Analysis of single-cell epigenomics datasets with a Shiny App


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Documentation for package ‘ChromSCape’ version 1.16.0

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A B C D E F G H I L M N P R S T W

-- A --

annotation_from_merged_peaks Find nearest peaks of each gene and return refined annotation
annotToCol2 annotToCol2
anocol_binary Helper binary column for anocol function
anocol_categorical Helper binary column for anocol function

-- B --

bams_to_matrix_indexes Count bam files on interval to create count indexes
beds_to_matrix_indexes Count bed files on interval to create count indexes

-- C --

calculate_CNA Estimate copy number alterations in cytobands
calculate_cyto_mat Calculate Fraction of reads in each cytobands
calculate_gain_or_loss Estimate the copy gains/loss of tumor vs normal based on log2-ratio of fraction of reads
calculate_logRatio_CNA Calculate the log2-ratio of tumor vs normal fraction of reads in cytobands
call_macs2_merge_peaks Calling MACS2 peak caller and merging resulting peaks
changeRange changeRange
CheA3_TF_nTargets A data.frame with the number of targets of each TF in ChEA3
check_correct_datamatrix Check if matrix rownames are well formated and correct if needed
choose_cluster_scExp Choose a number of clusters
choose_perplexity Choose perplexity depending on number of cells for Tsne
col2hex Col2Hex
colors_scExp Adding colors to cells & features
combine_datamatrix Combine two matrices and emit warning if no regions are in common
combine_enrichmentTests Run enrichment tests and combine into list
comparable_variables Find comparable variable scExp
CompareedgeRGLM Creates a summary table with the number of genes under- or overexpressed in each group and outputs several graphical representations
CompareWilcox CompareWilcox
concatenate_scBed_into_clusters Concatenate single-cell BED into clusters
consensus_clustering_scExp Wrapper to apply ConsensusClusterPlus to scExp object
correlation_and_hierarchical_clust_scExp Correlation and hierarchical clustering
count_coverage Create a smoothed and normalized coverage track from a BAM file and given a bin GenomicRanges object (same as deepTools bamCoverage)
create_project_folder Create ChromSCape project folder
create_sample_name_mat Create a sample name matrix
create_scDataset_raw Create a simulated single cell datamatrix & cell annotation
create_scExp Wrapper to create the single cell experiment from count matrix and feature dataframe

-- D --

DA_custom Differential Analysis Custom in 'One vs One' mode
DA_one_vs_rest Differential Analysis in 'One vs Rest' mode
DA_pairwise Run differential analysis in Pairwise mode
define_feature Define the features on which reads will be counted
detect_samples Heuristic discovery of samples based on cell labels
differential_activation Find Differentialy Activated Features (One vs All)
differential_analysis_scExp Runs differential analysis between cell clusters
distPearson distPearson

-- E --

enrichmentTest enrichmentTest
enrich_TF_ChEA3_genes Find the TF that are enriched in the differential genes using ChEA3 API
enrich_TF_ChEA3_scExp Find the TF that are enriched in the differential genes using ChEA3 database
exclude_features_scExp Remove specific features (CNA, repeats)

-- F --

feature_annotation_scExp Add gene annotations to features
filter_correlated_cell_scExp Filter lowly correlated cells
filter_genes_with_refined_peak_annotation Filter genes based on peak calling refined annotation
filter_scExp Filter cells and features
find_clusters_louvain_scExp Build SNN graph and find cluster using Louvain Algorithm
find_top_features Find most covered features

-- G --

generate_analysis Generate a complete ChromSCape analysis
generate_count_matrix Generate count matrix
generate_coverage_tracks Generate cell cluster pseudo-bulk coverage tracks
generate_feature_names Generate feature names
generate_report From a ChromSCape analysis directory, generate an HTML report.
gene_set_enrichment_analysis_scExp Runs Gene Set Enrichment Analysis on genes associated with differential features
getExperimentNames Get experiment names from a SingleCellExperiment
getMainExperiment Get Main experiment of a SingleCellExperiment
get_color_dataframe_from_input Get color dataframe from shiny::colorInput
get_cyto_features Map features onto cytobands
get_genomic_coordinates Get SingleCellExperiment's genomic coordinates
get_most_variable_cyto Retrieve the cytobands with the most variable fraction of reads
get_pathway_mat_scExp Get pathway matrix
gg_fill_hue gg_fill_hue
groupMat groupMat

-- H --

H1proportion H1proportion
has_genomic_coordinates Does SingleCellExperiment has genomic coordinates in features ?
hclustAnnotHeatmapPlot hclustAnnotHeatmapPlot
hg38.chromosomes Data.frame of chromosome length - hg38
hg38.cytoBand Data.frame of cytoBandlocation - hg38
hg38.GeneTSS Data.frame of gene TSS - hg38

-- I --

imageCol imageCol
import_count_input_files Import and count input files depending on their format
import_scExp Read single-cell matrix(ces) into scExp
index_peaks_barcodes_to_matrix_indexes Read index-peaks-barcodes trio files on interval to create count indexes
inter_correlation_scExp Calculate inter correlation between cluster or samples
intra_correlation_scExp Calculate intra correlation between cluster or samples

-- L --

launchApp Launch ChromSCape
load_MSIGdb Load and format MSIGdb pathways using msigdbr package

-- M --

merge_MACS2_peaks Merge peak files from MACS2 peak caller
mm10.chromosomes Data.frame of chromosome length - mm10
mm10.cytoBand Data.frame of cytoBandlocation - mm10
mm10.GeneTSS Data.frame of gene TSS - mm10

-- N --

normalize_scExp Normalize counts
num_cell_after_cor_filt_scExp Number of cells before & after correlation filtering
num_cell_after_QC_filt_scExp Table of cells before / after QC
num_cell_before_cor_filt_scExp Table of number of cells before correlation filtering
num_cell_in_cluster_scExp Number of cells in each cluster
num_cell_scExp Table of cells

-- P --

pca_irlba_for_sparseMatrix Run sparse PCA using irlba SVD
plot_cluster_consensus_scExp Plot cluster consensus
plot_correlation_PCA_scExp Plotting correlation of PCs with a variable of interest
plot_coverage_BigWig Coverage plot
plot_differential_summary_scExp Differential summary barplot
plot_differential_volcano_scExp Volcano plot of differential features
plot_distribution_scExp Plotting distribution of signal
plot_gain_or_loss_barplots Plot Gain or Loss of cytobands of the most variables cytobands
plot_heatmap_scExp Plot cell correlation heatmap with annotations
plot_inter_correlation_scExp Violin plot of inter-correlation distribution between one or multiple groups and one reference group
plot_intra_correlation_scExp Violin plot of intra-correlation distribution
plot_most_contributing_features Plot Top/Bottom most contributing features to PCA
plot_percent_active_feature_scExp Barplot of the % of active cells for a given features
plot_pie_most_contributing_chr Pie chart of top contribution of chromosomes in the 100 most contributing features to PCA #'
plot_reduced_dim_scExp Plot reduced dimensions (PCA, TSNE, UMAP)
plot_reduced_dim_scExp_CNA Plot UMAP colored by Gain or Loss of cytobands
plot_top_TF_scExp Barplot of top TFs from ChEA3 TF enrichment analysis
plot_violin_feature_scExp Violin plot of features
preprocessing_filtering_and_reduction Preprocess and filter matrix annotation data project folder to SCE
preprocess_CPM Preprocess scExp - Counts Per Million (CPM)
preprocess_feature_size_only Preprocess scExp - size only
preprocess_RPKM Preprocess scExp - Read per Kilobase Per Million (RPKM)
preprocess_TFIDF Preprocess scExp - TF-IDF
preprocess_TPM Preprocess scExp - Transcripts per Million (TPM)

-- R --

rawfile_ToBigWig rawfile_ToBigWig : reads in BAM file and write out BigWig coverage file, normalized and smoothed
raw_counts_to_sparse_matrix Create a sparse count matrix from various format of input data.
read_count_mat_with_separated_chr_start_end Read a count matrix with three first columns (chr,start,end)
read_sparse_matrix Read in one or multiple sparse matrices (10X format)
rebin_helper Rebin Helper for rebin_matrix function
rebin_matrix Transforms a bins x cells count matrix into a larger bins x cells count matrix.
reduce_dims_scExp Reduce dimensions (PCA, TSNE, UMAP)
reduce_dim_batch_correction Reduce dimension with batch corrections
remove_chr_M_fun Remove chromosome M from scExprownames
remove_non_canonical_fun Remove non canonical chromosomes from scExp
results_enrichmentTest Resutls of hypergeometric gene set enrichment test
retrieve_top_bot_features_pca Retrieve Top and Bot most contributing features of PCA
run_pairwise_tests Run pairwise tests
run_tsne_scExp Run tsne on single cell experiment

-- S --

scExp A SingleCellExperiment outputed by ChromSCape
separate_BAM_into_clusters Separate BAM files into cell cluster BAM files
separator_count_mat Determine Count matrix separator ("tab" or ",")
smoothBin Smooth a vector of values with nb_bins left and righ values
subsample_scExp Subsample scExp
subset_bam_call_peaks Peak calling on cell clusters
summary_DA Summary of the differential analysis
swapAltExp_sameColData Swap main & alternative Experiments, with fixed colData

-- T --

table_enriched_genes_scExp Creates table of enriched genes sets

-- W --

warning_DA Warning for differential_analysis_scExp
warning_filter_correlated_cell_scExp warning_filter_correlated_cell_scExp
warning_plot_reduced_dim_scExp A warning helper for plot_reduced_dim_scExp
warning_raw_counts_to_sparse_matrix Warning for raw_counts_to_sparse_matrix
wrapper_Signac_FeatureMatrix Wrapper around 'FeatureMatrix' function from Signac Package