Package: InterCellar 2.19.0

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_2.19.0.tar.gz
InterCellar_2.19.0.zip(r-4.7-any)InterCellar_2.19.0.zip(r-4.6-any)InterCellar_2.19.0.zip(r-4.5-any)
InterCellar_2.19.0.tgz(r-4.6-any)InterCellar_2.19.0.tgz(r-4.5-any)
InterCellar_2.19.0.tar.gz(r-4.7-any)InterCellar_2.19.0.tar.gz(r-4.6-any)
InterCellar_2.19.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
InterCellar/json (API)

# Install 'InterCellar' in R:

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

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

Datasets:

input.data - Input Data example

On BioConductor:InterCellar-2.19.0(bioc 3.24)InterCellar-2.18.0(bioc 3.23)

software singlecell visualization go transcriptomics

5.52 score 11 stars 20 scripts 446 downloads 5 exports 198 dependencies

Last updated from:6a4a6b28b2. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	221
linux-devel	NOTE	430
source / vignettes	OK	349
linux-release	NOTE	332
macos-release	NOTE	254
macos-oldrel	NOTE	191
windows-devel	NOTE	234
windows-release	NOTE	222
windows-oldrel	NOTE	213
wasm-release	OK	161

Exports:checkLL_RR getClusterNames getGeneTable getIntFlow run_app

Dependencies:abind AnnotationDbi askpass attempt backports base64enc Biobase BiocBaseUtils BiocFileCache BiocGenerics biomaRt Biostrings bit bit64 blob boot broom bslib cachem car carData cellranger circlize cli clue cluster codetools colorspace colourpicker commonmark ComplexHeatmap config corrplot cowplot cpp11 crayon crosstalk curl data.table DBI dbplyr dendextend Deriv digest doBy doParallel dplyr DT ellipse emmeans estimability evaluate factoextra FactoMineR farver fastmap filelock flashClust fmsb fontawesome foreach forecast Formula fracdiff fs generics GetoptLong ggplot2 ggpubr ggrepel ggsci ggsignif GlobalOptions glue golem gridExtra gtable here highr hms htmltools htmlwidgets httpuv httr httr2 igraph IRanges irlba isoband iterators jquerylib jsonlite KEGGREST knitr labeling later lattice lazyeval leaps lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels matrixStats memoise mgcv mime miniUI minqa modelr multcompView mvtnorm nlme nloptr nnet numDeriv openssl otel pbkrtest pillar pkgconfig plotly plyr png polynom prettyunits progress promises purrr quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen RcppTOML Rdpack readxl reformulas rematch reticulate rjson rlang rmarkdown rprojroot RSpectra RSQLite rstatix S4Vectors S7 sass scales scatterplot3d Seqinfo shape shiny shinyalert shinycssloaders shinydashboard shinyFeedback shinyFiles shinyjs showtext showtextdb signal sourcetools SparseM stringi stringr survival sys sysfonts tibble tidyr tidyselect timeDate tinytex umap urca utf8 uuid vctrs viridis viridisLite visNetwork withr wordcloud2 xfun xml2 xtable XVector yaml zoo

InterCellar User Guide

Rendered fromuser_guide.Rmdusingknitr::rmarkdown

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

Help page	Topics
Perform GO annotation of input data	annotateGO
Annotate pathways for input data	annotatePathways
Build binary matrix with int-pairs in rows, functions in cols	buildPairsbyFunctionMatrix
Manually change the annotation of L-L and R-R pairs	checkLL_RR
Plot circle plot	circlePlot
Combine GO annotation and pathways in a unique object	combineAnnotations
Create Barplot cluster-verse	createBarPlot_CV
Create ggplot barplot to be saved in tiff	createBarPlot1_ggplot
Create barplot of number of interaction for selected cluster	createBarPlot2_CV
Create ggplot barplot of Nint per cluster selected	createBarPlot2_ggplot
Create Network of clusters	createNetwork
Get dendrogram of int pair modules	dendroIntPairModules
Determine the elbow point on a curve (from package akmedoids)	elbowPoint
Get html link to ensembl	ensemblLink
Get back-to-back barplot for 2 conditions comparison	getBack2BackBarplot
Get dataframe for plotting barplot (all clusters)	getBarplotDF
Get dataframe for barplot (by cluster)	getBarplotDF2
Get cluster names only from sender cluster A	getClusterA_Names
Get colors for clusters	getClusterColors
Get clusters names from initial input data	getClusterNames
Creating edges dataframe for network of clusters	getClusterNetwork
Get Clusters size	getClusterSize
Get table of unique int-pairs/clust-pairs couplets	getDistinctCouplets
Functions to plot DotPlots	getDotPlot_selInt
Get table for gene-verse	getGeneTable
Connection to Ensembl via biomaRt to get GO terms	getGObiomaRt
Subfunction to calculate significant functions by permutation test	getHitsf
Get subset of interactions corresponding to a certain viewpoint and flow	getIntFlow
Calculate number of terms of a database	getNtermsBYdb
Get number of unique ligands and receptors	getNumLR
Get Pie Chart of unique couplets	getPieChart
#' 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 type	getRadar_df
Get table with ranked functional terms	getRankedTerms
Wrapper for other functions to get significant table of func terms	getSignif_table
Calculate significant function per intpair module	getSignificantFunctions
Get significance of functional terms related to unique int-pairs per condition	getSignificantFunctions_multiCond
Get Sunburst plot of selected functional terms	getSunburst
Get UMAP for IP modules	getUMAPipModules
Plot dotplot containing only unique int-pair/cluster pairs with many conditions	getUniqueDotplot
Get table of unique int-pairs by condition	getUniqueIntpairs_byCond
Get GO link	goLink
Input Data example	input.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 format	read.customInput
Read ICELLNET dataframe	read.icellnet
Read output from SingleCellSignalR	read.SCsignalR
Run the Shiny Application	run_app
Subset int-pair by function matrices to unique int-pairs by condition	subsetAnnot_multiCond
Subset pairs-function matrix by selected flow	subsetFuncMatBYFlow
Swaps interaction pairs that are R-L to L-R	swap.RLint
Get html link to uniprot	uniprotLink
Function that orders all interaction pairs as L-R. Leaves unchanged the R-R and L-L	updateInputLR

Package: InterCellar 2.19.0

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

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