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”. 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.
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.
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BiocManager::install("concordexR")
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#> 'BiocNeighbors', 'Biostrings', 'bit', 'bit64', 'bookdown', 'class',
#> 'classInt', 'clue', 'cluster', 'cpp11', 'data.table', 'DelayedMatrixStats',
#> 'edgeR', 'evaluate', 'fitdistrplus', 'fontawesome', 'foreign',
#> 'GenomeInfoDb', 'geometry', 'ggmapinset', 'graph', 'graphlayouts', 'igraph',
#> 'IRanges', 'KernSmooth', 'knitr', 'later', 'limma', 'lme4', 'lpSolve',
#> 'lubridate', 'maps', 'MASS', 'MatrixGenerics', 'matrixStats', 'nnet',
#> 'parallelly', 'pillar', 'pkgbuild', 'processx', 'progressr', 'promises',
#> 'quantreg', 'Rcpp', 'RcppArmadillo', 'Rdpack', 'reticulate', 'Rfast',
#> 'rhdf5', 'rpart', 'RSQLite', 'Seurat', 'SFEData', 'shiny',
#> 'SingleCellExperiment', 'spatial', 'SpatialFeatureExperiment', 'spatialLIBD',
#> 'spatstat.data', 'spatstat.explore', 'spatstat.geom', 'spatstat.utils',
#> 'spData', 'spdep', 'stringdist', 'survival', 'systemfonts', 'terra',
#> 'testthat', 'textshaping', 'tidyterra', 'waldo', 'xfun', 'XML'This is a basic example using concordex:
library(SFEData)
library(Voyager)
#> Loading required package: SpatialFeatureExperiment
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#> scale
library(scran)
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#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
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#> 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
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#> Loading required package: scuttle
library(bluster)
library(concordexR)sfe <- McKellarMuscleData("small")
#> see ?SFEData and browseVignettes('SFEData') for documentation
#> loading from cache
clusters <- quickCluster(sfe, min.size=2, d=15)nbc <- calculateConcordex(
sfe,
clusters,
n_neighbors=10,
BLUSPARAM=KmeansParam(2)
)
colData(sfe)[["shr"]] <- attr(nbc, "shrs")plotSpatialFeature(sfe, features="shr")
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} }