ -5579,11 +5585,7 @@ The CuratedAtlasQueryR Project
-ERROR
-
-Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"
-
+Data Sources in R / Bioconductor
@@ -5624,20 +5626,20 @@ Data Sources in R / Bioconductor
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5652,25 +5654,15 @@ HCA Metadata
+
R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)
-
-OUTPUT
-
-ℹ Downloading 1 file, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet
-
Get a view of the first 10 columns in the metadata with
glimpse
-
+
R
@@ -5683,7 +5675,7 @@ OUTPUT<
Rows: ??
Columns: 10
-Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
$ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG…
$ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", …
$ cell_type <chr> "mature NK T cell", "mature NK T cel…
@@ -5710,7 +5702,7 @@ A note on the piping operator
+
R
@@ -5734,7 +5726,7 @@ Summarizing the metadata
For each distinct tissue and dataset combination, count the number of
datasets by tissue type.
-
+
R
@@ -5746,26 +5738,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
tissue n
<chr> <dbl>
1 renal medulla 6
2 caecum 1
3 ileum 1
- 4 lymph node 2
- 5 transition zone of prostate 2
- 6 peripheral zone of prostate 2
+ 4 transition zone of prostate 2
+ 5 peripheral zone of prostate 2
+ 6 lymph node 2
7 fovea centralis 1
8 adrenal gland 1
- 9 heart left ventricle 7
-10 bone marrow 4
+ 9 blood 17
+10 kidney 8
# ℹ more rows
Columns available in the metadata
-
+
R
@@ -5774,19 +5766,13 @@ R
OUTPUT
-! The `names()` method of <tbl_lazy> is for internal use only.
-ℹ Did you mean `colnames()`?
-
-
-OUTPUT
-
[1] "src" "lazy_query"
Available assays
-
+
R
@@ -5798,26 +5784,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
assay n
<chr> <dbl>
- 1 10x 5' v2 2
- 2 sci-RNA-seq 1
- 3 10x 3' v1 1
- 4 Smart-seq2 1
- 5 10x 3' v3 21
- 6 Slide-seq 4
- 7 scRNA-seq 4
- 8 Seq-Well 2
- 9 10x 3' v2 27
-10 10x 5' v1 7
+ 1 10x 3' v2 27
+ 2 10x 5' v1 7
+ 3 10x 3' v3 21
+ 4 Slide-seq 4
+ 5 10x 5' v2 2
+ 6 sci-RNA-seq 1
+ 7 10x 3' v1 1
+ 8 Smart-seq2 1
+ 9 scRNA-seq 4
+10 Seq-Well 2
# ℹ more rows
Available organisms
-
+
R
@@ -5829,7 +5815,7 @@ ROUTPUT
# Source: SQL [1 x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
organism n
<chr> <dbl>
1 Homo sapiens 63
@@ -5845,7 +5831,7 @@ Download single-cell RNA seq
Query raw counts
-
+
R
@@ -5857,49 +5843,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment()
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.17 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment()
+
+single_cell_counts
OUTPUT
@@ -5923,7 +5869,7 @@ Query counts scaled per million
This is helpful if just few genes are of interest, as they can be
compared across samples.
-
+
R
@@ -5939,42 +5885,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.29 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 1571
metadata(0):
@@ -5992,7 +5902,7 @@ OUTPUT<
Extract only a subset of genes
-
+
R
@@ -6004,38 +5914,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment(assays = "cpm", features = "PUM1")
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 0 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment(assays = "cpm", features = "PUM1")
+
+single_cell_counts
OUTPUT
@@ -6060,7 +5941,7 @@ Extracting counts as a Seurat obje
If needed, the H5 SingleCellExperiment can be converted
into a Seurat object. Note that it may take a long time and use a lot of
memory depending on how many cells you are requesting.
-
+
R
@@ -6089,7 +5970,7 @@ Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Array package.
-
+
R
@@ -6119,9 +6000,9 @@ Exercise 1
Show me the solution
-
+
-
+
R
@@ -6153,9 +6034,9 @@ Exercise 2
Show me the solution
-
+
-
+
R
@@ -6187,9 +6068,9 @@ Exercise 3
Show me the solution
-
+
-
+
R
@@ -6229,9 +6110,9 @@ Exercise 4
Show me the solution
-
+
-
+
R
@@ -6249,41 +6130,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 12
metadata(0):
@@ -6315,15 +6161,6 @@ Key Points
-Acknowledgements
-
-Thank you to Stefano
-Mangiola and his team for developing CuratedAtlasQueryR
-and graciously providing the content from their vignette. Make sure to
-keep an eye out for their publication for proper citation. Their bioRxiv
-paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
Use Seurat’s DimPlot function.
Give me a hint
-
+
@@ -5483,7 +5483,7 @@ References
@@ -5571,11 +5577,7 @@ The CuratedAtlasQueryR Project
-
+Use Seurat’s DimPlot function.
ReferencesContent from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 |
+
Last updated on 2024-04-18 |
Edit this page
@@ -5497,7 +5497,8 @@ Overview
Questions
-- TODO
+- How to obtain comprehensive single-cell reference maps from the
+Human Cell Atlas?
@@ -5507,7 +5508,12 @@ Questions
Objectives
-- TODO
+- Learn about different resources for public single-cell RNA-seq
+data.
+- Learn how to access data from the Human Cell Atlas using the
+CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them
+into a SingleCellExperiment object.
Overview
Questions
-
-
- TODO +
- How to obtain comprehensive single-cell reference maps from the +Human Cell Atlas?
Objectives
-
-
- TODO +
- Learn about different resources for public single-cell RNA-seq +data. +
- Learn how to access data from the Human Cell Atlas using the +CuratedAtlasQueryR package. +
- Learn how to query for cells of interest and how to download them +into a SingleCellExperiment object.
The CuratedAtlasQueryR Project
-ERROR
-
-Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"
-
Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"Data Sources in R / Bioconductor
@@ -5616,20 +5618,20 @@ Data Sources in R / Bioconductor
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5644,25 +5646,15 @@ HCA Metadata
+
R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)
-
-OUTPUT
-
-ℹ Downloading 1 file, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet
-
Get a view of the first 10 columns in the metadata with
glimpse
-
+
R
@@ -5675,7 +5667,7 @@ OUTPUT<
Rows: ??
Columns: 10
-Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
$ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG…
$ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", …
$ cell_type <chr> "mature NK T cell", "mature NK T cel…
@@ -5702,7 +5694,7 @@ A note on the piping operator
+
R
@@ -5726,7 +5718,7 @@ Summarizing the metadata
For each distinct tissue and dataset combination, count the number of
datasets by tissue type.
-
+
R
@@ -5738,26 +5730,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
tissue n
<chr> <dbl>
1 renal medulla 6
2 caecum 1
3 ileum 1
- 4 lymph node 2
- 5 transition zone of prostate 2
- 6 peripheral zone of prostate 2
+ 4 transition zone of prostate 2
+ 5 peripheral zone of prostate 2
+ 6 lymph node 2
7 fovea centralis 1
8 adrenal gland 1
- 9 heart left ventricle 7
-10 bone marrow 4
+ 9 blood 17
+10 kidney 8
# ℹ more rows
Columns available in the metadata
-
+
R
@@ -5766,19 +5758,13 @@ R
OUTPUT
-! The `names()` method of <tbl_lazy> is for internal use only.
-ℹ Did you mean `colnames()`?
-
-
-OUTPUT
-
[1] "src" "lazy_query"
Available assays
-
+
R
@@ -5790,26 +5776,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
assay n
<chr> <dbl>
- 1 10x 5' v2 2
- 2 sci-RNA-seq 1
- 3 10x 3' v1 1
- 4 Smart-seq2 1
- 5 10x 3' v3 21
- 6 Slide-seq 4
- 7 scRNA-seq 4
- 8 Seq-Well 2
- 9 10x 3' v2 27
-10 10x 5' v1 7
+ 1 10x 3' v2 27
+ 2 10x 5' v1 7
+ 3 10x 3' v3 21
+ 4 Slide-seq 4
+ 5 10x 5' v2 2
+ 6 sci-RNA-seq 1
+ 7 10x 3' v1 1
+ 8 Smart-seq2 1
+ 9 scRNA-seq 4
+10 Seq-Well 2
# ℹ more rows
Available organisms
-
+
R
@@ -5821,7 +5807,7 @@ ROUTPUT
# Source: SQL [1 x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
organism n
<chr> <dbl>
1 Homo sapiens 63
@@ -5837,7 +5823,7 @@ Download single-cell RNA seq
Query raw counts
-
+
R
@@ -5849,49 +5835,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment()
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.17 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment()
+
+single_cell_counts
OUTPUT
@@ -5915,7 +5861,7 @@ Query counts scaled per million
This is helpful if just few genes are of interest, as they can be
compared across samples.
-
+
R
@@ -5931,42 +5877,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.29 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 1571
metadata(0):
@@ -5984,7 +5894,7 @@ OUTPUT<
Extract only a subset of genes
-
+
R
@@ -5996,38 +5906,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment(assays = "cpm", features = "PUM1")
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 0 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment(assays = "cpm", features = "PUM1")
+
+single_cell_counts
OUTPUT
@@ -6052,7 +5933,7 @@ Extracting counts as a Seurat obje
If needed, the H5 SingleCellExperiment can be converted
into a Seurat object. Note that it may take a long time and use a lot of
memory depending on how many cells you are requesting.
-
+
R
@@ -6081,7 +5962,7 @@ Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Array package.
-
+
R
@@ -6111,9 +5992,9 @@ Exercise 1
Show me the solution
-
+
-
+
R
@@ -6145,9 +6026,9 @@ Exercise 2
Show me the solution
-
+
-
+
R
@@ -6179,9 +6060,9 @@ Exercise 3
Show me the solution
-
+
-
+
R
@@ -6221,9 +6102,9 @@ Exercise 4
Show me the solution
-
+
-
+
R
@@ -6241,41 +6122,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 12
metadata(0):
@@ -6307,15 +6153,6 @@ Key Points
-Acknowledgements
-
-Thank you to Stefano
-Mangiola and his team for developing CuratedAtlasQueryR
-and graciously providing the content from their vignette. Make sure to
-keep an eye out for their publication for proper citation. Their bioRxiv
-paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
Content from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 |
+
Last updated on 2024-04-18 |
Edit this page
Estimated time: 30 minutes
@@ -5505,7 +5505,8 @@ Overview
Questions
-- TODO
+- How to obtain comprehensive single-cell reference maps from the
+Human Cell Atlas?
Questions
Objectives
-- TODO
+- Learn about different resources for public single-cell RNA-seq
+data.
+- Learn how to access data from the Human Cell Atlas using the
+CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them
+into a SingleCellExperiment object.
@@ -5579,11 +5585,7 @@ The CuratedAtlasQueryR Project
-ERROR
-
-Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"
-
+Data Sources in R / Bioconductor
@@ -5624,20 +5626,20 @@ Data Sources in R / Bioconductor
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5652,25 +5654,15 @@ HCA Metadata
+
R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)
-
-OUTPUT
-
-ℹ Downloading 1 file, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet
-
Get a view of the first 10 columns in the metadata with
glimpse
-
+
R
@@ -5683,7 +5675,7 @@ OUTPUT<
Rows: ??
Columns: 10
-Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
$ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG…
$ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", …
$ cell_type <chr> "mature NK T cell", "mature NK T cel…
@@ -5710,7 +5702,7 @@ A note on the piping operator
+
R
@@ -5734,7 +5726,7 @@ Summarizing the metadata
For each distinct tissue and dataset combination, count the number of
datasets by tissue type.
-
+
R
@@ -5746,26 +5738,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
tissue n
<chr> <dbl>
1 renal medulla 6
2 caecum 1
3 ileum 1
- 4 lymph node 2
- 5 transition zone of prostate 2
- 6 peripheral zone of prostate 2
+ 4 transition zone of prostate 2
+ 5 peripheral zone of prostate 2
+ 6 lymph node 2
7 fovea centralis 1
8 adrenal gland 1
- 9 heart left ventricle 7
-10 bone marrow 4
+ 9 blood 17
+10 kidney 8
# ℹ more rows
Columns available in the metadata
-
+
R
@@ -5774,19 +5766,13 @@ R
OUTPUT
-! The `names()` method of <tbl_lazy> is for internal use only.
-ℹ Did you mean `colnames()`?
-
-
-OUTPUT
-
[1] "src" "lazy_query"
Available assays
-
+
R
@@ -5798,26 +5784,26 @@ ROUTPUT
# Source: SQL [?? x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
assay n
<chr> <dbl>
- 1 10x 5' v2 2
- 2 sci-RNA-seq 1
- 3 10x 3' v1 1
- 4 Smart-seq2 1
- 5 10x 3' v3 21
- 6 Slide-seq 4
- 7 scRNA-seq 4
- 8 Seq-Well 2
- 9 10x 3' v2 27
-10 10x 5' v1 7
+ 1 10x 3' v2 27
+ 2 10x 5' v1 7
+ 3 10x 3' v3 21
+ 4 Slide-seq 4
+ 5 10x 5' v2 2
+ 6 sci-RNA-seq 1
+ 7 10x 3' v1 1
+ 8 Smart-seq2 1
+ 9 scRNA-seq 4
+10 Seq-Well 2
# ℹ more rows
Available organisms
-
+
R
@@ -5829,7 +5815,7 @@ ROUTPUT
# Source: SQL [1 x 2]
-# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:]
+# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:]
organism n
<chr> <dbl>
1 Homo sapiens 63
@@ -5845,7 +5831,7 @@ Download single-cell RNA seq
Query raw counts
-
+
R
@@ -5857,49 +5843,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment()
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.17 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment()
+
+single_cell_counts
OUTPUT
@@ -5923,7 +5869,7 @@ Query counts scaled per million
This is helpful if just few genes are of interest, as they can be
compared across samples.
-
+
R
@@ -5939,42 +5885,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0.29 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds
-
-
-OUTPUT
-
-Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files.
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 1571
metadata(0):
@@ -5992,7 +5902,7 @@ OUTPUT<
Extract only a subset of genes
-
+
R
@@ -6004,38 +5914,9 @@ R tissue == "lung parenchyma" &
stringr::str_like(cell_type, "%CD4%")
) |>
- get_single_cell_experiment(assays = "cpm", features = "PUM1")
-
-
-OUTPUT
-
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 0 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-R
-
-
-single_cell_counts
+ get_single_cell_experiment(assays = "cpm", features = "PUM1")
+
+single_cell_counts
OUTPUT
@@ -6060,7 +5941,7 @@ Extracting counts as a Seurat obje
If needed, the H5 SingleCellExperiment can be converted
into a Seurat object. Note that it may take a long time and use a lot of
memory depending on how many cells you are requesting.
-
+
R
@@ -6089,7 +5970,7 @@ Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Array package.
-
+
R
@@ -6119,9 +6000,9 @@ Exercise 1
Show me the solution
-
+
-
+
R
@@ -6153,9 +6034,9 @@ Exercise 2
Show me the solution
-
+
-
+
R
@@ -6187,9 +6068,9 @@ Exercise 3
Show me the solution
-
+
-
+
R
@@ -6229,9 +6110,9 @@ Exercise 4
Show me the solution
-
+
-
+
R
@@ -6249,41 +6130,6 @@ R
OUTPUT
-ℹ Realising metadata.
-
-
-OUTPUT
-
-ℹ Synchronising files
-
-
-OUTPUT
-
-ℹ Downloading 2 files, totalling 0 GB
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5
-
-
-OUTPUT
-
-ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds
-
-
-OUTPUT
-
-ℹ Reading files.
-
-
-OUTPUT
-
-ℹ Compiling Single Cell Experiment.
-
-
-OUTPUT
-
class: SingleCellExperiment
dim: 36229 12
metadata(0):
@@ -6315,15 +6161,6 @@ Key Points
-Acknowledgements
-
-Thank you to Stefano
-Mangiola and his team for developing CuratedAtlasQueryR
-and graciously providing the content from their vignette. Make sure to
-keep an eye out for their publication for proper citation. Their bioRxiv
-paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
Installation
-
+
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
-BiocManager::install("stemangiola/CuratedAtlasQueryR")
+BiocManager::install("CuratedAtlasQueryR")
Package load
-
+
R
@@ -5644,25 +5646,15 @@HCA Metadata +
-R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)--OUTPUT -
--ℹ Downloading 1 file, totalling 0 GB-OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquetGet a view of the first 10 columns in the metadata with
-glimpse+R
@@ -5675,7 +5667,7 @@OUTPUT<
Rows: ?? Columns: 10 -Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:] +Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:] $ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG… $ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", … $ cell_type <chr> "mature NK T cell", "mature NK T cel… @@ -5702,7 +5694,7 @@A note on the piping operator +
R
@@ -5726,7 +5718,7 @@Summarizing the metadata
For each distinct tissue and dataset combination, count the number of datasets by tissue type.
-+R
@@ -5738,26 +5730,26 @@ROUTPUT
# Source: SQL [?? x 2] -# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:] +# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:] tissue n <chr> <dbl> 1 renal medulla 6 2 caecum 1 3 ileum 1 - 4 lymph node 2 - 5 transition zone of prostate 2 - 6 peripheral zone of prostate 2 + 4 transition zone of prostate 2 + 5 peripheral zone of prostate 2 + 6 lymph node 2 7 fovea centralis 1 8 adrenal gland 1 - 9 heart left ventricle 7 -10 bone marrow 4 + 9 blood 17 +10 kidney 8 # ℹ more rowsColumns available in the metadata
-+-R
@@ -5766,19 +5758,13 @@R
OUTPUT
--! The `names()` method of <tbl_lazy> is for internal use only. -ℹ Did you mean `colnames()`?-OUTPUT -
[1] "src" "lazy_query"Available assays
-+R
@@ -5790,26 +5776,26 @@ROUTPUT
# Source: SQL [?? x 2] -# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:] +# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:] assay n <chr> <dbl> - 1 10x 5' v2 2 - 2 sci-RNA-seq 1 - 3 10x 3' v1 1 - 4 Smart-seq2 1 - 5 10x 3' v3 21 - 6 Slide-seq 4 - 7 scRNA-seq 4 - 8 Seq-Well 2 - 9 10x 3' v2 27 -10 10x 5' v1 7 + 1 10x 3' v2 27 + 2 10x 5' v1 7 + 3 10x 3' v3 21 + 4 Slide-seq 4 + 5 10x 5' v2 2 + 6 sci-RNA-seq 1 + 7 10x 3' v1 1 + 8 Smart-seq2 1 + 9 scRNA-seq 4 +10 Seq-Well 2 # ℹ more rowsAvailable organisms
-+R
@@ -5821,7 +5807,7 @@ROUTPUT
@@ -5837,7 +5823,7 @@# Source: SQL [1 x 2] -# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:] +# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:] organism n <chr> <dbl> 1 Homo sapiens 63Download single-cell RNA seq
Query raw counts
-+-R
@@ -5849,49 +5835,9 @@-R tissue == "lung parenchyma" & stringr::str_like(cell_type, "%CD4%") ) |> - get_single_cell_experiment()
--OUTPUT -
--ℹ Realising metadata.--OUTPUT -
--ℹ Synchronising files--OUTPUT -
--ℹ Downloading 2 files, totalling 0.17 GB--OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5--OUTPUT -
--Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s--OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds--OUTPUT -
--Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files. -ℹ Compiling Single Cell Experiment.-R -
--+ get_single_cell_experiment() + +single_cell_countssingle_cell_countsOUTPUT @@ -5915,7 +5861,7 @@
Query counts scaled per million
This is helpful if just few genes are of interest, as they can be compared across samples.
-+-R
@@ -5931,42 +5877,6 @@R
OUTPUT
--ℹ Realising metadata.--OUTPUT -
--ℹ Synchronising files--OUTPUT -
--ℹ Downloading 2 files, totalling 0.29 GB--OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5--OUTPUT -
--Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s--OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds--OUTPUT -
--Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files. -ℹ Compiling Single Cell Experiment.-OUTPUT -
class: SingleCellExperiment dim: 36229 1571 metadata(0): @@ -5984,7 +5894,7 @@OUTPUT<
Extract only a subset of genes
-+-R
@@ -5996,38 +5906,9 @@-R tissue == "lung parenchyma" & stringr::str_like(cell_type, "%CD4%") ) |> - get_single_cell_experiment(assays = "cpm", features = "PUM1")
--OUTPUT -
--ℹ Realising metadata.--OUTPUT -
--ℹ Synchronising files--OUTPUT -
--ℹ Downloading 0 files, totalling 0 GB--OUTPUT -
--ℹ Reading files.--OUTPUT -
--ℹ Compiling Single Cell Experiment.-R -
--+ get_single_cell_experiment(assays = "cpm", features = "PUM1") + +single_cell_countssingle_cell_countsOUTPUT @@ -6052,7 +5933,7 @@
Extracting counts as a Seurat obje
If needed, the H5
-SingleCellExperimentcan be converted into a Seurat object. Note that it may take a long time and use a lot of memory depending on how many cells you are requesting.+R
@@ -6081,7 +5962,7 @@Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Arraypackage. -+R
@@ -6111,9 +5992,9 @@Exercise 1
Show me the solution
-+-+R
@@ -6145,9 +6026,9 @@Exercise 2
Show me the solution
-+-+R
@@ -6179,9 +6060,9 @@Exercise 3
Show me the solution
-+-+R
@@ -6221,9 +6102,9 @@Exercise 4
Show me the solution
-++-@@ -5579,11 +5585,7 @@+@@ -5515,7 +5516,12 @@-R
@@ -6241,41 +6122,6 @@R
OUTPUT
--ℹ Realising metadata.--OUTPUT -
--ℹ Synchronising files--OUTPUT -
--ℹ Downloading 2 files, totalling 0 GB--OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5--OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds--OUTPUT -
--ℹ Reading files.--OUTPUT -
--ℹ Compiling Single Cell Experiment.-OUTPUT -
class: SingleCellExperiment dim: 36229 12 metadata(0): @@ -6307,15 +6153,6 @@Key Points -
Acknowledgements -
-Thank you to Stefano -Mangiola and his team for developing CuratedAtlasQueryR -and graciously providing the content from their vignette. Make sure to -keep an eye out for their publication for proper citation. Their bioRxiv -paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.
Content from Accessing data from the Human Cell Atlas (HCA)
-Last updated on 2024-04-09 | +
Last updated on 2024-04-18 | Edit this page
Estimated time: 30 minutes
@@ -5505,7 +5505,8 @@Overview
Questions
-
- TODO
+- How to obtain comprehensive single-cell reference maps from the +Human Cell Atlas?
Questions
Objectives
-
- TODO
+- Learn about different resources for public single-cell RNA-seq +data.
+- Learn how to access data from the Human Cell Atlas using the +CuratedAtlasQueryR package.
+- Learn how to query for cells of interest and how to download them +into a SingleCellExperiment object.
The CuratedAtlasQueryR Project -
ERROR -
--Error in knitr::include_graphics("figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"): Cannot find the file(s): "figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png"Data Sources in R / Bioconductor @@ -5624,20 +5626,20 @@
Data Sources in R / Bioconductor
Installation
-+R
+BiocManager::install("CuratedAtlasQueryR")if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") -BiocManager::install("stemangiola/CuratedAtlasQueryR")Package load
-+R
@@ -5652,25 +5654,15 @@HCA Metadata +
-R
metadata <- get_metadata(remote_url = CuratedAtlasQueryR::SAMPLE_DATABASE_URL)--OUTPUT -
--ℹ Downloading 1 file, totalling 0 GB-OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/sample_metadata.0.2.3.parquet to /home/runner/.cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquetGet a view of the first 10 columns in the metadata with
-glimpse+R
@@ -5683,7 +5675,7 @@OUTPUT<
Rows: ?? Columns: 10 -Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:] +Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:] $ cell_ <chr> "TTATGCTAGGGTGTTG_12", "GCTTGAACATGG… $ sample_ <chr> "039c558ca1c43dc74c563b58fe0d6289", … $ cell_type <chr> "mature NK T cell", "mature NK T cel… @@ -5710,7 +5702,7 @@A note on the piping operator +
R
@@ -5734,7 +5726,7 @@Summarizing the metadata
For each distinct tissue and dataset combination, count the number of datasets by tissue type.
-+R
@@ -5746,26 +5738,26 @@ROUTPUT
# Source: SQL [?? x 2] -# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:] +# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:] tissue n <chr> <dbl> 1 renal medulla 6 2 caecum 1 3 ileum 1 - 4 lymph node 2 - 5 transition zone of prostate 2 - 6 peripheral zone of prostate 2 + 4 transition zone of prostate 2 + 5 peripheral zone of prostate 2 + 6 lymph node 2 7 fovea centralis 1 8 adrenal gland 1 - 9 heart left ventricle 7 -10 bone marrow 4 + 9 blood 17 +10 kidney 8 # ℹ more rowsColumns available in the metadata
-+-R
@@ -5774,19 +5766,13 @@R
OUTPUT
--! The `names()` method of <tbl_lazy> is for internal use only. -ℹ Did you mean `colnames()`?-OUTPUT -
[1] "src" "lazy_query"Available assays
-+R
@@ -5798,26 +5784,26 @@ROUTPUT
# Source: SQL [?? x 2] -# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:] +# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:] assay n <chr> <dbl> - 1 10x 5' v2 2 - 2 sci-RNA-seq 1 - 3 10x 3' v1 1 - 4 Smart-seq2 1 - 5 10x 3' v3 21 - 6 Slide-seq 4 - 7 scRNA-seq 4 - 8 Seq-Well 2 - 9 10x 3' v2 27 -10 10x 5' v1 7 + 1 10x 3' v2 27 + 2 10x 5' v1 7 + 3 10x 3' v3 21 + 4 Slide-seq 4 + 5 10x 5' v2 2 + 6 sci-RNA-seq 1 + 7 10x 3' v1 1 + 8 Smart-seq2 1 + 9 scRNA-seq 4 +10 Seq-Well 2 # ℹ more rowsAvailable organisms
-+R
@@ -5829,7 +5815,7 @@ROUTPUT
@@ -5845,7 +5831,7 @@# Source: SQL [1 x 2] -# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1017-azure:R 4.3.3/:memory:] +# Database: DuckDB v0.10.1 [unknown@Linux 6.5.0-1018-azure:R 4.3.3/:memory:] organism n <chr> <dbl> 1 Homo sapiens 63Download single-cell RNA seq
Query raw counts
-+-R
@@ -5857,49 +5843,9 @@-R tissue == "lung parenchyma" & stringr::str_like(cell_type, "%CD4%") ) |> - get_single_cell_experiment()
--OUTPUT -
--ℹ Realising metadata.--OUTPUT -
--ℹ Synchronising files--OUTPUT -
--ℹ Downloading 2 files, totalling 0.17 GB--OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/assays.h5--OUTPUT -
--Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13s--OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/bc380dae8b14313a870973697842878b/se.rds--OUTPUT -
--Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 13sℹ Reading files. -ℹ Compiling Single Cell Experiment.-R -
--+ get_single_cell_experiment() + +single_cell_countssingle_cell_countsOUTPUT @@ -5923,7 +5869,7 @@
Query counts scaled per million
This is helpful if just few genes are of interest, as they can be compared across samples.
-+-R
@@ -5939,42 +5885,6 @@R
OUTPUT
--ℹ Realising metadata.--OUTPUT -
--ℹ Synchronising files--OUTPUT -
--ℹ Downloading 2 files, totalling 0.29 GB--OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/assays.h5--OUTPUT -
--Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21s--OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/cpm/bc380dae8b14313a870973697842878b/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/cpm/bc380dae8b14313a870973697842878b/se.rds--OUTPUT -
--Downloading files ■■■■■■■■■■■■■■■■ 50% | ETA: 21sℹ Reading files. -ℹ Compiling Single Cell Experiment.-OUTPUT -
class: SingleCellExperiment dim: 36229 1571 metadata(0): @@ -5992,7 +5902,7 @@OUTPUT<
Extract only a subset of genes
-+-R
@@ -6004,38 +5914,9 @@-R tissue == "lung parenchyma" & stringr::str_like(cell_type, "%CD4%") ) |> - get_single_cell_experiment(assays = "cpm", features = "PUM1")
--OUTPUT -
--ℹ Realising metadata.--OUTPUT -
--ℹ Synchronising files--OUTPUT -
--ℹ Downloading 0 files, totalling 0 GB--OUTPUT -
--ℹ Reading files.--OUTPUT -
--ℹ Compiling Single Cell Experiment.-R -
--+ get_single_cell_experiment(assays = "cpm", features = "PUM1") + +single_cell_countssingle_cell_countsOUTPUT @@ -6060,7 +5941,7 @@
Extracting counts as a Seurat obje
If needed, the H5
-SingleCellExperimentcan be converted into a Seurat object. Note that it may take a long time and use a lot of memory depending on how many cells you are requesting.+R
@@ -6089,7 +5970,7 @@Saving as HDF5saveHDF5SummarizedExperiment from the
HDF5Arraypackage. -+R
@@ -6119,9 +6000,9 @@Exercise 1
Show me the solution
-+-+R
@@ -6153,9 +6034,9 @@Exercise 2
Show me the solution
-+-+R
@@ -6187,9 +6068,9 @@Exercise 3
Show me the solution
-+-+R
@@ -6229,9 +6110,9 @@Exercise 4
Show me the solution
-+-+-R
@@ -6249,41 +6130,6 @@R
OUTPUT
--ℹ Realising metadata.--OUTPUT -
--ℹ Synchronising files--OUTPUT -
--ℹ Downloading 2 files, totalling 0 GB--OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/assays.h5 to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/assays.h5--OUTPUT -
--ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5/original/893d8537e318769108b4962020ddd846/se.rds to /home/runner/.cache/R/CuratedAtlasQueryR/0.2.1/original/893d8537e318769108b4962020ddd846/se.rds--OUTPUT -
--ℹ Reading files.--OUTPUT -
--ℹ Compiling Single Cell Experiment.-OUTPUT -
class: SingleCellExperiment dim: 36229 12 metadata(0): @@ -6315,15 +6161,6 @@Key Points -
Acknowledgements -
-Thank you to Stefano -Mangiola and his team for developing CuratedAtlasQueryR -and graciously providing the content from their vignette. Make sure to -keep an eye out for their publication for proper citation. Their bioRxiv -paper can be found at https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.