# Analysis of single-cell epigenomics datasets with a Shiny App

## Documentation for package ‘ChromSCape’ version 1.8.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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_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