View source: R/activityScores.R
build_gene_activity_matrix | R Documentation |
This function calculates the initial Cicero gene activity matrix. After this
function, the activity matrix should be normalized with any comparison
matrices using the function normalize_gene_activities
.
build_gene_activity_matrix( input_cds, cicero_cons_info, site_weights = NULL, dist_thresh = 250000, coaccess_cutoff = 0.25 )
input_cds |
Binary sci-ATAC-seq input CDS. The input CDS must have a
column in the fData table called "gene" which is the gene name if the
site is a promoter, and |
cicero_cons_info |
Cicero connections table, generally the output of
|
site_weights |
NULL or an individual weight for each site in input_cds. |
dist_thresh |
The maximum distance in base pairs between pairs of sites to include in the gene activity calculation. |
coaccess_cutoff |
The minimum Cicero co-accessibility score that should be considered connected. |
Unnormalized gene activity matrix.
data("cicero_data") data("human.hg19.genome") sample_genome <- subset(human.hg19.genome, V1 == "chr18") sample_genome$V2[1] <- 100000 input_cds <- make_atac_cds(cicero_data, binarize = TRUE) input_cds <- detectGenes(input_cds) input_cds <- reduceDimension(input_cds, max_components = 2, num_dim=6, reduction_method = 'tSNE', norm_method = "none") tsne_coords <- t(reducedDimA(input_cds)) row.names(tsne_coords) <- row.names(pData(input_cds)) cicero_cds <- make_cicero_cds(input_cds, reduced_coordinates = tsne_coords) cons <- run_cicero(cicero_cds, sample_genome, sample_num=2) data(gene_annotation_sample) gene_annotation_sub <- gene_annotation_sample[,c(1:3, 8)] names(gene_annotation_sub)[4] <- "gene" input_cds <- annotate_cds_by_site(input_cds, gene_annotation_sub) num_genes <- pData(input_cds)$num_genes_expressed names(num_genes) <- row.names(pData(input_cds)) unnorm_ga <- build_gene_activity_matrix(input_cds, cons)
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