Package: InterCellar 2.11.0

Marta Interlandi

InterCellar: InterCellar: an R-Shiny app for interactive analysis and exploration of cell-cell communication in single-cell transcriptomics

InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell communication in one or multiple conditions.

Authors:Marta Interlandi [cre, aut]

InterCellar_2.11.0.tar.gz
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InterCellar.pdf |InterCellar.html✨
InterCellar/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/martaint/intercellar/issues

Datasets:

On BioConductor:InterCellar-2.11.0(bioc 3.20)InterCellar-2.10.0(bioc 3.19)

bioconductor-package

5 exports 0.49 score 188 dependencies

Last updated 2 months agofrom:79aebf6ecd

Exports:checkLL_RRgetClusterNamesgetGeneTablegetIntFlowrun_app

Dependencies:abindAnnotationDbiaskpassattemptbackportsbase64encBiobaseBiocFileCacheBiocGenericsbiomaRtBiostringsbitbit64blobbootbroombslibcachemcarcarDatacellrangercirclizecliclueclustercodetoolscolorspacecolourpickercommonmarkComplexHeatmapconfigcorrplotcowplotcpp11crayoncrosstalkcurldata.tableDBIdbplyrdendextendDerivdigestdoBydoParalleldplyrDTellipseemmeansestimabilityevaluatefactoextraFactoMineRfansifarverfastmapfilelockflashClustfmsbfontawesomeforeachfsgenericsGenomeInfoDbGenomeInfoDbDataGetoptLongggplot2ggpubrggrepelggsciggsignifGlobalOptionsgluegolemgridExtragtableherehighrhmshtmltoolshtmlwidgetshttpuvhttrhttr2igraphIRangesisobanditeratorsjquerylibjsonliteKEGGRESTknitrlabelinglaterlatticelazyevalleapslifecyclelme4magrittrMASSMatrixMatrixModelsmatrixStatsmemoisemgcvmicrobenchmarkmimeminiUIminqamodelrmultcompViewmunsellmvtnormnlmenloptrnnetnumDerivopensslpbkrtestpillarpkgconfigplogrplotlyplyrpngpolynomprettyunitsprogresspromisespurrrquantregR6rappdirsRColorBrewerRcppRcppEigenRcppTOMLreadxlrematchreshape2reticulaterjsonrlangrmarkdownrprojrootRSpectraRSQLiterstatixS4Vectorssassscalesscatterplot3dshapeshinyshinyalertshinycssloadersshinydashboardshinyFeedbackshinyFilesshinyjssignalsourcetoolsSparseMstringistringrsurvivalsystibbletidyrtidyselecttinytexUCSC.utilsumaputf8uuidvctrsviridisviridisLitevisNetworkwithrwordcloud2xfunxml2xtableXVectoryamlzlibbioc

InterCellar User Guide

Marta Interlandi

Rendered fromuser_guide.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2021-10-19
Started: 2021-02-07

Readme and manuals

Help Manual

Help pageTopics
Perform GO annotation of input dataannotateGO
Annotate pathways for input dataannotatePathways
Build binary matrix with int-pairs in rows, functions in colsbuildPairsbyFunctionMatrix
Manually change the annotation of L-L and R-R pairscheckLL_RR
Plot circle plotcirclePlot
Combine GO annotation and pathways in a unique objectcombineAnnotations
Create Barplot cluster-versecreateBarPlot_CV
Create ggplot barplot to be saved in tiffcreateBarPlot1_ggplot
Create barplot of number of interaction for selected clustercreateBarPlot2_CV
Create ggplot barplot of Nint per cluster selectedcreateBarPlot2_ggplot
Create Network of clusterscreateNetwork
Get dendrogram of int pair modulesdendroIntPairModules
Determine the elbow point on a curve (from package akmedoids)elbowPoint
Get html link to ensemblensemblLink
Get back-to-back barplot for 2 conditions comparisongetBack2BackBarplot
Get dataframe for plotting barplot (all clusters)getBarplotDF
Get dataframe for barplot (by cluster)getBarplotDF2
Get cluster names only from sender cluster AgetClusterA_Names
Get colors for clustersgetClusterColors
Get clusters names from initial input datagetClusterNames
Creating edges dataframe for network of clustersgetClusterNetwork
Get Clusters sizegetClusterSize
Get table of unique int-pairs/clust-pairs coupletsgetDistinctCouplets
Functions to plot DotPlotsgetDotPlot_selInt
Get table for gene-versegetGeneTable
Connection to Ensembl via biomaRt to get GO termsgetGObiomaRt
Subfunction to calculate significant functions by permutation testgetHitsf
Get subset of interactions corresponding to a certain viewpoint and flowgetIntFlow
Calculate number of terms of a databasegetNtermsBYdb
Get number of unique ligands and receptorsgetNumLR
Get Pie Chart of unique coupletsgetPieChart
#' Get radar plot of relative numbers of interactions for a certain cell type #' #' @param tab_c1 barplot dataframe from Viewpoint generated by getBarplotDF2() containing data for condition 1 #' @param tab_c2 barplot dataframe from Viewpoint generated by getBarplotDF2() containing data for condition 2 #' @param tab_c3 barplot dataframe from Viewpoint generated by getBarplotDF2() containing data for condition 3 #' @param lab_c1 label for condition 1 #' @param lab_c2 label for condition 2 #' @param lab_c3 label for condition 3 #' @param cell_name label of cell type of interest #' #' @return plot #' @importFrom fmsb radarchart #' @importFrom data.table transpose getRadarPlot <- function(tab_c1, tab_c2, tab_c3, lab_c1, lab_c2, lab_c3, cell_name) if(is.null(tab_c3)) df <- merge(tab_c1, tab_c2, by = "Clusters", all = TRUE) colnames(df) <- c("Clusters", "nint_c1", "nint_c2") else df <- merge(tab_c1, tab_c2, by = "Clusters", all = TRUE) df <- merge(df, tab_c3, by = "Clusters", all = TRUE) colnames(df) <- c("Clusters", "nint_c1", "nint_c2", "nint_c3") df[is.na(df)] <- 0 cluster_names <- df$Clusters # add max and min max_nint <- max(df[, -1]) df <- add_column(df, max_nint, .after = "Clusters") df <- add_column(df, "min_nint" = 0, .after = "max_nint") radar_df <- data.table::transpose(df[, -1]) if(is.null(lab_c3)) rownames(radar_df) <- c("max", "min", lab_c1, lab_c2) else rownames(radar_df) <- c("max", "min", lab_c1, lab_c2, lab_c3) colnames(radar_df) <- cluster_names color <- c("#438ECC", "#E97778", "#00BA38") fmsb::radarchart( radar_df, axistype = 1, # Customize the polygon pcol = color, pfcol = scales::alpha(color, 0.5), plwd = 2, plty = 1, # Customize the grid cglcol = "grey", cglty = 1, cglwd = 0.8, # Customize the axis axislabcol = "grey30", # Variable labels vlcex = 1.2, vlabels = colnames(radar_df), caxislabels = round(seq(from = 0, to = radar_df["max",1], length.out = 5)), title = cell_name ) legend( x = "bottomleft", legend = rownames(radar_df[-c(1,2),]), horiz = FALSE, bty = "n", pch = 20 , col = color, text.col = "black", cex = 1, pt.cex = 1.5 ) Get radar df of relative numbers of interactions for a certain cell typegetRadar_df
Get table with ranked functional termsgetRankedTerms
Wrapper for other functions to get significant table of func termsgetSignif_table
Calculate significant function per intpair modulegetSignificantFunctions
Get significance of functional terms related to unique int-pairs per conditiongetSignificantFunctions_multiCond
Get Sunburst plot of selected functional termsgetSunburst
Get UMAP for IP modulesgetUMAPipModules
Plot dotplot containing only unique int-pair/cluster pairs with many conditionsgetUniqueDotplot
Get table of unique int-pairs by conditiongetUniqueIntpairs_byCond
Get GO linkgoLink
Input Data exampleinput.data
Read dataframe of cell-cell communication from CellChat (ligand/receptor)read.cellchat
Read output from CellPhoneDB v2.read.CPDBv2
Read custom input file and re-structure it with InterCellar formatread.customInput
Read ICELLNET dataframeread.icellnet
Read output from SingleCellSignalRread.SCsignalR
Run the Shiny Applicationrun_app
Subset int-pair by function matrices to unique int-pairs by conditionsubsetAnnot_multiCond
Subset pairs-function matrix by selected flowsubsetFuncMatBYFlow
Swaps interaction pairs that are R-L to L-Rswap.RLint
Get html link to uniprotuniprotLink
Function that orders all interaction pairs as L-R. Leaves unchanged the R-R and L-LupdateInputLR