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None +chrX 170150000 170200000 1.0 None +chrX 170200000 170250000 1.0 None +chrX 170250000 170300000 1.0 None +chrX 170300000 170350000 1.0 None +chrX 170350000 170400000 1.0 None +chrX 170400000 170450000 1.0 None +chrX 170450000 170500000 1.0 None +chrX 170500000 170550000 1.0 None +chrX 170550000 170600000 1.0 None +chrX 170600000 170650000 1.0 None +chrX 170650000 170700000 1.0 None +chrX 170700000 170750000 1.0 None +chrX 170750000 170800000 1.0 None +chrX 170800000 170850000 1.0 None +chrX 170850000 170900000 1.0 None +chrX 170900000 170950000 1.0 None +chrX 170950000 171000000 1.0 None +chrX 171000000 171031299 1.0 None diff --git a/workflow/rules/common.smk b/workflow/rules/common.smk index f50ec3f..b4dcc26 100644 --- a/workflow/rules/common.smk +++ b/workflow/rules/common.smk @@ -298,11 +298,17 @@ class HandleInput: "strandphaser", ] - for sample in [ + l_to_process = [ e for e in os.listdir(thisdir) if e not in exclude and e.endswith(".zip") is False - ]: + ] + if config["samples_to_process"]: + l_to_process = [ + e for e in l_to_process if e in config["samples_to_process"] + ] + + for sample in l_to_process: # Create a list of files to process for each sample l_files_all = [ f @@ -313,12 +319,13 @@ class HandleInput: ) if f.endswith(ext) ] + # print(l_files_all) for f in l_files_all: if len(f.split("_")) == 4: assert ( len(f.split("_")) != 4 - ), "Your file name is using 4 times the '_' character, which is currently not supported by ashleys-qc, please rename your files" + ), "Your file name is using 3 times the '_' character, which is currently not supported by ashleys-qc, please rename your files" # print(l_files_all) # Dataframe creation @@ -528,7 +535,7 @@ def publishdir_fct(wildcards): Function to generate a list of files and directories for backup. """ - print(config) + # print(config) list_files_to_copy = [ e diff --git a/workflow/scripts/normalization/merge-blacklist.py b/workflow/scripts/normalization/merge-blacklist.py new file mode 100755 index 0000000..998c965 --- /dev/null +++ b/workflow/scripts/normalization/merge-blacklist.py @@ -0,0 +1,104 @@ +#!/usr/bin/env python +""" +Merge two blacklisted intervals if distance between them is below a given distance. +""" + +import sys +from argparse import ArgumentParser +import pandas as pd + + +def main(): + parser = ArgumentParser(prog="merge-blacklist.py", description=__doc__) + parser.add_argument( + "--merge_distance", + default=500000, + type=int, + help="If the distance between two blacklisted intervals is below this threshold, they are merged.", + ) + parser.add_argument("--output", default=None, help="Output file name") + parser.add_argument( + "--whitelist", default=None, help="TSV file with intervals to be removed from the blacklist (columns: chrom, start, end)." + ) + parser.add_argument("--min_whitelist_interval_size", default=400000, type=int, help="Ignore whitelisted intervals below this size.") + + parser.add_argument("normalization", metavar="NORM", help="File (tsv) with normalization and blacklist data") + + args = parser.parse_args() + + print("Reading", args.normalization, file=sys.stderr) + norm_table = pd.read_csv(args.normalization, sep="\t") + + assert set(norm_table.columns) == set(["chrom", "start", "end", "scalar", "class"]) + + whitelist = None + if args.whitelist is not None: + whitelist = pd.read_csv(args.whitelist, sep="\t") + assert set(whitelist.columns) == set(["chrom", "start", "end"]) + print("Read", len(whitelist), "whitelisted intervals from", args.whitelist, file=sys.stderr) + whitelist = whitelist[whitelist.end - whitelist.start >= args.min_whitelist_interval_size] + print(" -->", len(whitelist), "remained after removing intervals below", args.min_whitelist_interval_size, "bp", file=sys.stderr) + + additional_blacklist = 0 + prev_blacklist_index = None + prev_blacklist_chrom = None + prev_blacklist_end = None + for i in range(len(norm_table)): + row = norm_table.iloc[i] + # print('Processing row', i, ' -->', tuple(row), file=sys.stderr) + # is row blacklisted? + if row["class"] == "None": + # print(' --> is black', file=sys.stderr) + if (prev_blacklist_chrom == row["chrom"]) and (row["start"] - prev_blacklist_end <= args.merge_distance): + # print(' --> black listing', prev_blacklist_index+1, 'to', i, file=sys.stderr) + for j in range(prev_blacklist_index + 1, i): + norm_table.loc[[j], "class"] = "None" + row_j = norm_table.iloc[j] + additional_blacklist += row_j.end - row_j.start + prev_blacklist_index = i + prev_blacklist_chrom = row["chrom"] + prev_blacklist_end = row["end"] + + print("Additionally blacklisted", additional_blacklist, "bp of sequence", file=sys.stderr) + + additional_whitelist = 0 + if whitelist is not None: + for i in range(len(norm_table)): + row = norm_table.iloc[i] + if row["class"] == "None": + if len(whitelist[(whitelist.chrom == row.chrom) & (row.start < whitelist.end) & (whitelist.start < row.end)]) > 0: + norm_table.loc[[i], "class"] = "good" + additional_whitelist += row.end - row.start + + + print("White listing: Removed", additional_whitelist, "bp of sequence for blacklist", file=sys.stderr) + + norm_table.to_csv(args.output, index=False, sep="\t") + + ## Identify "complex" intervals + # segments = calls.groupby(by=['chrom','start','end']).sv_call_name.agg({'is_complex':partial(is_complex, ignore_haplotypes=args.ignore_haplotypes, min_cell_count=args.min_cell_count)}).reset_index().sort_values(['chrom','start','end']) + + ## merge complex segments if closer than args.merge_distance + # complex_segments = pd.DataFrame(columns=['chrom','start','end']) + # cur_chrom, cur_start, cur_end = None, None, None + # for chrom, start, end in segments[segments.is_complex][['chrom','start','end']].values: + # if cur_chrom is None: + # cur_chrom, cur_start, cur_end = chrom, start, end + # elif (cur_chrom == chrom) and (start - cur_end < args.merge_distance): + # cur_end = end + # else: + # complex_segments = complex_segments.append({'chrom': cur_chrom, 'start': cur_start,'end': cur_end}, ignore_index=True) + # cur_chrom, cur_start, cur_end = chrom, start, end + # if cur_chrom is not None: + # complex_segments = complex_segments.append({'chrom': cur_chrom, 'start': cur_start,'end': cur_end}, ignore_index=True) + + # print(complex_segments, file=sys.stderr) + # total_complex = sum(complex_segments.end - complex_segments.start) + + # print('Total amount of complex sequence: {}Mbp'.format(total_complex/1000000), file=sys.stderr) + # complex_segments[['chrom','start','end']].to_csv(sys.stdout, index=False, sep='\t') + ##print(complex_segments, file=sys.stderr) + + +if __name__ == "__main__": + main() diff --git a/workflow/scripts/normalization/normalize.R b/workflow/scripts/normalization/normalize.R new file mode 100644 index 0000000..8225823 --- /dev/null +++ b/workflow/scripts/normalization/normalize.R @@ -0,0 +1,160 @@ +suppressMessages(library(dplyr)) +suppressMessages(library(data.table)) +suppressMessages(library(assertthat)) + + +args <- commandArgs(trailingOnly = T) +if (length(args) != 4) { + print("Usage: Rscript scale.R ") + print("") + print(" Normalize Strand-seq read counts. Divide the counts of all bins") + print(" by a scaling factor (norm$scalar) and further black-list bins") + print(" if requested in the normalizatio file (norm$class).") + options(show.error.messages = F) + stop() +} + +# Read counts +message(" * Reading counts from ", args[1]) +counts <- fread(paste("zcat", args[1])) +assert_that( + is.data.table(counts), + "chrom" %in% colnames(counts), + "start" %in% colnames(counts), + "end" %in% colnames(counts), + "class" %in% colnames(counts), + "sample" %in% colnames(counts), + "cell" %in% colnames(counts), + "w" %in% colnames(counts), + "c" %in% colnames(counts) +) %>% invisible() +setkey(counts, chrom, start, end) + +# Check that all cells have the same bins +bins <- unique(counts[, .(chrom, start, end)]) +counts[, + assert_that(all(.SD == bins), msg = "Not the same bins in all cells"), + by = .(sample, cell), + .SDcols = c("chrom", "start", "end") +] %>% invisible() + + +# remove bad cells +bad_cells <- counts[class == "None", .N, by = .(sample, cell)][N == nrow(bins)] +if (nrow(bad_cells) > 0) { + message(" * Removing ", nrow(bad_cells), " cells because thery were black-listed.") + counts <- counts[!bad_cells, on = c("sample", "cell")] +} + +# Check that the "None" bins are all the same across cells +none_bins <- unique(counts[!bad_cells, on = c("sample", "cell")][class == "None", .(chrom, start, end)]) +if (nrow(none_bins) > 0) { + counts[!bad_cells, on = c("sample", "cell")][class == "None", + assert_that(all(.SD == none_bins, msg = "None bins are not the same in all cells (excl. bad cells)")), + by = .(sample, cell), + .SDcols = c("chrom", "start", "end") + ] %>% invisible() +} + + +# Read normalization factors +message(" * Reading norm file from ", args[2]) +norm <- fread(args[2]) +assert_that( + is.data.table(norm), + "chrom" %in% colnames(norm), + "start" %in% colnames(norm), + "end" %in% colnames(norm), + "scalar" %in% colnames(norm) +) %>% invisible() +if ("class" %in% colnames(norm)) { + norm <- norm[, .(chrom, start, end, scalar, norm_class = class)] +} else { + norm <- norm[, .(chrom, start, end, scalar, norm_class = "good")] +} +setkey(norm, chrom, start, end) + +# Check normalization type from snakemake +if (args[4] == "False") { + norm$scalar <- 1 +} + +# Set particular values of the norm_class to "None": +norm[scalar < 0.01, norm_class := "None"] + + +# annotate counts with scaling factor +counts <- merge(counts, + norm, + by = c("chrom", "start", "end"), + all.x = T +) + +if (any(is.na(counts$scalar))) { + message( + " * Assign scalars: Could not match ", + unique(counts[, .(chrom, start, end, scalar)])[is.na(scalar), .N], + " bins (out of ", + unique(counts[, .(chrom, start, end)])[, .N], + ") -> set those to 1" + ) +} + +# Fill gaps in the norm file +counts[is.na(scalar), `:=`(scalar = 1, norm_class = "good")] + +# Black-listing bins +test <- counts[!bad_cells, on = c("sample", "cell")][cell == unique(cell)[1]] +test <- test[, .( + count_None = sum(class == "None"), + norm_None = sum(norm_class == "None"), + final_None = sum(class == "None" | norm_class == "None") +)] +message(" * ", test$count_None, " bins were already black-listed; ", test$norm_None, " are blacklisted via the normalization, leading to a total of ", test$final_None) +counts[norm_class == "None", class := "None"] + + + +# Apply normalization factor +counts[, `:=`(c = as.numeric(c), w = as.numeric(w))] + +counts[class != "None", `:=`( + c = c * scalar, + w = w * scalar +)] + +# # Check normalization type from snakemake +# if (args[4] == "True") { +# counts[class != "None", `:=`( +# c = c * scalar, +# w = w * scalar +# )] +# } else { +# scalar <- 1 + +# } + + + +counts[class == "None", `:=`(c = 0.0, w = 0.0)] + +message( + " * Applying normalization: min = ", + round(min(counts[class != "None", scalar]), 3), + ", max = ", + round(max(counts[class != "None", scalar]), 3), + ", median = ", + median(unique(counts[, .(chrom, start, end, class, scalar)][class != "None", scalar])) +) + + +# Remove column +counts[, norm_class := NULL] +counts[, scalar := NULL] + + +# Write down table +message(" * Write data to ", args[3]) +gz1 <- gzfile(args[3], "w") +write.table(counts, gz1, sep = "\t", quote = F, col.names = T, row.names = F) +close(gz1) diff --git a/workflow/scripts/plotting/qc.R b/workflow/scripts/plotting/qc.R index 34559fc..3f4e031 100644 --- a/workflow/scripts/plotting/qc.R +++ b/workflow/scripts/plotting/qc.R @@ -156,9 +156,9 @@ if (add_overview_plot == T) { d_mv <- d[class != "None", .(mean = mean(w + c), var = var(w + c)), by = .(sample, cell)] print(d_mv) - if(nrow(d_mv) > 0) { + if (nrow(d_mv) > 0) { d_p <- d_mv[, .(p = sum(mean * mean) / sum(mean * var)), by = sample] - + ov_meanvar <- ggplot(d_mv) + geom_point(aes(mean, var), alpha = 0.4) + facet_wrap(~sample, nrow = 1) + @@ -168,7 +168,7 @@ if (add_overview_plot == T) { ggtitle("Mean variance relationship of reads per bin") + xlab("Mean") + ylab("Variance") - + # Arranging overview plot content <- ggdraw() + draw_plot(ov_binsizes, x = 0, y = .66, width = .5, height = .33) + @@ -177,7 +177,7 @@ if (add_overview_plot == T) { draw_plot(ov_meanvar, x = 0, y = 0, width = min(n_samples / 3, 1), height = .33) } else { print("d_mv is empty. Skipping the overview mean/variance plot...") - + # Arranging overview plot without ov_meanvar content <- ggdraw() + draw_plot(ov_binsizes, x = 0, y = .66, width = .5, height = .33) + diff --git a/workflow/scripts/utils/populated_counts_for_qc_plot copy.py b/workflow/scripts/utils/populated_counts_for_qc_plot copy.py new file mode 100644 index 0000000..86ca165 --- /dev/null +++ b/workflow/scripts/utils/populated_counts_for_qc_plot copy.py @@ -0,0 +1,101 @@ +import pandas as pd + +# Read 200kb bins file + +binbed = pd.read_csv( + snakemake.input.bin_bed[0], + # "../../../../mosaicatcher-update/workflow/data/bin_200kb_all.bed", + sep="\t", + # names=["chrom", "start", "end", "scalar", "class"], +) +binbed["bin_id"] = ( + binbed["chrom"].astype(str) + + "_" + + binbed["start"].astype(str) + + "_" + + binbed["end"].astype(str) +) + +binbed = binbed.drop(columns=["scalar"]) + + +# Turn chrom into categorical +max_chrom = 23 if snakemake.config["reference"] != "mm10" else 20 +binbed["chrom"] = pd.Categorical( + binbed["chrom"], + categories=["chr{}".format(e) for e in range(1, 23)] + ["chrX", "chrY"], + ordered=True, +) + +# Sort & filter out chrY #TMP / can be changed +binbed = binbed.sort_values(by=["chrom", "start", "end"]).reset_index(drop=True) +binbed["w"], binbed["c"] = 0, 0 + +# Read SV file +# df = pd.read_csv("../../../../mosaicatcher-update/.tests/data_CHR17/RPE-BM510/counts/RPE-BM510.txt.raw.gz", sep="\t") + +# sep = "," if "/multistep_normalisation/" in snakemake.input.counts else "\t" +sep = "\t" +df = pd.read_csv(snakemake.input.counts, sep=sep, compression="gzip") +df["bin_id"] = ( + df["chrom"].astype(str) + + "_" + + df["start"].astype(str) + + "_" + + df["end"].astype(str) +) +df["w"] = df["w"].round(0).astype(int) +df["c"] = df["c"].round(0).astype(int) +df["class"] = df["class"].astype(str) +if sep == ",": + df["tot_count"] = df["tot_count"].round(0).astype(int) + + +# Update 'class' in df using norm_df +df = df.merge(binbed[["bin_id", "class"]], on="bin_id", how="left") +df["class"] = df.apply( + lambda x: "None" if x["class_y"] != "good" else x["class_x"], axis=1 +) + +# set w & c to 0 for None class if None in class_y +df.loc[df["class_y"] == "None", "w"] = 0 +df.loc[df["class_y"] == "None", "c"] = 0 + + +df = df.drop(columns=["class_x", "class_y"]) +# df.loc[df["class"] == "None", "w"] = 0 +# df.loc[df["class"] == "None", "c"] = 0 + +## Populate counts df for each cell in order to have all bins represented +l = list() + +# Loop over cells +for cell in df.cell.unique().tolist()[1:6]: + # Outer join to retrieve both real count values from specified chromosome and empty bins + tmp_df = pd.concat( + [ + binbed.loc[ + ~binbed["bin_id"].isin( + df.loc[df["cell"] == cell].bin_id.values.tolist() + ) + ], + df.loc[df["cell"] == cell], + ] + ) + + # Filla cell & sample columns + tmp_df["cell"] = cell + tmp_df["sample"] = df.loc[df["cell"] == cell, "sample"].values.tolist()[0] + l.append(tmp_df) + +# Concat list of DF and output +populated_df = pd.concat(l).sort_values(by=["cell", "chrom", "start"]) +print(populated_df) +populated_df["start"] = populated_df["start"].astype(int) +populated_df["end"] = populated_df["end"].astype(int) +populated_df["w"] = populated_df["w"].astype(int) +populated_df["c"] = populated_df["c"].astype(int) +# populated_df.to_csv("test.txt.gz", compression="gzip", sep="\t", index=False) +populated_df.to_csv( + snakemake.output.populated_counts, compression="gzip", sep="\t", index=False +) diff --git a/workflow/scripts/utils/populated_counts_for_qc_plot.py b/workflow/scripts/utils/populated_counts_for_qc_plot.py index c597d02..60b6d7e 100644 --- a/workflow/scripts/utils/populated_counts_for_qc_plot.py +++ b/workflow/scripts/utils/populated_counts_for_qc_plot.py @@ -8,7 +8,13 @@ sep="\t", names=["chrom", "start", "end", "bin_id"], ) -binbed["ID"] = binbed["chrom"].astype(str) + "_" + binbed["start"].astype(str) + "_" + binbed["end"].astype(str) +binbed["ID"] = ( + binbed["chrom"].astype(str) + + "_" + + binbed["start"].astype(str) + + "_" + + binbed["end"].astype(str) +) # Turn chrom into categorical binbed["chrom"] = pd.Categorical( @@ -21,18 +27,53 @@ binbed = binbed.sort_values(by=["chrom", "start", "end"]).reset_index(drop=True) binbed["w"], binbed["c"], binbed["class"] = 0, 0, None + +# Determine if using mm10 data and load normalization file if so +reference = snakemake.config["reference"] +if reference == "mm10": + print(reference) + norm_file = "workflow/data/normalization/mm10/HGSVC.200000.txt" + norm_df = pd.read_csv(norm_file, sep="\t") + norm_df["ID"] = ( + norm_df["chrom"].astype(str) + + "_" + + norm_df["start"].astype(str) + + "_" + + norm_df["end"].astype(str) + ) + norm_df["class"] = norm_df["class"].fillna("None") + norm_df["class"] = norm_df["class"].astype(str) +else: + norm_df = pd.DataFrame() + # Read SV file # df = pd.read_csv("../../../../mosaicatcher-update/.tests/data_CHR17/RPE-BM510/counts/RPE-BM510.txt.raw.gz", sep="\t") # sep = "," if "/multistep_normalisation/" in snakemake.input.counts else "\t" sep = "\t" df = pd.read_csv(snakemake.input.counts, sep=sep, compression="gzip") -df["ID"] = df["chrom"].astype(str) + "_" + df["start"].astype(str) + "_" + df["end"].astype(str) +df["ID"] = ( + df["chrom"].astype(str) + + "_" + + df["start"].astype(str) + + "_" + + df["end"].astype(str) +) df["w"] = df["w"].round(0).astype(int) df["c"] = df["c"].round(0).astype(int) if sep == ",": df["tot_count"] = df["tot_count"].round(0).astype(int) +# Adjust counts based on normalization data if applicable +if not norm_df.empty: + df = df.merge(norm_df[["ID", "class"]], on="ID", how="left") + df.loc[df["class_y"] == "None", ["w", "c"]] = 0 + df.drop(columns=["class_y"], inplace=True) + df = df.rename(columns={"class_x": "class"}) + df["class"] = df["class"].fillna("None") + print(df) + + ## Populate counts df for each cell in order to have all bins represented l = list() @@ -41,7 +82,9 @@ # Outer join to retrieve both real count values from specified chromosome and empty bins tmp_df = pd.concat( [ - binbed.loc[~binbed["ID"].isin(df.loc[df["cell"] == cell].ID.values.tolist())], + binbed.loc[ + ~binbed["ID"].isin(df.loc[df["cell"] == cell].ID.values.tolist()) + ], df.loc[df["cell"] == cell], ] ) @@ -54,4 +97,6 @@ # Concat list of DF and output populated_df = pd.concat(l).sort_values(by=["cell", "chrom", "start"]) # populated_df.to_csv("test.txt.gz", compression="gzip", sep="\t", index=False) -populated_df.to_csv(snakemake.output.populated_counts, compression="gzip", sep="\t", index=False) +populated_df.to_csv( + snakemake.output.populated_counts, compression="gzip", sep="\t", index=False +) diff --git a/workflow/scripts/utils/update_counts.awk b/workflow/scripts/utils/update_counts.awk new file mode 100644 index 0000000..2e48012 --- /dev/null +++ b/workflow/scripts/utils/update_counts.awk @@ -0,0 +1,26 @@ +#!/usr/bin/awk -f + +BEGIN {OFS="\t"} + +NR==FNR { + norm[$1,$2,$3]=$5; + next +} + +FNR==1 { + if (NF < 10) { + print $0, "bin_id"; + add_bin_id = 1; + } else { + print $0; + } +} + +FNR>1 { + $8=(($1,$2,$3) in norm && norm[$1,$2,$3]!="good") ? "None" : $8; + if (add_bin_id) { + print $0, $1"_"$2"_"$3; + } else { + print $0; + } +}