example_results: Example results

In sankaranlab/SCAVENGE: Identifying genetic trait/phenotype relevant cell type/state at single cell resolution

Example results

Description

Example results for SCAVENGE. trait_import <- example_data(name="mono.PP001.bed") SE_pbmc5k <- example_data(name="pbmc5k_SE.rda") genome <- BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19 SE_pbmc5k <- addGCBias(object = SE_pbmc5k, genome = genome) SE_pbmc5k_bg <- getBackgroundPeaks(object = SE_pbmc5k, niterations = 200) SE_pbmc5k_DEV <- computeWeightedDeviations(object = SE_pbmc5k, weights = trait_import, background_peaks = SE_pbmc5k_bg) z_score_mat <- data.frame(colData(SE_pbmc5k), z_score=c(t(assays(SE_pbmc5k_DEV)[["z"]])) ) scale_factor <- cal_scalefactor(z_score = z_score_mat$z_score, percent_cut = 0.01) seed_idx <- seedindex(z_score_mat$z_score, 0.05) peak_by_cell_mat <- SummarizedExperiment::assay(SE_pbmc5k) tfidf_mat <- tfidf(bmat=peak_by_cell_mat) lsi_mat <- do_lsi(mat = tfidf_mat, dims = 30) mutualknn30 <- getmutualknn(lsimat = lsi_mat, num_k = 30) np_score <- randomWalk_sparse(intM=mutualknn30, queryCells = rownames(mutualknn30)[seed_idx], gamma=0.05) omit_idx <- np_score==0 mutualknn30 <- mutualknn30[!omit_idx, !omit_idx] np_score <- np_score[!omit_idx] TRS <- capOutlierQuantile(np_score, 0.95) |> max_min_scale() TRS <- TRS * scale_factor mono_mat <- data.frame(z_score_mat[!omit_idx, ], seed_idx[!omit_idx], np_score, TRS) #### Merge into one list #### example_results <- list(seed_idx=seed_idx, # tfidf_mat=tfidf_mat, # lsi_mat=lsi_mat, mutualknn30=mutualknn30, mono_mat=mono_mat) usethis::use_data(example_results, overwrite = TRUE)

Usage

data("example_results")

Format

List

sankaranlab/SCAVENGE documentation built on March 2, 2023, 2:17 a.m.