Package: cytosignal 0.2.0

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_0.2.0.tar.gz
cytosignal_0.2.0.zip(r-4.5)cytosignal_0.2.0.zip(r-4.4)cytosignal_0.2.0.zip(r-4.3)
cytosignal_0.2.0.tgz(r-4.4-arm64)cytosignal_0.2.0.tgz(r-4.4-x86_64)cytosignal_0.2.0.tgz(r-4.3-arm64)cytosignal_0.2.0.tgz(r-4.3-x86_64)
cytosignal_0.2.0.tar.gz(r-4.5-noble)cytosignal_0.2.0.tar.gz(r-4.4-noble)
cytosignal_0.2.0.tgz(r-4.4-emscripten)cytosignal_0.2.0.tgz(r-4.3-emscripten)
cytosignal.pdf |cytosignal.html
cytosignal/json (API)

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

Peer review:

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:

48 exports 12 stars 2.22 score 43 dependencies

Last updated 2 months agofrom:bc2573dfe7

Exports:addIntrDBaddVelochangeUniprotcreateCytoSignalfindNNfindNNDTfindNNGauEBfindNNRawgraphNicheLRheatmap_GOhex_binhex_coordhex_posimputeLRimputeNicheimputeNicheVeloimputeVeloLRinferCorrScoreinferEpsParamsinferIntrDEGinferIntrScoreinferIntrVeloinferNullScoreLRinferScoreLRinferSignifinferVeloLRpermuteLRplotClusterplotEdgeplotIntrValueplotREVIGOplotSigClusterplotSignifplotSignif2plotVelopurgeBeforeSaverankIntrSpatialVarremoveLowQualityrevigorunPears.stdshowshowImpshowIntrshowLogshowScoreshowUnptshowVelosuggestScaleFactor

Dependencies:clicodetoolscolorspacedbscanfansifarverforeachggplot2ggrepelglmnetgluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RANNRColorBrewerRcppRcppArmadilloRcppEigenRcppProgressrlangRTrianglescalesshapesurvivaltibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
#' 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
Add interaction database to CytoSignal objectaddIntrDB
Add velocity data to CytoSignal objectaddVelo
Subset gene expression matrix according to availability in the UNIPROT database.changeUniprot changeUniprot.CytoSignal changeUniprot.matrix_like
Create a CytoSignal objectcreateCytoSignal
The CytoSignal ClassCytoSignal CytoSignal-class
Interaction database derived from CellphoneDB V2db.cont db.diff g_to_u inter.index
Identify nearest neighbors for each location using different strategies for different types of interactionsfindNN
Find the direct connected neighbor of each cell, using Delaunay triangulationfindNNDT findNNDT.CytoSignal findNNDT.matrix
Find the neighbors of each cell in the Epsilon BallfindNNGauEB findNNGauEB.CytoSignal findNNGauEB.matrix
Create a ImpData object using raw data without imputationfindNNRaw
Format a CytosignalIntrDEG object to stringformat.CytosignalIntrDEG
Compute the LR score for specific ligand-receptor imputation obj pairsgraphNicheLR
Sub function for graphNicheLR, input a CytoSignal objectgraphNicheLR.CytoSignal
Sub function for graphNicheLR, input a sparse matrixgraphNicheLR.dgCMatrix
Show significant genes across top GO term hits with coefficients from regression analysis of an interactionheatmap_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 ClassImpData ImpData-class
Impute the L or R value from the nearest neighbors of each locationimputeLR
Impute the dataimputeNiche imputeNiche.CytoSignal imputeNiche.dgCMatrix
Impute the velocity mtx using the specified methodimputeNicheVelo
Sub function for imputeNicheVelo, input a Cytosignal objectimputeNicheVelo.CytoSignal
Impute time derivative of L or R from the nearest neighbors of each locationimputeVeloLR
Infer the correspondence between LR-scores and SignificanceinferCorrScore
Infer the parameters of the Gaussian kernelinferEpsParams
Infer significant genes for each interactioninferIntrDEG
Calculate LRScore from the imputed L and R valuesinferIntrScore
Calculate the interaction velocity from the imputed time derivative of L or R valuesinferIntrVelo
Permute LR score for specific ligand-receptor imputation obj pairsinferNullScoreLR
Compute the LR score for specific ligand-receptor imputation pairsinferScoreLR
Infer significance of LR scoresinferSignif
Infer the correspondence between LR-scores and SignificanceinferSpatialCorr
Sub function for inferVeloLR, input a CytoSignal objectinferVeloLR
The lrScores ClasslrScores lrScores-class
The lrvelo ClasslrVelo lrVelo-class
Permute Imputation Results of specific imputation methodpermuteLR
Plotting edge for a given interaction from a CytoSignal objectplotEdge
Plot the refined score of each interaction after regression model refinementplotRefinedScore
Plot edges by each cluster as sender or receiver cellsplotSigCluster
Plot significant interactions ranked by the user-specified metricplotSignif
Plot significant interactions ranked by the user-specified metricplotSignif2
Plot 3D LR-velo ranked by the user-specified metricplotVelo
Print the CytosignalIntrDEG object representation to screenprint.CytosignalIntrDEG
Remove imputed data and normalized imputed data from CytoSignal object to save disk spacepurgeBeforeSave
Rank the inferred high-quality interactions by their spatial variabilityrankIntrSpatialVar
Remove low quality cells and genes from raw countsremoveLowQuality
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 CytoSignalshow,CytoSignal-method
show method for cytosignal objshow,ImpData-method
show method for ImpDatashowImp
show method for CytoSignalshowIntr
show all current logsshowLog
show method for lrScoresshowScore
show intr.data in ImpDatashowUnpt
show method for lrVeloshowVelo
Suggest scaling factor of real units to spatial unitssuggestScaleFactor
Generate character vector of unique color hex codes Base color palette adopts 'ggsci:::ggsci_db$igv$default'uniqueColors