Package: countland 0.1.2

countland: Analysis of Biological Count Data, Especially from Single-Cell RNA-Seq

A set of functions for applying a restricted linear algebra to the analysis of count-based data. See the accompanying preprint manuscript: "Normalizing need not be the norm: count-based math for analyzing single-cell data" Church et al (2022) <doi:10.1101/2022.06.01.494334> This tool is specifically designed to analyze count matrices from single cell RNA sequencing assays. The tools implement several count-based approaches for standard steps in single-cell RNA-seq analysis, including scoring genes and cells, comparing cells and clustering, calculating differential gene expression, and several methods for rank reduction. There are many opportunities for further optimization that may prove useful in the analysis of other data. We provide the source code freely available at <https://github.com/shchurch/countland> and encourage users and developers to fork the code for their own purposes.

Authors:Church Samuel H. [aut, cre]

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NEWS

# Install 'countland' in R:
install.packages('countland', repos = c('https://shchurch.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/shchurch/countland/issues

On CRAN:

1.32 score 21 scripts 168 downloads 20 exports 28 dependencies

Last updated 10 months agofrom:348e6886f6. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 29 2024
R-4.5-winOKOct 29 2024
R-4.5-linuxOKOct 29 2024
R-4.4-winOKOct 29 2024
R-4.4-macOKOct 29 2024
R-4.3-winOKOct 29 2024
R-4.3-macOKOct 29 2024

Exports:ClustercountlandDotEmbedPlotEigengapPlotEmbeddingPlotGeneCountsPlotIMAPlotIMAElbowPlotMarkerPlotSharedCountsRankMarkerGenesRestoreCountsRunIMAScoreCellsScoreGenesSharedCountsSubsampleSubsetCellsSubsetGenes

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Recapitulate Seurat centering scaled and transformed dataCenter
Perform spectral clustering on dot products.Cluster
Internal function for calculating count index.CountIndex
Initialize a countland object from a dgCMatrixcountland
An S4 class to represent a countland objectcountland-class
Calculate pairwise dot products of counts between all cells.Dot
Perform spectral embedding on dot products.Embed
run integer matrix approximationIMA
rescale if max val is above upper boundIMA_Compute_Init_Scaled
function to initialize U, V, and LambdaIMA_init
Parameter class for IMAIMA_params
Update factor matrix - see SUSTain codeIMA_Update_Factor
Split dgCMatrix into column vectors.listCols
Recapitulate Seurat log transformationLog
Recapitulate Seurat normalizationNormalize
Plots eigenvalues to investigate the optimal number of clustersPlotEigengap
Plot cells using spectral embedding of dot products.PlotEmbedding
Generate a strip plot for counts across selected genesPlotGeneCounts
Plot cells using integer matrix approximationPlotIMA
Plot the difference between the observed and reconstructed count matrix using integer matrix approximation and a series of total features.PlotIMAElbow
Plot cell using spectral embedding and display counts in a given gene.PlotMarker
Plot cells using matrix of counts summed by clusters of genes.PlotSharedCounts
Restore count matrix to original statePrintGeneNumber
Rank the top marker genes for each cluster from spectral clustering.RankMarkerGenes
Internal function to remove empty columns and rowsRemoveEmpty
Recapitulate Seurat scaling to unit varianceRescaleVariance
Restore count matrix to original stateRestoreCounts
Perform integer matrix approximation on count matrix.RunIMA
Recapitulate scikit.manifold.spectral_embedding from python.ScikitManifoldSpectralEmbedding
Calculate several scores for counts across cellsScoreCells
Calculate several scores for count-based gene expression.ScoreGenes
Combine groups of genes with similar counts by clustering and summing.SharedCounts
Subsample cells to a standard number of counts by randomly sampling observations without replacement.Subsample
Internal function for subsampling a column from a sparse matrix.SubsampleCol
Subsets cells using a vector of cell indicesSubsetCells
Subsets genes using a vector of gene indicesSubsetGenes