README.md

In BarlierC/FunPart: Functional splitting based on functionally relevant set of genes

FunPart

FunPart: Deciphering and characterizing functional heterogeneity in single cell data

FunPart is a computational tool that partitions heterogeneous cell populations into functionally distinct subpopulations and simultaneously identifies modules of functionally relevant set of genes for each of them.

How to install it

Installation of FunPart using Devtools

Please note that FunPart was built under R version 3.5.1. A version compatible with R >= 4.0 will be developed. Details about the R environment and packages versions used can be found at the end of the README (R session info).

install.packages("devtools")
library("devtools")
install_github("BarlierC/FunPart")

How to use it

Load all the necessary packages and prepare the parallel environment

# Load packages and dependencies
library(pheatmap)
require(data.tree)
require("plyr")
require("igraph")
require("EnvStats")
require("data.table")
library(stringr)
library(clusterProfiler)
library(Matrix)
library(doParallel)
library(foreach)
require(Seurat)
library(WGCNA)
require(funpart)

#Parallel background, using 4 cores
clustnum <- parallel::makeCluster(4)
doParallel::registerDoParallel(clustnum)

options(stringsAsFactors = FALSE)

Load necessary data to run FunPart

# 1) The annotation file: annotated immune modules by Singhania et al. (https://doi.org/10.1038/s41467-019-10601-6) 
data(gda)

# 2) Mouse Transcription factors 
data(mouse_tfs)

# 3) Your data (matrix or dataframe with cells in columns and genes in rows)
#data_exp

Run FunPart

#Run FunPart with default parameters
res <- run_functional_splitting(data_exp,mouse_tfs$Symbol,gda)

FunPart parameters

1. scm - single cell RNA-seq expression matrix (*cells in columns, genes in rows)
2. tfs - vector of TFs names or ID (it needs to match with the type of genes you have in your matrix)
3. gda - file used to perform the functional enrichment
4. norm - perform normalization (using Seurat, default is TRUE)
5. qtarget - quantile to select the strongest target genes for each identified cliques (default to 0.90)
6. adjMethod - method used in the enrichment part to perform the p-value adjustement (for possible values, see enricher package, default = "bonferroni")
7. cutoff - p-adjusted value cutoff for the functional enrichment (default=0.05)
8. percExp - percentage of cells a gene needs to be expressed in to be considered (*default=10%)
9. qExp - quantile for which the gene expression not be used as considered too low (default=0.05)

FunPart object

The output is an object of class 'functionalSplitting' composed of 6 slots: 1. data - contains the filtered and normalized matrix used to perform the analysis 2. clust - contains the functional cell states identified 3. genesets - list containing all the genes used to split each level 4. cliques - list of TFs cliques and genes identified for each branch C1 or C2 and each level 5. functionalenrich - list of all the significant functional enrichment found for each genes modules 6. modules - list of all the genes modules identified

Get Summary Table of FunPart object

The function 'getModuleFunctionalState()' will summarize FunPart results into a summary table with 6 columns: 1. Module: M1 or M2 (correspond to cliques slot C1 or C2 of functionalSplitting object respectively) 2. Branch: level and branch (e.g., 0_0_1 is third level branch 1, 0_1 is second level branch 1 and 0 is first level branch 0) 3. Type: intermediate or direct genes modules. An intermediate genes modules characterize a group of functional states whereas a direct modules genes characterize the specific functional state. 4. TFs: transcription factors of the clique (of the genes modules) 5. Genes: genes having a direct interaction with TFs of the clique. TFs of the clique + genes having a direct interaction = genes module 6. Enrichment: immune processes enriched for the specific module

summary_table <- getModuleFunctionalState(res)

R session info

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X Snow Leopard 11.6

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] Seurat_3.1.5           doParallel_1.0.16      iterators_1.0.13       foreach_1.5.1         
 [5] Matrix_1.2-18          clusterProfiler_3.10.1 stringr_1.4.0          data.table_1.14.0     
 [9] EnvStats_2.3.1         igraph_1.2.6           plyr_1.8.6             data.tree_0.7.11      
[13] pheatmap_1.0.12       

loaded via a namespace (and not attached):
  [1] fgsea_1.8.0          Rtsne_0.15           colorspace_2.0-0     ellipsis_0.3.1       ggridges_0.5.2      
  [6] qvalue_2.14.1        leiden_0.3.3         listenv_0.8.0        farver_2.1.0         urltools_1.7.3      
 [11] graphlayouts_0.7.0   ggrepel_0.8.2        bit64_4.0.5          AnnotationDbi_1.44.0 fansi_0.4.2         
 [16] xml2_1.3.2           codetools_0.2-18     splines_3.5.1        cachem_1.0.6         GOSemSim_2.8.0      
 [21] polyclip_1.10-0      jsonlite_1.7.2       ica_1.0-2            cluster_2.1.1        GO.db_3.7.0         
 [26] png_0.1-7            uwot_0.1.8           sctransform_0.2.1    ggforce_0.3.1        BiocManager_1.30.10 
 [31] compiler_3.5.1       httr_1.4.2           rvcheck_0.1.8        lazyeval_0.2.2       assertthat_0.2.1    
 [36] fastmap_1.1.0        tweenr_1.0.1         htmltools_0.5.0      prettyunits_1.1.1    tools_3.5.1         
 [41] rsvd_1.0.3           gtable_0.3.0         glue_1.4.2           RANN_2.6.1           reshape2_1.4.4      
 [46] DO.db_2.9            dplyr_0.8.5          fastmatch_1.1-0      Rcpp_1.0.6           enrichplot_1.2.0    
 [51] Biobase_2.42.0       vctrs_0.3.6          gdata_2.18.0         ape_5.4              nlme_3.1-148        
 [56] lmtest_0.9-38        ggraph_2.0.2         globals_0.14.0       irlba_2.3.3          lifecycle_1.0.0     
 [61] gtools_3.8.2         future_1.18.0        DOSE_3.8.2           zoo_1.8-8            europepmc_0.3       
 [66] MASS_7.3-53.1        scales_1.1.1         tidygraph_1.1.2      hms_0.5.3            RColorBrewer_1.1-2  
 [71] pbapply_1.4-2        reticulate_1.16      memoise_2.0.0        gridExtra_2.3        ggplot2_3.3.3       
 [76] UpSetR_1.4.0         triebeard_0.3.0      stringi_1.4.6        RSQLite_2.2.0        S4Vectors_0.20.1    
 [81] caTools_1.17.1.3     BiocGenerics_0.28.0  BiocParallel_1.16.6  rlang_0.4.10         pkgconfig_2.0.3     
 [86] bitops_1.0-6         lattice_0.20-41      ROCR_1.0-7           purrr_0.3.4          htmlwidgets_1.5.1   
 [91] patchwork_1.0.0      cowplot_1.0.0        bit_4.0.4            tidyselect_1.0.0     RcppAnnoy_0.0.16    
 [96] magrittr_2.0.1       R6_2.5.0             IRanges_2.16.0       gplots_3.0.4         DBI_1.1.1           
[101] pillar_1.5.1         fitdistrplus_1.1-1   survival_3.2-3       tsne_0.1-3           future.apply_1.5.0  
[106] tibble_3.1.0         crayon_1.4.1         KernSmooth_2.23-18   utf8_1.1.4           plotly_4.9.2.1      
[111] viridis_0.5.1        progress_1.2.2       grid_3.5.1           blob_1.2.1           digest_0.6.27       
[116] tidyr_1.0.2          gridGraphics_0.5-1   stats4_3.5.1         munsell_0.5.0        viridisLite_0.3.0   
[121] ggplotify_0.0.5      

Additional package (directly called in sub-functions):
WGCNA 1.69

BarlierC/FunPart documentation built on Dec. 17, 2021, 10:45 a.m.