Update README to include citation, proper plots, and maintainer info

README.Rmd

@@ -20,18 +20,36 @@ knitr::opts_chunk$set(
 <!-- badges: end -->
 
 
-
-The goal of concordexR is to replace UMAP as a clustering diagnostic.
+The goal of concordexR is to identify spatial homogeneous regions (SHRs) as 
+defined in the recent manuscript, ["Identification of spatial homogenous regions in tissues with concordex"](https://doi.org/10.1101/2023.06.28.546949). Briefly, SHRs are are 
+domains that are homogeneous with respect to cell type composition. `concordex` 
+relies on the the k-nearest-neighbor (kNN) graph to representing similarities 
+between cells and uses common clustering algorithms to identify SHRs.
 
 ## Installation
 
-You can install the development version of concordexR from [GitHub](https://github.com/) with:
+(Recommended) You can install the development version of `concordexR` from
+[GitHub](https://github.com/) with:
 
-``` r
+```{r}
 # install.packages("devtools")
 devtools::install_github("pachterlab/concordexR")
 ```
 
+Versions of the `concordexR` package that do not enable clustering spatial data
+into spatial homogeneous regions (SHRs) are available for Bioconductor versions
+3.17-19. The most recent version of the package is slated to be released on 
+Bioconductor version 3.20. You can install the Bioconductor development version 
+with: 
+
+```{r}
+if (!requireNamespace("BiocManager", quietly = TRUE))
+    install.packages("BiocManager")
+    
+BiocManager::install(version="devel")
+BiocManager::install("concordexR")
+```
+
 ## Example
 
 This is a basic example using concordex:
@@ -59,10 +77,29 @@ nbc <- calculateConcordex(
 )
 
 colData(sfe)[["shr"]] <- attr(nbc, "shrs")
-
 ```
 
-```{r plot}
-plotSpatialFeature(sfe,features="shr")
+```{r plot-shr}
+plotSpatialFeature(sfe, features="shr")
 ```
 
+## Citation
+
+If you'd like to use the `concordexR` package in your research, please cite our
+recent bioRxiv preprint
+
+>  Jackson, K.; Booeshaghi, A. S.; Gálvez-Merchán, Á.; Moses, L.; Chari, T.; Pachter, L. Quantitative assessment of single-cell RNA-seq clustering with CONCORDEX. bioRxiv (Cold Spring Harbor Laboratory) 2023. https://doi.org/10.1101/2023.06.28.546949.
+
+   @article {Jackson2023.06.28.546949,
+	author = {Jackson, Kayla C. and Booeshaghi, A. Sina and G{\'a}lvez-Merch{\'a}n, {\'A}ngel and Moses, Lambda and Chari, Tara and Kim, Alexandra and Pachter, Lior},
+	title = {Identification of spatial homogeneous regions in tissues with concordex},
+	year = {2024},
+	doi = {10.1101/2023.06.28.546949},
+	publisher = {Cold Spring Harbor Laboratory},
+	URL = {https://www.biorxiv.org/content/early/2024/07/18/2023.06.28.546949},
+	journal = {bioRxiv}
+}
+
+## Maintainer
+
+[Kayla Jackson](https://github.com/kayla-jackson)

README.md

@@ -8,38 +8,169 @@
 [![codecov](https://codecov.io/gh/pachterlab/concordexR/branch/main/graph/badge.svg?token=FSASJPR4T5)](https://codecov.io/gh/pachterlab/concordexR)
 <!-- badges: end -->
 
-The goal of concordexR is to identify spatial homogeneous regions (SHRs) as defined in the recent manuscrpt["Identification of spatial homogenous regions in tissues with concordex"](https://doi.org/10.1101/2023.06.28.546949). Briefly, SHRs are are domains that are homogeneous with respect to cell type composition. concordex relies on the the k-nearest-neighbor (kNN) graph to representing similarities between cells and uses common clustering algorithms to identify SHRs.
+The goal of concordexR is to identify spatial homogeneous regions (SHRs)
+as defined in the recent manuscript, [“Identification of spatial
+homogenous regions in tissues with
+concordex”](https://doi.org/10.1101/2023.06.28.546949). Briefly, SHRs
+are are domains that are homogeneous with respect to cell type
+composition. `concordex` relies on the the k-nearest-neighbor (kNN)
+graph to representing similarities between cells and uses common
+clustering algorithms to identify SHRs.
 
 ## Installation
 
-You can install the development version of concordexR from
-[GitHub](https://github.com/) with:
+(Recommended) You can install the development version of `concordexR`
+from [GitHub](https://github.com/) with:
 
 ``` r
 # install.packages("devtools")
 devtools::install_github("pachterlab/concordexR")
+#> Using GitHub PAT from the git credential store.
+#> Skipping install of 'concordexR' from a github remote, the SHA1 (046127e9) has not changed since last install.
+#>   Use `force = TRUE` to force installation
+```
+
+Versions of the `concordexR` package that do not enable clustering
+spatial data into spatial homogeneous regions (SHRs) are available for
+Bioconductor versions 3.17-19. The most recent version of the package is
+slated to be released on Bioconductor version 3.20. You can install the
+Bioconductor development version with:
+
+``` r
+if (!requireNamespace("BiocManager", quietly = TRUE))
+    install.packages("BiocManager")
+
+BiocManager::install(version="devel")
+BiocManager::install("concordexR")
 ```
 
 ## Example
 
 This is a basic example using concordex:
 
 ``` r
+library(SFEData)
+library(Voyager)
+#> Loading required package: SpatialFeatureExperiment
+#> 
+#> Attaching package: 'SpatialFeatureExperiment'
+#> The following object is masked from 'package:base':
+#> 
+#>     scale
+library(scran)
+#> Loading required package: SingleCellExperiment
+#> Loading required package: SummarizedExperiment
+#> Loading required package: MatrixGenerics
+#> Loading required package: matrixStats
+#> 
+#> Attaching package: 'MatrixGenerics'
+#> The following objects are masked from 'package:matrixStats':
+#> 
+#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
+#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
+#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
+#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
+#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
+#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
+#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
+#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
+#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
+#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
+#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
+#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
+#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
+#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
+#>     rowWeightedSds, rowWeightedVars
+#> Loading required package: GenomicRanges
+#> Loading required package: stats4
+#> Loading required package: BiocGenerics
+#> 
+#> Attaching package: 'BiocGenerics'
+#> The following objects are masked from 'package:stats':
+#> 
+#>     IQR, mad, sd, var, xtabs
+#> The following objects are masked from 'package:base':
+#> 
+#>     anyDuplicated, aperm, append, as.data.frame, basename, cbind,
+#>     colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
+#>     get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
+#>     match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
+#>     Position, rank, rbind, Reduce, rownames, sapply, setdiff, table,
+#>     tapply, union, unique, unsplit, which.max, which.min
+#> Loading required package: S4Vectors
+#> 
+#> Attaching package: 'S4Vectors'
+#> The following object is masked from 'package:utils':
+#> 
+#>     findMatches
+#> The following objects are masked from 'package:base':
+#> 
+#>     expand.grid, I, unname
+#> Loading required package: IRanges
+#> Loading required package: GenomeInfoDb
+#> Loading required package: Biobase
+#> Welcome to Bioconductor
+#> 
+#>     Vignettes contain introductory material; view with
+#>     'browseVignettes()'. To cite Bioconductor, see
+#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
+#> 
+#> Attaching package: 'Biobase'
+#> The following object is masked from 'package:MatrixGenerics':
+#> 
+#>     rowMedians
+#> The following objects are masked from 'package:matrixStats':
+#> 
+#>     anyMissing, rowMedians
+#> Loading required package: scuttle
+library(bluster)
+
 library(concordexR)
 ```
 
 ``` r
-res <- calculateConcordex(g$index, labels = iris$Species, k = 10, return.map = TRUE)
+sfe <- McKellarMuscleData("small")
+#> see ?SFEData and browseVignettes('SFEData') for documentation
+#> loading from cache
+clusters <- quickCluster(sfe, min.size=2, d=15)
 ```
 
 ``` r
-plotConcordexSim(res)
+nbc <- calculateConcordex(
+    sfe,
+    clusters,
+    n_neighbors=10,
+    BLUSPARAM=KmeansParam(2)
+)
+
+colData(sfe)[["shr"]] <- attr(nbc, "shrs")
 ```
 
-<img src="man/figures/README-unnamed-chunk-3-1.png" width="70%" />
-
 ``` r
-heatConcordex(res)
+plotSpatialFeature(sfe, features="shr")
 ```
 
-<img src="man/figures/README-unnamed-chunk-4-1.png" width="70%" />
+<img src="man/figures/README-plot-shr-1.png" width="70%" />
+
+## Citation
+
+If you’d like to use the `concordexR` package in your research, please
+cite our recent bioRxiv preprint
+
+> Jackson, K.; Booeshaghi, A. S.; Gálvez-Merchán, Á.; Moses, L.; Chari,
+> T.; Pachter, L. Quantitative assessment of single-cell RNA-seq
+> clustering with CONCORDEX. bioRxiv (Cold Spring Harbor Laboratory)
+> 2023. <https://doi.org/10.1101/2023.06.28.546949>.
+
+@article {Jackson2023.06.28.546949, author = {Jackson, Kayla C. and
+Booeshaghi, A. Sina and G{'a}lvez-Merch{'a}n, {'A}ngel and Moses, Lambda
+and Chari, Tara and Kim, Alexandra and Pachter, Lior}, title =
+{Identification of spatial homogeneous regions in tissues with
+concordex}, year = {2024}, doi = {10.1101/2023.06.28.546949}, publisher
+= {Cold Spring Harbor Laboratory}, URL =
+{<https://www.biorxiv.org/content/early/2024/07/18/2023.06.28.546949>},
+journal = {bioRxiv} }
+
+## Maintainer
+
+[Kayla Jackson](https://github.com/kayla-jackson)