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)
data("example_results")List