semibin in subgroups

workflow/rules/cobinning.smk

@@ -29,12 +29,14 @@ rule filter_contigs:
         " -Xmx{resources.java_mem}G 2> {log} "
 
 
+def get_samples_of_bingroup(wildcards):
 
+    return sampleTable.query(f'BinGroup=="{wildcards.bingroup}"').index.tolist()
 
 def get_filtered_contigs_of_bingroup(wildcards):
 
-
-    samples_of_group = sampleTable.query(f'BinGroup=="{wildcards.bingroup}"').index.tolist()
+    samples_of_group = get_samples_of_bingroup(wildcards)
+
 
     if len(samples_of_group) <= 5:
         raise ValueError(f"Bin group {wildcards.bingroup} has {len(samples_of_group)} less than 5 samples."

workflow/rules/semibin.smk

@@ -2,77 +2,90 @@
 rule semibin_generate_data_multi:
     input:
         fasta=rules.combine_contigs.output,
-        bams=expand(rules.sort_bam.output, sample=SAMPLES),
+        bams=lambda wc: expand(rules.sort_bam.output, sample= get_samples_of_bingroup(wc)),
     output:
-        expand(
-            "Cobinning/SemiBin/samples/{sample}/{files}",
-            sample=SAMPLES,
-            files=["data.csv", "data_split.csv"],
-        ),
+        directory("Intermediate/cobinning/{bingroup}/semibin/data_multi")
+        # expand(
+        #     "Cobinning/SemiBin/samples/{sample}/{files}",
+        #     sample=SAMPLES,
+        #     files=["data.csv", "data_split.csv"],
+        # ),
     conda:
         "../envs/semibin.yaml"
     threads: config["threads"]
     resources:
         mem=config["mem"],
         time=config["runtime"]["default"],
     log:
-        "logs/semibin/generate_data_multi.log",
+        "logs/semibin/{bingroup}/generate_data_multi.log",
     benchmark:
-        "logs/benchmarks/semibin/generate_data_multi.tsv"
+        "logs/benchmarks/semibin/{bingroup}/generate_data_multi.tsv"
     params:
-        output_dir="Cobinning/SemiBin",
+        # output_dir="Cobinning/SemiBin",
         separator=config["cobinning_separator"],
     shell:
         "SemiBin generate_sequence_features_multi"
         " --input-fasta {input.fasta} "
         " --input-bam {input.bams} "
-        " --output {params.output_dir} "
+        " --output {output} "
         " --threads {threads} "
         " --separator {params.separator} "
         " 2> {log}"
 
 
 rule semibin_train:
     input:
-        "{sample}/{sample}_contigs.fasta",
-        fasta=rules.filter_contigs.output,
-        bams=expand(rules.sort_bam.output, sample=SAMPLES),
-        data="Cobinning/SemiBin/samples/{sample}/data.csv",
-        data_split="Cobinning/SemiBin/samples/{sample}/data_split.csv",
+        flag = "{sample}/{sample}_contigs.fasta",
+        fasta_sample = rules.filter_contigs.output,
+        bams= rules.semibin_generate_data_multi.input.bams,
+        data_folder= rules.semibin_generate_data_multi.output[0],
     output:
-        "Cobinning/SemiBin/{sample}/model.h5",
+        "Intermediate/cobinning/{bingroup}/semibin/models/{sample}/model.h5",
     conda:
         "../envs/semibin.yaml"
     threads: config["threads"]
     resources:
         mem=config["mem"],
         time=config["runtime"]["default"],
     log:
-        "logs/semibin/train/{sample}.log",
+        "logs/semibin/{bingroup}/train/{sample}.log",
     benchmark:
-        "logs/benchmarks/semibin/train/{sample}.tsv"
+        "logs/benchmarks/semibin/{bingroup}/train/{sample}.tsv"
     params:
         output_dir=lambda wc, output: os.path.dirname(output[0]),
+        data = lambda wc, input: Path(input.data_folder)/"samples"/wc.sample/"data.csv",
+        data_split = lambda wc, input: Path(input.data_folder)/"samples"/wc.sample/"data_split.csv",
         extra=config["semibin_train_extra"],
     shell:
         "SemiBin train_self "
         " --output {params.output_dir} "
         " --threads {threads} "
-        " --data {input.data} "
-        " --data-split {input.data_split} "
+        " --data {params.data} "
+        " --data-split {params.data_split} "
         " {params.extra} "
         " 2> {log}"
 
 
+def semibin_input(wildcards):
+
+    bingroup_of_sample = sampleTable.loc[wildcards.sample, "bingroup"]
+    samples_of_bingroup = sampleTable.query(f'BinGroup=="{bingroup_of_sample}"').index.tolist()
+
+
+    return dict(
+        flag= "{sample}/{sample}_contigs.fasta",
+        fasta = rules.filter_contigs.output,
+        bams =lambda wc: expand(rules.sort_bam.output, sample= samples_of_bingroup),
+        data_folder = rules.semibin_generate_data_multi.output[0].format(bingroup=bingroup_of_sample),
+        model = rules.semibin_train.output[0].format(bingroup=bingroup_of_sample, sample=wildcards.sample),
+    )
+
 rule run_semibin:
     input:
-        "{sample}/{sample}_contigs.fasta",
-        fasta=rules.filter_contigs.output,
-        bams=expand(rules.sort_bam.output, sample=SAMPLES),
-        data="Cobinning/SemiBin/samples/{sample}/data.csv",
-        model=rules.semibin_train.output[0],
+        unpack(semibin_input),
     output:
-        directory("Cobinning/SemiBin/{sample}/output_recluster_bins/"),
+        # contains no info to bingroup
+        directory("Intermediate/cobinning/semibin_output/{sample}/output_recluster_bins/"),
     conda:
         "../envs/semibin.yaml"
     threads: config["threads"]
@@ -84,15 +97,16 @@ rule run_semibin:
     benchmark:
         "logs/benchmarks/semibin/bin/{sample}.tsv"
     params:
-        output_dir="Cobinning/SemiBin/{sample}/",
+        output_dir= lambda wc, output: os.path.dirname(output[0])
+        data = lambda wc, input: Path(input.data_folder)/"samples"/wc.sample/"data.csv",
         min_bin_kbs=int(config["cobining_min_bin_size"] / 1000),
         extra=config["semibin_options"],
     shell:
         "SemiBin bin "
         " --input-fasta {input.fasta} "
         " --output {params.output_dir} "
         " --threads {threads} "
-        " --data {input.data} "
+        " --data {params.data} "
         " --model {input.model} "
         " --minfasta-kbs {params.min_bin_kbs}"
         " {params.extra} "
@@ -120,5 +134,4 @@ rule parse_semibin_output:
 
 rule semibin:
     input:
-        expand("Cobinning/SemiBin/{sample}/output_recluster_bins/", sample=SAMPLES),
         expand("{sample}/binning/SemiBin/cluster_attribution.tsv", sample=SAMPLES),