Analysis of single-cell epigenomics datasets with a Shiny App


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Documentation for package ‘ChromSCape’ version 1.0.0

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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
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
call_macs2_merge_peaks Calling MACS2 peak caller and merging resulting peaks
changeRange changeRange
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
CompareedgeRGLM Creates a summary table with the number of genes under- or overexpressed in each group and outputs several graphical representations
CompareWilcox CompareWilcox
consensus_clustering_scExp Wrapper to apply ConsensusClusterPlus to scExp object
correlation_and_hierarchical_clust_scExp Correlation and hierarchical clustering
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
DA_one_vs_rest_fun 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_analysis_scExp Runs differential analysis between cell clusters
distPearson distPearson
enrichmentTest enrichmentTest
exclude_features_scExp Remove specific features (CNA, repeats)
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
generate_count_matrix Generate count matrix
generate_feature_names Generate feature names
gene_set_enrichment_analysis_scExp Runs Gene Set Enrichment Analysis on genes associated with differential features
get_color_dataframe_from_input Get color dataframe from shiny::colorInput
get_genomic_coordinates Get SingleCellExperiment's genomic coordinates
gg_fill_hue gg_fill_hue
groupMat groupMat
H1proportion H1proportion
has_genomic_coordinates Does SingleCellExperiment has genomic coordinates in features ?
hclustAnnotHeatmapPlot hclustAnnotHeatmapPlot
hg38.chromosomes Data.frame of chromosome length - hg38
hg38.GeneTSS Data.frame of gene TSS - hg38
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
launchApp Launch ChromSCape
load_MSIGdb Load and format MSIGdb pathways using msigdbr package
merge_MACS2_peaks Merge peak files from MACS2 peak caller
mm10.chromosomes Data.frame of chromosome length - mm10
mm10.GeneTSS Data.frame of gene TSS - mm10
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
pca_irlba_for_sparseMatrix Run sparse PCA using irlba SVD
peaks_to_bins Transforms a peaks x cells count matrix into a bins x cells count matrix.
plot_cluster_consensus_scExp Plot cluster consensus
plot_differential_H1_scExp Differential H1 distribution 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_heatmap_scExp Plot cell correlation heatmap with annotations
plot_reduced_dim_scExp Plot reduced dimensions (PCA, TSNE, UMAP)
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_TPM Preprocess scExp - Transcripts per Million (TPM)
raw_counts_to_feature_count_files 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)
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
run_pairwise_tests Run pairwise tests
run_tsne_scExp Run tsne on single cell experiment
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 ",")
subsample_scExp Subsample scExp
subset_bam_call_peaks Peak calling on cell clusters
table_enriched_genes_scExp Creates table of enriched genes sets
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_feature_count_files Warning for _raw_counts_to_feature_count_files