diff --git a/docs/articles/gDR.html b/docs/articles/gDR.html
index 6e798ef..9413089 100644
--- a/docs/articles/gDR.html
+++ b/docs/articles/gDR.html
@@ -235,18 +235,18 @@
se
#> class: SummarizedExperiment
@@ -299,7 +299,7 @@
mae
#> A MultiAssayExperiment object of 2 listed
diff --git a/docs/index.html b/docs/index.html
index b096104..9068ae2 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -159,7 +159,7 @@
Preparing your machine to run gDR images
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 033521d..58463c3 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -3,8 +3,8 @@ pkgdown: 2.0.7
pkgdown_sha: ~
articles:
gDR: gDR.html
-last_built: 2024-02-14T13:42Z
+last_built: 2024-02-15T12:48Z
urls:
- reference: https://gdrplatform.github.io/gDRutils/reference
- article: https://gdrplatform.github.io/gDRutils/articles
+ reference: https://gdrplatform.github.io/gDR/reference
+ article: https://gdrplatform.github.io/gDR/articles
diff --git a/docs/reference/gDR-package.html b/docs/reference/gDR-package.html
index 61e7025..7023b72 100644
--- a/docs/reference/gDR-package.html
+++ b/docs/reference/gDR-package.html
@@ -1,7 +1,7 @@
-gDR: Umbrella package for R packages in the gDR suite — gDR-package • gDRgDR: Umbrella package for R packages in the gDR suite — gDR-package • gDR
@@ -51,8 +51,8 @@
-
Package is a part of the gDR suite. It provides info about processing functions and utilities from individual gDR packages. The vignette walks through the full processing pipeline for drug response analyses that the gDR suite offers.
-
Package is a part of the gDR suite. It provides info about processing functions and utilities from individual gDR packages. The vignette walks through the full processing pipeline for drug response analyses that the gDR suite offers.
+
Package is a part of the gDR suite. It reexports functions from other packages in the gDR suite that contain critical processing functions and utilities. The vignette walks through the full processing pipeline for drug response analyses that the gDR suite offers.
+
Package is a part of the gDR suite. It reexports functions from other packages in the gDR suite that contain critical processing functions and utilities. The vignette walks through the full processing pipeline for drug response analyses that the gDR suite offers.
@@ -67,7 +67,17 @@
Value
+
+
See also
+
Useful links:
Useful links:
Author
@@ -76,6 +86,7 @@
Author<
Bartosz Czech
Marc Hafner
Dariusz Scigocki
+
Janina Smola
Sergiu Mocanu
diff --git a/docs/reference/import_data.html b/docs/reference/import_data.html
index b74a70b..aab57d6 100644
--- a/docs/reference/import_data.html
+++ b/docs/reference/import_data.html
@@ -95,12 +95,12 @@
Value
Examples
diff --git a/docs/search.json b/docs/search.json
index b5e46ad..93c8716 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":[]},{"path":"https://gdrplatform.github.io/gDRutils/PULL_REQUEST_TEMPLATE.html","id":"what-changed","dir":"","previous_headings":"","what":"What changed?","title":"Description","text":"Related JIRA issue:","code":""},{"path":[]},{"path":"https://gdrplatform.github.io/gDRutils/PULL_REQUEST_TEMPLATE.html","id":"checklist-for-sustainable-code-base","dir":"","previous_headings":"","what":"Checklist for sustainable code base","title":"Description","text":"added tests code changed/added added documentation code changed/added made sure naming new functions self-explanatory consistent","code":""},{"path":"https://gdrplatform.github.io/gDRutils/PULL_REQUEST_TEMPLATE.html","id":"logistic-checklist","dir":"","previous_headings":"","what":"Logistic checklist","title":"Description","text":"Package version bumped Changelog updated","code":""},{"path":[]},{"path":"https://gdrplatform.github.io/gDRutils/articles/gDR.html","id":"intro","dir":"Articles","previous_headings":"","what":"Introduction","title":"gDR suite","text":"decades, many departments across gRED Roche generated large amounts drug response screening data using Genentech’s rich drug compounds inventory. extensive labor time invested generate data, analyzed standardized manner meaningful comparison. one hand, large screens performed across many cell lines drugs semi-automated manner. hand, small-scale studies, focused factors contribute sensitivity resistance certain therapies, generally performed individual scientist limited automation. complementary approaches rarely handled way. Commercial softwares available analyzing large datasets, whereas researchers small-scale datasets often process data ad hoc software like PRISM. , propose suite computational tools enable processing, archiving, visualization drug response data experiment, regardless size experimental design, thus ensuring reproducibility implementation Findable, Accessible, Interoperable, Reusable (F...R.) principles, goal making accessible public community. now share subset gDR suite components pre-processing processing data.","code":""},{"path":"https://gdrplatform.github.io/gDRutils/articles/gDR.html","id":"rpackages","dir":"Articles","previous_headings":"Introduction","what":"R Packages","title":"gDR suite","text":"gDR suite consists packages power app make comprehensive tool. packages gDR umbrella stored gDR platform GitHub organization. happy share packages importing, processing managing gDR data: - gDRimport - gDRcore - gDRutils - gDRtestData","code":""},{"path":"https://gdrplatform.github.io/gDRutils/articles/gDR.html","id":"data-structures","dir":"Articles","previous_headings":"Introduction","what":"Data structures","title":"gDR suite","text":"gDR data model based SummarizedExperiment BumpyMatrix. readers unfamiliar data models, recommend first reading SummarizedExperiment vignettes, followed BumpyMatrix vignettes. SummarizedExperiment data structure enables ease subsetting within SummarizedExperiment object, also provides ease trying correlate drug response data genomic data, data may jointly stored MultiAssayExperiment. BumpyMatrix allows storage multi-dimensional data retaining matrix abstraction. data structure core data structure downstream processing functions well visualization tools operate .","code":""},{"path":"https://gdrplatform.github.io/gDRutils/articles/gDR.html","id":"overview","dir":"Articles","previous_headings":"Introduction","what":"Overview","title":"gDR suite","text":"gDR suite designed modular manner, user can jump “standard” end--end gDR processing pipeline several entry points suitable needs. full pipeline involves: user able enter part pipeline long able create intermediate object (.e., individual manifest, template, raw data files, single, long table, SummarizedExperiment object Bumpy assays).","code":"manifest, template(s), raw data | | 1. Aggregating all raw data and metadata | into a single long table. | V single, long table | | 2. Transforming the long table into | a SummarizedExperiment object with BumpyMatrix assays | by specifying what columns belong on rows, | columns, and nested. | V SummarizedExperiment object with raw and treated assays | | 3. Normalizing, averaging, and fitting data. | V SummarizedExperiment object with raw, treated, normalized, averaged, and metric assays, ready for use by downstream visualization"},{"path":[]},{"path":"https://gdrplatform.github.io/gDRutils/articles/gDR.html","id":"aggregating-raw-data-and-metadata-1","dir":"Articles","previous_headings":"Quick start","what":"Aggregating raw data and metadata (1)","title":"gDR suite","text":"gDR suite ultimately requires single, long merged table containing raw data metadata. support common use case, provide convenience function takes three objects: manifest, template(s), raw data create single, long merged table user. manifest contains metadata experimental design, template files specify drugs cell lines used, raw data output files obtained plate reader scanner. Exemplary data can found : [1] “/usr/local/lib/R/site-library/gDRimport/extdata/data1/manifest.xlsx” [1] “/usr/local/lib/R/site-library/gDRimport/extdata/data1/Template_7daytreated.xlsx” [2] “/usr/local/lib/R/site-library/gDRimport/extdata/data1/Template_Untreated.xlsx” [1] “/usr/local/lib/R/site-library/gDRimport/extdata/data1/RawData_day0.xlsx” [2] “/usr/local/lib/R/site-library/gDRimport/extdata/data1/RawData_day7.xlsx” Using convenience function import_data, long table easily created: function expect certain “identifiers” tell processing functions columns long table map certain expected fields, column interpreted correctly. details regarding identifiers, see “Details” section ?identifiers. Use set_env_identifier set_SE_identifiers set correct mappings expected fields long table column names.","code":"library(gDR) #> Loading required package: gDRcore #> Loading required package: gDRimport #> Loading required package: gDRutils # get test data from gDRimport package # i.e. paths to manifest, templates and results files td <- get_test_data() manifest_path(td) template_path(td) result_path(td) # Import data imported_data <- import_data(manifest_path(td), template_path(td), result_path(td)) head(imported_data)"},{"path":"https://gdrplatform.github.io/gDRutils/articles/gDR.html","id":"transforming-data-into-a-summarizedexperiment-2","dir":"Articles","previous_headings":"Quick start","what":"Transforming data into a SummarizedExperiment (2)","title":"gDR suite","text":"Next, can transform long table initial SummarizedExperiment object. , need tell software: - go rows columns versus nested assay. - rows table consider “control” versus “treated” normalization. - data type converted SE. can setting untreated_tag identifier like set_env_identifier(\"untreated_tag\" = c(\"MY_CONTROL_TERMINOLOGY_HERE\")). specifying nested_keys argument within create_and_normalize_SE specifiying data_type. Note created SummarizedExperiment object rowData, colData, metadata 3 assays.","code":"inl <- prepare_input(imported_data) #> Warning in .set_nested_confounders(nested_confounders = nested_confounders, : 'Plate' nested confounder(s) is/are not present in the data. #> Switching into 'Barcode' nested confounder(s). detected_data_types <- names(inl$exps) detected_data_types #> [1] \"combination\" \"single-agent\" se <- create_and_normalize_SE( inl$df_list[[\"single-agent\"]], data_type = \"single-agent\", nested_confounders = inl$nested_confounders) #> INFO [2024-02-14 13:42:29] #> INFO [2024-02-14 13:42:29] se #> class: SummarizedExperiment #> dim: 3 6 #> metadata(3): identifiers experiment_metadata Keys #> assays(3): RawTreated Controls Normalized #> rownames(3): G00002_drug_002_moa_A_168 G00004_drug_004_moa_A_168 #> G00011_drug_011_moa_B_168 #> rowData names(4): Gnumber DrugName drug_moa Duration #> colnames(6): CL00011_cellline_BA_breast_cellline_BA_unknown_26 #> CL00012_cellline_CA_breast_cellline_CA_unknown_30 ... #> CL00015_cellline_FA_breast_cellline_FA_unknown_42 #> CL00018_cellline_IB_breast_cellline_IB_unknown_54 #> colData names(6): clid CellLineName ... subtype ReferenceDivisionTime"},{"path":"https://gdrplatform.github.io/gDRutils/articles/gDR.html","id":"averaging-and-fitting-data-3","dir":"Articles","previous_headings":"Quick start","what":"Averaging and fitting data (3)","title":"gDR suite","text":"Next, can average fit data interest.","code":"se <- average_SE(se, data_type = \"single-agent\") se <- fit_SE(se, data_type = \"single-agent\") se #> class: SummarizedExperiment #> dim: 3 6 #> metadata(5): identifiers experiment_metadata Keys fit_parameters #> .internal #> assays(5): RawTreated Controls Normalized Averaged Metrics #> rownames(3): G00002_drug_002_moa_A_168 G00004_drug_004_moa_A_168 #> G00011_drug_011_moa_B_168 #> rowData names(4): Gnumber DrugName drug_moa Duration #> colnames(6): CL00011_cellline_BA_breast_cellline_BA_unknown_26 #> CL00012_cellline_CA_breast_cellline_CA_unknown_30 ... #> CL00015_cellline_FA_breast_cellline_FA_unknown_42 #> CL00018_cellline_IB_breast_cellline_IB_unknown_54 #> colData names(6): clid CellLineName ... subtype ReferenceDivisionTime"},{"path":"https://gdrplatform.github.io/gDRutils/articles/gDR.html","id":"rundrugresponseprocessingpipeline","dir":"Articles","previous_headings":"Quick start","what":"runDrugResponseProcessingPipeline","title":"gDR suite","text":"Steps (2) (3) can combined single step convenience function: runDrugResponseProcessingPipeline. Moreover, output MultiAssayExperiment object one experiment per detected data type. Currently four data types supported: ‘single-agent’, ‘cotreatment’, ‘codilution’ ‘matrix’. first three data types processed via ‘single-agent’ model ‘marix’ data processed via ‘combintation’ model. Note final MultiAssayExperiment object can made multiple experiments multiple assays: assay experiment can easily transformed data.table format using convert_se_assay_to_dt function:","code":"# Run gDR pipeline mae <- runDrugResponseProcessingPipeline(imported_data) mae #> A MultiAssayExperiment object of 2 listed #> experiments with user-defined names and respective classes. #> Containing an ExperimentList class object of length 2: #> [1] combination: SummarizedExperiment with 2 rows and 6 columns #> [2] single-agent: SummarizedExperiment with 3 rows and 6 columns #> Functionality: #> experiments() - obtain the ExperimentList instance #> colData() - the primary/phenotype DataFrame #> sampleMap() - the sample coordination DataFrame #> `$`, `[`, `[[` - extract colData columns, subset, or experiment #> *Format() - convert into a long or wide DataFrame #> assays() - convert ExperimentList to a SimpleList of matrices #> exportClass() - save data to flat files names(mae) #> [1] \"combination\" \"single-agent\" SummarizedExperiment::assayNames(mae[[1]]) #> [1] \"RawTreated\" \"Controls\" \"Normalized\" \"Averaged\" #> [5] \"excess\" \"all_iso_points\" \"isobolograms\" \"scores\" #> [9] \"Metrics\" library(kableExtra) se <- mae[[\"single-agent\"]] head(convert_se_assay_to_dt(se, \"Metrics\")) #> rId cId #>