Split into files ; define modes & associated conda envs ; run on a single...

Split into files ; define modes & associated conda envs ; run on a single chromosome by keeping full initial BAM  ; functionnal until strandphaser

.gitignore

@@ -13,3 +13,4 @@ sv_probabilities/
 *.png
 *.pdf
 *.svg
+workflow/.snakemake
\ No newline at end of file

Snakefile_lite.smk

@@ -73,6 +73,8 @@ exclude_list = ['BM510x3PE20490']
 SAMPLES, BAM_PER_SAMPLE, CELL_PER_SAMPLE, ALLBAMS_PER_SAMPLE, df_config_files = handle_input_data(thisdir=config["input_bam_location"], exclude_list=exclude_list)
 
 print(df_config_files)
+print(df_config_files['Full_path'][0])
+
 tqdm.pandas(desc="Checking if BAM SM tags correspond to folder names")
 df_config_files["Full_path"].progress_apply(check_bam_header, )
 
@@ -82,20 +84,21 @@ for s in SAMPLES:
 
 
 
-# METHODS = [
-#     "simpleCalls_llr4_poppriorsTRUE_haplotagsTRUE_gtcutoff0_regfactor6_filterFALSE",
-#     "simpleCalls_llr4_poppriorsTRUE_haplotagsFALSE_gtcutoff0.05_regfactor6_filterTRUE",
-# ]
+METHODS = [
+    "simpleCalls_llr4_poppriorsTRUE_haplotagsTRUE_gtcutoff0_regfactor6_filterFALSE",
+    "simpleCalls_llr4_poppriorsTRUE_haplotagsFALSE_gtcutoff0.05_regfactor6_filterTRUE",
+]
 
 # # FIXME : move to yaml/json settings or to something else
-# BPDENS = [
-#     "selected_j{}_s{}_scedist{}".format(joint, single, scedist) for joint in [0.1] for single in [0.5] for scedist in [20]
-# ]
+BPDENS = [
+    "selected_j{}_s{}_scedist{}".format(joint, single, scedist) for joint in [0.1] for single in [0.5] for scedist in [20]
+]
 
 
 rule all:
     input:
-        expand(config["output_location"] + "counts/{sample}/{window}.txt.gz", sample=SAMPLES, window=[100000]),
+        # expand(config["output_location"] + "counts/{sample}/{window}.txt.gz", sample=SAMPLES, window=[100000]),
+        expand(config["output_location"] + "counts/{sample}.txt.gz", sample=SAMPLES),
 
 
 
@@ -110,36 +113,32 @@ rule all:
 # Read counting                                                                #
 ################################################################################
 
-# CHECKME : exclude file rule useful ?
-rule generate_exclude_file_1:
+rule generate_exclude_file_for_mosaic_count:
+    """
+    rule fct: 
     input:
-        bam = expand(config["input_bam_location"] +  "{sample}/selected/{bam}.bam", sample = SAMPLES[0], bam = BAM_PER_SAMPLE[SAMPLES[0]][0])
     output:
-        config["output_location"] + "log/exclude_file.temp"
-    log:
-        config["output_location"] + "log/generate_exclude_file_1.log"
-    params:
-        samtools = config["samtools"]
-    shell:
-        """
-        {params.samtools} view -H {input.bam} | awk "/^@SQ/" > {output} 
-        """
-
-rule generate_exclude_file_2:
+    """
     input:
-        config["output_location"] + "log/exclude_file.temp"
+        bam = df_config_files['Full_path'][0]
     output:
         config["output_location"] + "log/exclude_file"
     params:
         chroms = config["chromosomes"]
     run:
-        with open(input[0]) as f:
-            with open(output[0],"w") as out:
-                for line in f:
-                    contig = line.strip().split()[1]
-                    contig = contig[3:]
-                    # if contig not in params.chroms:
-                        # print(contig, file = out)
+        # READ BAM FILE HEADER OF FIRST BAM IN THE PANDAS DF
+        h = pysam.view("-H", input[0])
+        h = [e.split("\t") for e in h.split("\n") if "@SQ" in e]
+
+        # CONVERT TO PANDAS DF
+        df_h = pd.DataFrame(h, columns=["TAG", "Contig", "LN"])
+
+        # PROCESS CONTIGS
+        output_h = pd.DataFrame(df_h["Contig"].str.replace("SN:", ""))
+        output_h = output_h.loc[~output_h["Contig"].isin(params.chroms)]
+        
+        # EXPORT
+        output_h["Contig"].to_csv(output[0], index=False, sep='\t', header=False)
 
                         
 # CHECKME : same as above for input ???
@@ -154,15 +153,15 @@ rule mosaic_count:
     input:
         bam = lambda wc: expand(config["input_bam_location"] + wc.sample +  "/selected/{bam}.bam", bam = BAM_PER_SAMPLE[wc.sample]) if wc.sample in BAM_PER_SAMPLE else "FOOBAR",
         bai = lambda wc: expand(config["input_bam_location"] + wc.sample +  "/selected/{bam}.bam.bai", bam = BAM_PER_SAMPLE[wc.sample]) if wc.sample in BAM_PER_SAMPLE else "FOOBAR",
-
-        # excl = config["output_location"] + "log/exclude_file"
+        excl = config["output_location"] + "log/exclude_file"
     output:
-        counts = config["output_location"] + "counts/{sample}/{window}.txt.fixme.gz",
-        info   = config["output_location"] + "counts/{sample}/{window}.info"
+        counts = config["output_location"] + "counts/{sample}.txt.gz",
+        info   = config["output_location"] + "counts/{sample}.info"
     log:
-        config["output_location"] + "log/{sample}/mosaic_count.{window}.log"
+        config["output_location"] + "log/{sample}/mosaic_count.log"
     params:
-        mc_command = config["mosaicatcher"]
+        mc_command = config["mosaicatcher"],
+        window = config["window"]
     shell:
         """
         {params.mc_command} count \
@@ -170,24 +169,12 @@ rule mosaic_count:
             --do-not-blacklist-hmm \
             -o {output.counts} \
             -i {output.info} \
-            -w {wildcards.window} \
+            -w {params.window} \
+            -x {input.excl} \
             {input.bam} 
         > {log} 2>&1
         """
 
-rule tmp_filter_mosaic_count_by_chr:
-    input:
-        config["output_location"] + "counts/{sample}/{window}.txt.fixme.gz"
-    output:
-        config["output_location"] + "counts/{sample}/{window}.txt.gz"
-    run:
-        df = pd.read_csv(input[0], compression='gzip', sep='\t')
-        df = df.loc[df['chrom'].isin(wildcards.chromosomes)]
-        df.to_csv(output[0], compression='gzip', sep='\t', index=False)
-
-
-
-
 
 
 ################################################################################

afac/lite_mosaic_count.py

+import pandas as pd
+import sys
+
+df_counts = pd.read_csv(sys.argv[1], compression="gzip", sep="\t")
+sample_cells = df_counts["cell"].unique().tolist()[:2]
+df_counts_lite = df_counts.loc[df_counts["cell"].isin(sample_cells)]
+df_counts_lite.to_csv(sys.argv[3], sep="\t", compression="gzip", index=False)
+
+info_header = "".join([line for line in open(sys.argv[2], "r").readlines() if line[0] == "#"])
+with open(sys.argv[4], "w") as w:
+    w.write(info_header)
+
+df_info = pd.read_csv(sys.argv[2], sep="\t", skiprows=13)
+df_info_lite = df_info.loc[df_info["cell"].isin(sample_cells)]
+df_info_lite.to_csv(sys.argv[4], sep="\t", mode="a", index=False)

afac/test_pysam.py

+import pysam
+import sys, os
+import pandas as pd
+
+bam_file_path = "/g/korbel2/weber/MosaiCatcher_files/bam_KG_full/RPE1-WT/all/RPE1WTPE20401.sort.mdup.bam"
+# bam_file_path = sys.argv[1]
+
+
+# def check_bam_header(bam_file_path):
+#     # GET BAM FILE HEADER WITH PYSAM
+#     h = pysam.view("-H", bam_file_path)
+#     h = [e.split("\t") for e in h.split("\n")]
+#     sm_tag_list = list(set([sub_e.replace("SM:", "") for e in h for sub_e in e if "SM:" in sub_e]))
+
+#     # FOLDER NAME BASED ON PATH
+#     folder_name = bam_file_path.split("/")[-3]
+
+#     # CHECKS
+#     assert len(sm_tag_list) == 1, "Two different SM tags in the header of BAM file {}".format(bam_file_path)
+#     assert sm_tag_list[0] == folder_name, "SM tag in BAM file {} do not correspond to folder name {}".format(bam_file_path, folder_name)
+
+
+# check_bam_header(bam_file_path=bam_file_path)
+
+
+
+print(df_h)

afac/tmp_input_smk.py

+import math
+from collections import defaultdict
+
+configfile: "config/Snake.config_embl.yaml"
+import pandas as pd
+import os, sys
+from pprint import pprint
+import pysam
+from tqdm import tqdm
+
+# TODO I/O : Function to define inputs ; simplify list/dict system // SOLVED
+# TODO Use remote file system to download example files
+
+
+def handle_input_data(thisdir, exclude_list=list):
+    """ """
+    # Parsing folder and retrieve only files with .bam extension
+    data = [(r, file.replace(".bam", "")) for r, d, f in os.walk(thisdir) for file in f if ".bam" in file and ".bai" not in file]
+
+    # Building pandas df based on folder structure
+    df = pd.DataFrame(data, columns=["Folder", "File"])
+
+    # Defining cols
+    df["all/selected"] = df["Folder"].apply(lambda r: r.split("/")[-1])
+    df["Sample"] = df["Folder"].apply(lambda r: r.split("/")[-2])
+    df["Cell"] = df["File"].apply(lambda r: r.split(".")[0])
+    df["Full_path"] = df["Folder"] + "/" + df["File"] + ".bam"
+
+    # Filtering based on exclude list defined
+    df_config_files = df.loc[~df["Cell"].isin(exclude_list)]
+
+    # Export dicts
+    SAMPLES = sorted(df_config_files.Sample.unique().tolist())
+    BAM_PER_SAMPLE = df_config_files.loc[df_config_files["all/selected"] == "selected"].groupby("Sample")["File"].apply(list).to_dict()
+    CELL_PER_SAMPLE = df_config_files.loc[df_config_files["all/selected"] == "selected"].groupby("Sample")["Cell"].apply(list).to_dict()
+    ALLBAMS_PER_SAMPLE = df_config_files.loc[df_config_files["all/selected"] == "all"].groupby("Sample")["File"].apply(list).to_dict()
+
+    return SAMPLES, BAM_PER_SAMPLE, CELL_PER_SAMPLE, ALLBAMS_PER_SAMPLE, df_config_files
+
+
+def check_bam_header(bam_file_path):
+    """ """
+
+    # Get BAM file header with pysam
+    h = pysam.view("-H", bam_file_path)
+    h = [e.split("\t") for e in h.split("\n")]
+    sm_tag_list = list(set([sub_e.replace("SM:", "") for e in h for sub_e in e if "SM:" in sub_e]))
+
+    # Folder name based on path
+    folder_name = bam_file_path.split("/")[-3]
+
+    # Assertions
+    assert len(sm_tag_list) == 1, "Two different SM tags in the header of BAM file {}".format(bam_file_path)
+    assert sm_tag_list[0] == folder_name, 'Folder name "{}" must correspond to SM tag in BAM file "{}"'.format(folder_name, bam_file_path)
+
+
+# FIXME : tmp solution to remove bad cells => need to fix this with combination of ASHLEYS ?
+# TODO : other solution by giving in config file, CLI input ?
+
+exclude_list = ["BM510x3PE20490"]
+
+SAMPLES, BAM_PER_SAMPLE, CELL_PER_SAMPLE, ALLBAMS_PER_SAMPLE, df_config_files = handle_input_data(
+    thisdir="/g/korbel2/weber/MosaiCatcher_files/bam_KG_full/", exclude_list=exclude_list
+)
+
+# print(df_config_files)
+# print(df_config_files["Full_path"][0])
+
+tqdm.pandas(desc="Checking if BAM SM tags correspond to folder names")
+df_config_files["Full_path"].progress_apply(
+    check_bam_header,
+)
+
+all_dict = df_config_files.loc[df_config_files["all/selected"] == "all"].groupby("Sample")["Cell"].nunique().to_dict()
+selected_dict = df_config_files.loc[df_config_files["all/selected"] == "selected"].groupby("Sample")["Cell"].nunique().to_dict()
+print("Detected {} samples:".format(df_config_files.Sample.nunique()))
+[print("  {}:\t{} cells\t {} selected cells".format(s, all_dict[s], selected_dict[s])) for s in SAMPLES]
+

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+"1"
+#else
+"0"
+#endif
+"cxx_aggregate_default_initializers\n"
+"CXX_FEATURE:"
+#if (__GNUC__ * 100 + __GNUC_MINOR__) >= 409 && __cplusplus > 201103L
+"1"
+#else
+"0"
+#endif
+"cxx_attribute_deprecated\n"
+"CXX_FEATURE:"
+#if (__GNUC__ * 100 + __GNUC_MINOR__) >= 409 && __cplusplus > 201103L
+"1"
+#else
+"0"
+#endif
+"cxx_binary_literals\n"
+"CXX_FEATURE:"
+#if (__GNUC__ * 100 + __GNUC_MINOR__) >= 409 && __cplusplus > 201103L
+"1"
+#else
+"0"
+#endif
+"cxx_contextual_conversions\n"
+"CXX_FEATURE:"
+#if (__GNUC__ * 100 + __GNUC_MINOR__) >= 409 && __cplusplus > 201103L
+"1"
+#else
+"0"
+#endif
+"cxx_decltype_auto\n"
+"CXX_FEATURE:"
+#if (__GNUC__ * 100 + __GNUC_MINOR__) >= 409 && __cplusplus > 201103L
+"1"
+#else
+"0"
+#endif
+"cxx_digit_separators\n"
+"CXX_FEATURE:"
+#if (__GNUC__ * 100 + __GNUC_MINOR__) >= 409 && __cplusplus > 201103L
+"1"
+#else
+"0"
+#endif
+"cxx_generic_lambdas\n"
+"CXX_FEATURE:"
+#if (__GNUC__ * 100 + __GNUC_MINOR__) >= 409 && __cplusplus > 201103L
+"1"
+#else
+"0"
+#endif
+"cxx_lambda_init_captures\n"
+"CXX_FEATURE:"
+#if (__GNUC__ * 100 + __GNUC_MINOR__) >= 500 && __cplusplus >= 201402L
+"1"
+#else
+"0"
+#endif
+"cxx_relaxed_constexpr\n"
+"CXX_FEATURE:"
+#if (__GNUC__ * 100 + __GNUC_MINOR__) >= 409 && __cplusplus > 201103L
+"1"
+#else
+"0"
+#endif
+"cxx_return_type_deduction\n"
+"CXX_FEATURE:"
+#if (__GNUC__ * 100 + __GNUC_MINOR__) >= 500 && __cplusplus >= 201402L
+"1"
+#else
+"0"
+#endif
+"cxx_variable_templates\n"
+
+};
+
+int main(int argc, char** argv) { (void)argv; return features[argc]; }