Package: ChromSCape 1.23.0

Pacome Prompsy

ChromSCape: Analysis of single-cell epigenomics datasets with a Shiny App

ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis.

Authors:Pacome Prompsy [aut, cre], Celine Vallot [aut]

ChromSCape_1.23.0.tar.gz
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ChromSCape_1.23.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ChromSCape/json (API)

# Install 'ChromSCape' in R:

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

Bug tracker:https://github.com/vallotlab/chromscape/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

ce11.chromosomes - Data.frame of chromosome length - ce11
ce11.GeneTSS - Data.frame of gene TSS - ce11
CheA3_TF_nTargets - A data.frame with the number of targets of each TF in ChEA3
hg38.chromosomes - Data.frame of chromosome length - hg38
hg38.cytoBand - Data.frame of cytoBandlocation - hg38
hg38.GeneTSS - Data.frame of gene TSS - hg38
mm10.chromosomes - Data.frame of chromosome length - mm10
mm10.cytoBand - Data.frame of cytoBandlocation - mm10
mm10.GeneTSS - Data.frame of gene TSS - mm10
scExp - A SingleCellExperiment outputed by ChromSCape

On BioConductor:ChromSCape-1.23.0(bioc 3.24)ChromSCape-1.22.0(bioc 3.23)

shinyapps software singlecell chipseq atacseq methylseq classification clustering epigenetics principalcomponent annotation batcheffect multiplecomparison normalization pathways preprocessing qualitycontrol reportwriting visualization genesetenrichment differentialpeakcalling epigenomics shiny single-cell cpp

6.42 score 14 stars 18 scripts 1 mentions 82 exports 195 dependencies

Last updated from:7200a92780. Checks:12 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	310
linux-devel-arm64	NOTE	691
linux-devel-x86_64	NOTE	841
source / vignettes	OK	440
linux-release-arm64	NOTE	779
linux-release-x86_64	NOTE	929
macos-release-arm64	NOTE	600
macos-release-x86_64	NOTE	1538
macos-oldrel-arm64	NOTE	516
macos-oldrel-x86_64	NOTE	937
windows-devel	NOTE	795
windows-release	NOTE	818
windows-oldrel	NOTE	817
wasm-release	OK	278

Exports:annotation_from_merged_peaks annotToCol2 calculate_CNA calculate_cyto_mat calculate_gain_or_loss calculate_logRatio_CNA choose_cluster_scExp colors_scExp comparable_variables CompareedgeRGLM CompareWilcox consensus_clustering_scExp correlation_and_hierarchical_clust_scExp create_project_folder create_scDataset_raw create_scExp define_feature detect_samples differential_activation differential_analysis_scExp enrich_TF_ChEA3_genes enrich_TF_ChEA3_scExp exclude_features_scExp feature_annotation_scExp filter_correlated_cell_scExp filter_scExp find_clusters_louvain_scExp find_top_features gene_set_enrichment_analysis_scExp generate_analysis generate_coverage_tracks generate_report get_cyto_features get_genomic_coordinates get_most_variable_cyto get_pathway_mat_scExp getExperimentNames getMainExperiment has_genomic_coordinates import_scExp inter_correlation_scExp intra_correlation_scExp launchApp normalize_scExp num_cell_after_cor_filt_scExp num_cell_after_QC_filt_scExp num_cell_before_cor_filt_scExp num_cell_in_cluster_scExp num_cell_scExp plot_cluster_consensus_scExp plot_correlation_PCA_scExp plot_coverage_BigWig plot_differential_summary_scExp plot_differential_volcano_scExp plot_distribution_scExp plot_gain_or_loss_barplots plot_heatmap_scExp plot_inter_correlation_scExp plot_intra_correlation_scExp plot_most_contributing_features plot_percent_active_feature_scExp plot_pie_most_contributing_chr plot_reduced_dim_scExp plot_reduced_dim_scExp_CNA plot_top_TF_scExp plot_violin_feature_scExp preprocess_CPM preprocess_feature_size_only preprocess_RPKM preprocess_TFIDF preprocess_TPM preprocessing_filtering_and_reduction read_sparse_matrix rebin_matrix reduce_dims_scExp run_tsne_scExp subsample_scExp subset_bam_call_peaks summary_DA swapAltExp_sameColData table_enriched_genes_scExp wrapper_Signac_FeatureMatrix

Dependencies:abind ALL askpass assertthat assorthead babelgene base64enc batchelor beachmat beeswarm BH Biobase BiocBaseUtils BiocGenerics BiocIO BiocNeighbors BiocParallel BiocSingular Biostrings bitops bluster bslib cachem cigarillo cli cluster codetools colorRamps colourpicker commonmark ConsensusClusterPlus coop cpp11 crayon crosstalk curl data.table DelayedArray DelayedMatrixStats digest dplyr dqrng DT edgeR evaluate farver fastmap flexdashboard FNN fontawesome forcats formatR fresh fs futile.logger futile.options generics GenomicAlignments GenomicRanges ggbeeswarm ggfittext gggenes ggplot2 ggrepel glue gridExtra gridtext gtable here highr htmltools htmlwidgets httpuv httr igraph IRanges irlba isoband jpeg jquerylib jsonlite kableExtra KernSmooth knitr labeling lambda.r later lattice lazyeval lifecycle limma litedown locfit magrittr markdown Matrix MatrixGenerics matrixStats matrixTests memoise metapod mime miniUI msigdbr openssl otel pheatmap pillar pkgconfig plotly png promises purrr qs2 qualV R6 rappdirs RColorBrewer Rcpp RcppAnnoy RcppEigen RcppML RcppParallel RcppProgress RcppTOML RCurl ResidualMatrix restfulr reticulate Rhtslib rjson rlang rlist rmarkdown rprojroot Rsamtools RSpectra rstudioapi rsvd rtracklayer Rtsne S4Arrays S4Vectors S7 sass ScaledMatrix scales scater scran scuttle Seqinfo shades shiny shinycssloaders shinydashboard shinydashboardPlus shinyFiles shinyhelper shinyjs shinyWidgets SingleCellExperiment sitmo snow sourcetools SparseArray sparseMatrixStats statmod stringdist stringfish stringi stringr SummarizedExperiment svglite sys systemfonts textshaping tibble tidyr tidyselect tinytex umap utf8 uwot vctrs vipor viridis viridisLite waiter withr xfun XML xml2 xtable XVector yaml

ChromSCape

Rendered fromvignette.Rmdusingknitr::rmarkdown

Last update: 2021-05-14
Started: 2020-09-10

Help page	Topics
Find nearest peaks of each gene and return refined annotation	annotation_from_merged_peaks
annotToCol2	annotToCol2
Helper binary column for anocol function	anocol_binary
Helper binary column for anocol function	anocol_categorical
Count bam files on interval to create count indexes	bams_to_matrix_indexes
Count bed files on interval to create count indexes	beds_to_matrix_indexes
Estimate copy number alterations in cytobands	calculate_CNA
Calculate Fraction of reads in each cytobands	calculate_cyto_mat
Estimate the copy gains/loss of tumor vs normal based on log2-ratio of fraction of reads	calculate_gain_or_loss
Calculate the log2-ratio of tumor vs normal fraction of reads in cytobands	calculate_logRatio_CNA
Calling MACS2 peak caller and merging resulting peaks	call_macs2_merge_peaks
Data.frame of chromosome length - ce11	ce11.chromosomes
Data.frame of gene TSS - ce11	ce11.GeneTSS
changeRange	changeRange
A data.frame with the number of targets of each TF in ChEA3	CheA3_TF_nTargets
Check if matrix rownames are well formated and correct if needed	check_correct_datamatrix
Choose a number of clusters	choose_cluster_scExp
Choose perplexity depending on number of cells for Tsne	choose_perplexity
Col2Hex	col2hex
Adding colors to cells & features	colors_scExp
Combine two matrices and emit warning if no regions are in common	combine_datamatrix
Run enrichment tests and combine into list	combine_enrichmentTests
Find comparable variable scExp	comparable_variables
Creates a summary table with the number of genes under- or overexpressed in each group and outputs several graphical representations	CompareedgeRGLM
CompareWilcox	CompareWilcox
Concatenate single-cell BED into clusters	concatenate_scBed_into_clusters
Wrapper to apply ConsensusClusterPlus to scExp object	consensus_clustering_scExp
Correlation and hierarchical clustering	correlation_and_hierarchical_clust_scExp
Create a smoothed and normalized coverage track from a BAM file and given a bin GenomicRanges object (same as deepTools bamCoverage)	count_coverage
Create ChromSCape project folder	create_project_folder
Create a sample name matrix	create_sample_name_mat
Create a simulated single cell datamatrix & cell annotation	create_scDataset_raw
Wrapper to create the single cell experiment from count matrix and feature dataframe	create_scExp
Differential Analysis Custom in 'One vs One' mode	DA_custom
Differential Analysis in 'One vs Rest' mode	DA_one_vs_rest
Run differential analysis in Pairwise mode	DA_pairwise
Define the features on which reads will be counted	define_feature
Heuristic discovery of samples based on cell labels	detect_samples
Find Differentialy Activated Features (One vs All)	differential_activation
Runs differential analysis between cell clusters	differential_analysis_scExp
distPearson	distPearson
Find the TF that are enriched in the differential genes using ChEA3 API	enrich_TF_ChEA3_genes
Find the TF that are enriched in the differential genes using ChEA3 database	enrich_TF_ChEA3_scExp
enrichmentTest	enrichmentTest
Remove specific features (CNA, repeats)	exclude_features_scExp
Add gene annotations to features	feature_annotation_scExp
Filter lowly correlated cells	filter_correlated_cell_scExp
Filter genes based on peak calling refined annotation	filter_genes_with_refined_peak_annotation
Filter cells and features	filter_scExp
Build SNN graph and find cluster using Louvain Algorithm	find_clusters_louvain_scExp
Find most covered features	find_top_features
Runs Gene Set Enrichment Analysis on genes associated with differential features	gene_set_enrichment_analysis_scExp
Generate a complete ChromSCape analysis	generate_analysis
Generate count matrix	generate_count_matrix
Generate cell cluster pseudo-bulk coverage tracks	generate_coverage_tracks
Generate feature names	generate_feature_names
From a ChromSCape analysis directory, generate an HTML report.	generate_report
Get color dataframe from shiny::colorInput	get_color_dataframe_from_input
Map features onto cytobands	get_cyto_features
Get SingleCellExperiment's genomic coordinates	get_genomic_coordinates
Retrieve the cytobands with the most variable fraction of reads	get_most_variable_cyto
Get pathway matrix	get_pathway_mat_scExp
Get experiment names from a SingleCellExperiment	getExperimentNames
Get Main experiment of a SingleCellExperiment	getMainExperiment
gg_fill_hue	gg_fill_hue
groupMat	groupMat
H1proportion	H1proportion
Does SingleCellExperiment has genomic coordinates in features ?	has_genomic_coordinates
hclustAnnotHeatmapPlot	hclustAnnotHeatmapPlot
Data.frame of chromosome length - hg38	hg38.chromosomes
Data.frame of cytoBandlocation - hg38	hg38.cytoBand
Data.frame of gene TSS - hg38	hg38.GeneTSS
imageCol	imageCol
Import and count input files depending on their format	import_count_input_files
Read single-cell matrix(ces) into scExp	import_scExp
Read index-peaks-barcodes trio files on interval to create count indexes	index_peaks_barcodes_to_matrix_indexes
Calculate inter correlation between cluster or samples	inter_correlation_scExp
Calculate intra correlation between cluster or samples	intra_correlation_scExp
Launch ChromSCape	launchApp
Load and format MSIGdb pathways using msigdbr package	load_MSIGdb
Merge peak files from MACS2 peak caller	merge_MACS2_peaks
Data.frame of chromosome length - mm10	mm10.chromosomes
Data.frame of cytoBandlocation - mm10	mm10.cytoBand
Data.frame of gene TSS - mm10	mm10.GeneTSS
Normalize counts	normalize_scExp
Number of cells before & after correlation filtering	num_cell_after_cor_filt_scExp
Table of cells before / after QC	num_cell_after_QC_filt_scExp
Table of number of cells before correlation filtering	num_cell_before_cor_filt_scExp
Number of cells in each cluster	num_cell_in_cluster_scExp
Table of cells	num_cell_scExp
Run sparse PCA using irlba SVD	pca_irlba_for_sparseMatrix
Plot cluster consensus	plot_cluster_consensus_scExp
Plotting correlation of PCs with a variable of interest	plot_correlation_PCA_scExp
Coverage plot	plot_coverage_BigWig
Differential summary barplot	plot_differential_summary_scExp
Volcano plot of differential features	plot_differential_volcano_scExp
Plotting distribution of signal	plot_distribution_scExp
Plot Gain or Loss of cytobands of the most variables cytobands	plot_gain_or_loss_barplots
Plot cell correlation heatmap with annotations	plot_heatmap_scExp
Violin plot of inter-correlation distribution between one or multiple groups and one reference group	plot_inter_correlation_scExp
Violin plot of intra-correlation distribution	plot_intra_correlation_scExp
Plot Top/Bottom most contributing features to PCA	plot_most_contributing_features
Barplot of the % of active cells for a given features	plot_percent_active_feature_scExp
Pie chart of top contribution of chromosomes in the 100 most contributing features to PCA #'	plot_pie_most_contributing_chr
Plot reduced dimensions (PCA, TSNE, UMAP)	plot_reduced_dim_scExp
Plot UMAP colored by Gain or Loss of cytobands	plot_reduced_dim_scExp_CNA
Barplot of top TFs from ChEA3 TF enrichment analysis	plot_top_TF_scExp
Violin plot of features	plot_violin_feature_scExp
Preprocess scExp - Counts Per Million (CPM)	preprocess_CPM
Preprocess scExp - size only	preprocess_feature_size_only
Preprocess scExp - Read per Kilobase Per Million (RPKM)	preprocess_RPKM
Preprocess scExp - TF-IDF	preprocess_TFIDF
Preprocess scExp - Transcripts per Million (TPM)	preprocess_TPM
Preprocess and filter matrix annotation data project folder to SCE	preprocessing_filtering_and_reduction
Create a sparse count matrix from various format of input data.	raw_counts_to_sparse_matrix
rawfile_ToBigWig : reads in BAM file and write out BigWig coverage file, normalized and smoothed	rawfile_ToBigWig
Read a count matrix with three first columns (chr,start,end)	read_count_mat_with_separated_chr_start_end
Read in one or multiple sparse matrices (10X format)	read_sparse_matrix
Rebin Helper for rebin_matrix function	rebin_helper
Transforms a bins x cells count matrix into a larger bins x cells count matrix.	rebin_matrix
Reduce dimension with batch corrections	reduce_dim_batch_correction
Reduce dimensions (PCA, TSNE, UMAP)	reduce_dims_scExp
Remove chromosome M from scExprownames	remove_chr_M_fun
Remove non canonical chromosomes from scExp	remove_non_canonical_fun
Resutls of hypergeometric gene set enrichment test	results_enrichmentTest
Retrieve Top and Bot most contributing features of PCA	retrieve_top_bot_features_pca
Run pairwise tests	run_pairwise_tests
Run tsne on single cell experiment	run_tsne_scExp
A SingleCellExperiment outputed by ChromSCape	scExp
Separate BAM files into cell cluster BAM files	separate_BAM_into_clusters
Determine Count matrix separator ("tab" or ",")	separator_count_mat
Smooth a vector of values with nb_bins left and righ values	smoothBin
Subsample scExp	subsample_scExp
Peak calling on cell clusters	subset_bam_call_peaks
Summary of the differential analysis	summary_DA
Swap main & alternative Experiments, with fixed colData	swapAltExp_sameColData
Creates table of enriched genes sets	table_enriched_genes_scExp
Warning for differential_analysis_scExp	warning_DA
warning_filter_correlated_cell_scExp	warning_filter_correlated_cell_scExp
A warning helper for plot_reduced_dim_scExp	warning_plot_reduced_dim_scExp
Warning for raw_counts_to_sparse_matrix	warning_raw_counts_to_sparse_matrix
Wrapper around 'FeatureMatrix' function from Signac Package	wrapper_Signac_FeatureMatrix

Package: ChromSCape 1.23.0

ChromSCape: Analysis of single-cell epigenomics datasets with a Shiny App

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