Package: cytosignal 0.5.0

Jialin Liu

cytosignal: What the Package Does (One Line, Title Case)

What the package does (one paragraph).

Authors:Jialin Liu [aut, cre], Yichen Wang [aut]

cytosignal_0.5.0.tar.gz
cytosignal_0.5.0.zip(r-4.5)
cytosignal_0.5.0.tgz(r-4.6-x86_64)cytosignal_0.5.0.tgz(r-4.6-arm64)cytosignal_0.5.0.tgz(r-4.5-x86_64)cytosignal_0.5.0.tgz(r-4.5-arm64)

cytosignal_0.5.0.tgz(r-4.5-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
cytosignal/json (API)

# Install 'cytosignal' in R:

install.packages('cytosignal', repos = c('https://welch-lab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/welch-lab/cytosignal/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

db.cont - Interaction database derived from CellphoneDB V2
db.diff - Interaction database derived from CellphoneDB V2
g_to_u - Interaction database derived from CellphoneDB V2
inter.index - Interaction database derived from CellphoneDB V2

On CRAN:

openblas cpp

4.72 score 25 stars 14 scripts 61 exports 35 dependencies

Last updated from:cf2a804454. Checks:4 FAIL, 1 ERROR, 7 WARNING, 1 OK. Indexed: yes.

The latest version of this package failed to build. Look at thebuild logs for more information.

Target	Result	Time
linux-devel-arm64	FAIL	186
linux-devel-x86_64	FAIL	189
source / vignettes	ERROR	533
linux-release-arm64	WARNING	287
linux-release-x86_64	WARNING	249
macos-release-arm64	WARNING	111
macos-release-x86_64	WARNING	378
macos-oldrel-arm64	WARNING	124
macos-oldrel-x86_64	WARNING	388
windows-devel	FAIL	281
windows-release	FAIL	173
windows-oldrel	WARNING	204
wasm-release	OK	243

Exports:addIntrDB addVelo changeUniprot createCytoSignal csToH5AD csToSCE csToSeurat findNN findNNDT findNNGauEB findNNRaw formatLRDB graphNicheLR H5ADToCS heatmap_GO hex_bin hex_coord hex_pos imputeLR imputeNiche imputeNicheVelo imputeVeloLR inferCorrScore inferEpsParams inferIntrDEG inferIntrScore inferIntrVelo inferNullScoreLR inferScoreLR inferSignif inferVeloLR mergeCytoSignal permuteLR plotCircosNIntr plotCluster plotEdge plotIntrValue plotNebulaAll plotNebulaVolcano plotREVIGO plotSigCluster plotSignif plotVelo purgeBeforeSave rankIntrSpatialVar removeLowQuality revigo runNEBULA runPears.std SCEToCS SeuratToCS show showCov showImp showIntr showLog showScore showUnpt showVelo smoothScoreLR suggestScaleFactor

Dependencies:cli codetools cpp11 dbscan farver foreach generics ggplot2 ggrepel glmnet glue gtable isoband iterators labeling lattice lifecycle Matrix plyr R6 RANN RColorBrewer Rcpp RcppArmadillo RcppEigen RcppProgress rlang RTriangle S7 scales shape survival vctrs viridisLite withr

Vignettes and/or manual for this package failed to build, some documents may be unavailable. Please inspect thebuild logs for more information.

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
#' Compute the LR velo for specific ligand-receptor imputation obj pairs #' #' @param object A Cytosignal object #' @param lig.slot The ligand slot to use #' @param recep.slot The receptor slot to use #' @param intr.db.name The intr database name to use #' @param nn.use The neighbor index as niche #' #' @return A Cytosignal object #' @export #' inferVeloLR <- function( object, ... ) UseMethod(generic = 'inferVeloLR', object = object)	.inferVeloLR.matrix_like
Extract metadata variable from mergedCytoSignal object	$.mergedCytoSignal $<-.mergedCytoSignal
Add interaction database to CytoSignal object	addIntrDB
Add velocity data to CytoSignal object	addVelo
Subset gene expression matrix according to availability in the UNIPROT database.	changeUniprot changeUniprot.CytoSignal changeUniprot.matrix_like
Create a CytoSignal object	createCytoSignal
Conversion between CytoSignal object and AnnData object in H5AD file	csToH5AD H5ADToCS
Conversion between CytoSignal object and SingleCellExperiment object	csToSCE SCEToCS
Conversion between CytoSignal object and Seurat object	csToSeurat SeuratToCS
The CytoSignal Class	CytoSignal CytoSignal-class
Interaction database derived from CellphoneDB V2	db.cont db.diff g_to_u inter.index
dim Method for mergedCytoSignal object	dim.mergedCytoSignal
Identify nearest neighbors for each location using different strategies for different types of interactions	findNN
Find the direct connected neighbor of each cell, using Delaunay triangulation	findNNDT
Find the neighbors of each cell in the Epsilon Ball	findNNGauEB
Create a ImpData object using raw data without imputation	findNNRaw
Format a CytosignalIntrDEG object to string	format.CytosignalIntrDEG
Process Ligand–Receptor Data Without Assigning New Random CPI-CC IDs	formatLRDB
Compute the LR score for specific ligand-receptor imputation obj pairs	graphNicheLR
Sub function for graphNicheLR, input a CytoSignal object	graphNicheLR.CytoSignal
Sub function for graphNicheLR, input a sparse matrix	graphNicheLR.dgCMatrix
Show significant genes across top GO term hits with coefficients from regression analysis of an interaction	heatmap_GO
GGPLOT2 FUNCTIONALITY FOR MAPPING TO HEXAGON SIZE AND COLOUR AESTHETICS by Robin Edwards, 2013 (geotheory.co.uk, @geotheory) This has been adapted from the ggplot bin_hex.R script that underpins geom_hex, etc (see https://github.com/hadley/densityvis/blob/master/R/bin-hex.r).	hex_bin
The ImpData Class	ImpData ImpData-class
Impute the L or R value from the nearest neighbors of each location	imputeLR
Impute the data	imputeNiche
Impute the velocity mtx using the specified method	imputeNicheVelo
Sub function for imputeNicheVelo, input a Cytosignal object	imputeNicheVelo.CytoSignal
Impute time derivative of L or R from the nearest neighbors of each location	imputeVeloLR
Infer the correspondence between LR-scores and Significance	inferCorrScore
Infer the parameters of the Gaussian kernel	inferEpsParams
Infer significant genes (or TFs) for each interaction	inferIntrDEG
Calculate LRScore from the imputed L and R values	inferIntrScore
Calculate the interaction velocity from the imputed time derivative of L or R values	inferIntrVelo
Permute LR score for specific ligand-receptor imputation obj pairs	inferNullScoreLR
Compute the LR score for specific ligand-receptor imputation pairs	inferScoreLR
Infer significance of LR scores	inferSignif
Infer the correspondence between LR-scores and Significance	inferSpatialCorr
Sub function for inferVeloLR, input a CytoSignal object	inferVeloLR
The lrScores Class	lrScores lrScores-class
The lrvelo Class	lrVelo lrVelo-class
Merge multiple CytoSignal objects for cross-dataset analysis	mergeCytoSignal
mergedCytoSignal Class for cross-dataset analysis of spatial cell-cell communication	mergedCytoSignal-class
Dataset names of a mergedCytoSignal object	names.mergedCytoSignal names<-.mergedCytoSignal
Permute Imputation Results of specific imputation method	permuteLR
Make Circos plot summarising number of interactions between clusters	plotCircosNIntr
Plotting edge for a given interaction from a CytoSignal object	plotEdge
Visualize NEBULA analysis result with dot plot that involves all covariates	plotNebulaAll
Make volcano plot for NEBULA analysis result	plotNebulaVolcano
Plot the refined score of each interaction after regression model refinement	plotRefinedScore
Create scatter plot from REVIGO result	plotREVIGO
Plot edges by each cluster as sender or receiver cells	plotSigCluster
Plot significant interactions ranked by the user-specified metric	plotSignif
Plot 3D LR-velo ranked by the user-specified metric	plotVelo
Print the CytosignalIntrDEG object representation to screen	print.CytosignalIntrDEG
Remove imputed data and normalized imputed data from CytoSignal object to save disk space	purgeBeforeSave
Rank the inferred high-quality interactions by their spatial variability	rankIntrSpatialVar
Remove low quality cells and genes from raw counts	removeLowQuality
Submit query to REVIGO and get result to R	revigo
Run NEBULA's association analysis to compare datasets within mergedCytoSignal object	runNEBULA
Identify spatially significant interactions using std-corrected pearson correlation Normal Moran's I test is not applicable here since the total number of the cell is too large, causing unnacceptable computation cost. Here we use a modified version of Moran's I test, which is to take only the top KNNs to compute the Moran's I test.	runPears.std
show method for CytoSignal	show,CytoSignal-method
show method for cytosignal obj	show,ImpData-method
show method for lrScores object	show,lrScores-method
Show method for mergedCytoSignal object	show,mergedCytoSignal-method
Show available covariate levels used in NEBULA analysis	showCov
show method for ImpData	showImp
show method for CytoSignal	showIntr showIntr.CytoSignal showIntr.mergedCytoSignal
show all current logs	showLog
show method for lrScores	showScore
show intr.data in ImpData	showUnpt
show method for lrVelo	showVelo
Smooth existing LR-scores using spatial nearest neighbors	smoothScoreLR
Subset a mergedCytoSignal obejct	subset.mergedCytoSignal
Suggest scaling factor of real units to spatial units	suggestScaleFactor
Generate character vector of unique color hex codes Base color palette adopts 'ggsci:::ggsci_db$igv$default'	uniqueColors