Add renameMetaPrograms function for renaming MPs in GeneNMF results

[liuzhenyu-yyy]

This PR introduces a new utility function renameMetaPrograms for renaming meta-programs in GeneNMF analysis results. This function allow users to assign new names to existing meta-programs, and ensures all associated data structures are updated accordingly. Comprehensive documentation has also been added.

This is especially useful under 2 conditions:

• to rename and reorder MPs based on similar biological functions (eg. MP1/3/5 cell cycle, MP2/4/6 EMT -> MP1-3 cell cycle, MP4-6 EMT).
• to revise discontinuous MP names after filtering by dropMetaPrograms (eg. MP1/3/4/5 after filtering out MP2 -> MP1/2/3/4)

Core scripts are adapted from getMetaPrograms and tested on my local machine.

GeneNMF/R/main.R

Lines 268 to 302 in 355c466

	#Rename meta-programs after sorting them by metrics
	ord <- order(metaprograms.metrics$sampleCoverage,
	metaprograms.metrics$silhouette,
	decreasing = T)
	old.names <- names(markers.consensus)[ord]
	new.names <- paste0("MP",seq_along(ord))
	markers.consensus <- markers.consensus[ord]
	names(markers.consensus) <- new.names
	#Reorder metrics and composition tables
	metaprograms.metrics <- metaprograms.metrics[ord,,drop=F]
	rownames(metaprograms.metrics) <- new.names
	metaprograms.composition <- metaprograms.composition[ord,,drop=F]
	rownames(metaprograms.composition) <- new.names
	map.index <- seq_along(old.names)
	names(map.index) <- as.numeric(gsub("MetaProgram","",old.names))
	cl_members.new <- map.index[as.character(cl_members)]
	names(cl_members.new) <- names(cl_members)
	#weights of individual genes in each MP
	markers.consensus <- lapply(markers.consensus, function(m){m/sum(m)})
	markers.genes <- lapply(markers.consensus, names)
	output.object <- list()
	output.object[["metaprograms.genes"]] <- markers.genes
	output.object[["metaprograms.genes.weights"]] <- markers.consensus
	output.object[["metaprograms.metrics"]] <- metaprograms.metrics
	output.object[["metaprograms.composition"]] <- metaprograms.composition
	output.object[["programs.similarity"]] <- J
	output.object[["programs.tree"]] <- tree
	output.object[["programs.clusters"]] <- cl_members.new
	return(output.object)