generate_analysis: Generate a complete ChromSCape analysis

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

Generate a complete ChromSCape analysis

Description

Generate a complete ChromSCape analysis

Usage

generate_analysis(input_data_folder,
analysis_name = "Analysis_1",
output_directory = "./",
input_data_type = c("scBED", "DenseMatrix", "SparseMatrix", "scBAM")[1],
rebin_sparse_matrix = FALSE,
feature_count_on = c("bins","genebody","peaks")[1],
feature_count_parameter = 50000,
ref_genome = c("hg38","mm10", "ce11")[1],
run = c("filter", "CNA","cluster", "consensus","peak_call", "coverage", 
       "DA", "GSA", "report")[c(1,3,6,7,8,9)],
min_reads_per_cell = 1000,
max_quantile_read_per_cell = 99,
n_top_features = 40000,
norm_type = "CPM",
subsample_n = NULL,
exclude_regions = NULL,
n_clust = NULL,
corr_threshold = 99,
percent_correlation = 1,
maxK = 10,
qval.th = 0.1,
logFC.th = 1,
enrichment_qval = 0.1,
doBatchCorr  = FALSE,
batch_sels  = NULL,
control_samples_CNA = NULL,
genes_to_plot = c("Krt8","Krt5","Tgfb1", "Foxq1", "Cdkn2b",
                 "Cdkn2a", "chr7:15000000-20000000")
)

Arguments

input_data_folder	Directory containing the input data.
analysis_name	Name given to the analysis.
output_directory	Directory where to create the analysis and the HTML report.
input_data_type	The type of input data.
feature_count_on	For raw data type, on which features to count the cells.
feature_count_parameter	Additional parameter corresponding to the 'feature_count_on' parameter. E.g. for 'bins' must be a numeric, e.g. 50000, for 'peaks' must be a character containing path towards a BED peak file.
rebin_sparse_matrix	A boolean specifying if the SparseMatrix should be rebinned on features (see feature_count_on and feature_count_parameter).
ref_genome	The genome of reference.
run	What steps to run. By default runs everything. Some steps are required in order to run downstream steps.
min_reads_per_cell	Minimum number of reads per cell.
max_quantile_read_per_cell	Upper quantile above which to consider cells doublets.
n_top_features	Number of features to keep in the analysis.
norm_type	Normalization type.
subsample_n	Number of cells per condition to downsample to, for performance principally.
exclude_regions	Path towards a BED file containing CNA to exclude from the analysis (optional).
n_clust	Number of clusters to force choice of clusters.
corr_threshold	Quantile of correlation above which two cells are considered as correlated.
percent_correlation	Percentage of the total cells that a cell must be correlated with in order to be kept in the analysis.
maxK	Upper cluster number to rest for ConsensusClusterPlus.
qval.th	Adjusted p-value below which to consider features differential.
logFC.th	Log2-fold-change above/below which to consider a feature depleted/enriched.
enrichment_qval	Adjusted p-value below which to consider a gene set as significantly enriched in differential features.
doBatchCorr	Logical indicating if batch correction using fastMNN should be run.
batch_sels	If doBatchCorr is TRUE, a named list containing the samples in each batch.
control_samples_CNA	If running CopyNumber Analysis, a character vector of the sample names that are 'normal'.
genes_to_plot	A character vector containing genes of interest of which to plot the coverage.

Value

Creates a ChromSCape-readable directory and saved objects, as well as a multi-tabbed HTML report resuming the analysis.

Examples

## Not run: 
generate_analysis("/path/to/data/", "Analysis_1")

## End(Not run)

vallotlab/ChromSCape documentation built on Oct. 15, 2023, 1:47 p.m.