コード例 #1
0
ファイル: express.py プロジェクト: Tmacme/bcbio-nextgen
def run(data):
    """Quantitaive isoforms expression by eXpress"""
    name = dd.get_sample_name(data)
    in_bam = dd.get_transcriptome_bam(data)
    config = data['config']
    if not in_bam:
        logger.info("Transcriptome-mapped BAM file not found, skipping eXpress.")
        return data
    gtf_fasta = gtf.gtf_to_fasta(dd.get_gtf_file(data), dd.get_ref_file(data))
    out_dir = os.path.join(dd.get_work_dir(data), "express", name)
    out_file = os.path.join(out_dir, name + ".xprs")
    express = config_utils.get_program("express", data['config'])
    strand = _set_stranded_flag(in_bam, data)
    if not file_exists(out_file):
        with tx_tmpdir(data) as tmp_dir:
            with file_transaction(out_dir) as tx_out_dir:
                bam_file = _prepare_bam_file(in_bam, tmp_dir, config)
                cmd = ("{express} --no-update-check -o {tx_out_dir} {strand} {gtf_fasta} {bam_file}")
                do.run(cmd.format(**locals()), "Run express on %s." % in_bam, {})
            shutil.move(os.path.join(out_dir, "results.xprs"), out_file)
    eff_count_file = _get_column(out_file, out_file.replace(".xprs", "_eff.counts"), 7)
    tpm_file = _get_column(out_file, out_file.replace("xprs", "tpm"), 14)
    fpkm_file = _get_column(out_file, out_file.replace("xprs", "fpkm"), 10)
    data = dd.set_express_counts(data, eff_count_file)
    data = dd.set_express_tpm(data, tpm_file)
    data = dd.set_express_fpkm(data, fpkm_file)
    return data
コード例 #2
0
ファイル: express.py プロジェクト: hshujia/bcbio-nextgen
def run(data):
    """Quantitaive isoforms expression by eXpress"""
    name = dd.get_sample_name(data)
    in_bam = dd.get_transcriptome_bam(data)
    if not in_bam:
        logger.info(
            "Transcriptome-mapped BAM file not found, skipping eXpress.")
        return data
    gtf_fasta = gtf.gtf_to_fasta(dd.get_gtf_file(data), dd.get_ref_file(data))
    out_dir = os.path.join(dd.get_work_dir(data), "express", name)
    out_file = os.path.join(out_dir, name + ".xprs")
    express = config_utils.get_program("express", data['config'])
    strand = _set_stranded_flag(in_bam, data)
    if not file_exists(out_file):
        with file_transaction(out_dir) as tx_out_dir:
            cmd = (
                "{express} --no-update-check -o {tx_out_dir} {strand} {gtf_fasta} {in_bam}"
            )
            do.run(cmd.format(**locals()), "Run express on %s." % in_bam, {})
        shutil.move(os.path.join(out_dir, "results.xprs"), out_file)
    eff_count_file = _get_column(out_file,
                                 out_file.replace(".xprs", "_eff.counts"), 7)
    tpm_file = _get_column(out_file, out_file.replace("xprs", "tpm"), 14)
    fpkm_file = _get_column(out_file, out_file.replace("xprs", "fpkm"), 10)
    data = dd.set_express_counts(data, eff_count_file)
    data = dd.set_express_tpm(data, tpm_file)
    data = dd.set_express_fpkm(data, fpkm_file)
    return data
コード例 #3
0
def combine_files(samples):
    """
    after quantitation, combine the counts/FPKM/TPM/etc into a single table with
    all samples
    """
    gtf_file = dd.get_gtf_file(samples[0][0], None)
    dexseq_gff = dd.get_dexseq_gff(samples[0][0])

    # combine featureCount files
    count_files = filter_missing([dd.get_count_file(x[0]) for x in samples])
    combined = count.combine_count_files(count_files, ext=".counts")
    annotated = count.annotate_combined_count_file(combined, gtf_file)

    # combine eXpress files
    express_counts_combined = combine_express(samples, combined)

    # combine Cufflinks files
    fpkm_combined_file = os.path.splitext(combined)[0] + ".fpkm"
    fpkm_files = filter_missing([dd.get_fpkm(x[0]) for x in samples])
    if fpkm_files:
        fpkm_combined = count.combine_count_files(fpkm_files, fpkm_combined_file)
    else:
        fpkm_combined = None
    fpkm_isoform_combined_file = os.path.splitext(combined)[0] + ".isoform.fpkm"
    isoform_files = filter_missing([dd.get_fpkm_isoform(x[0]) for x in samples])
    if isoform_files:
        fpkm_isoform_combined = count.combine_count_files(isoform_files,
                                                          fpkm_isoform_combined_file,
                                                          ".isoform.fpkm")
    else:
        fpkm_isoform_combined = None
    # combine DEXseq files
    dexseq_combined_file = os.path.splitext(combined)[0] + ".dexseq"
    to_combine_dexseq = filter_missing([dd.get_dexseq_counts(data[0]) for data in samples])
    if to_combine_dexseq:
        dexseq_combined = count.combine_count_files(to_combine_dexseq,
                                                    dexseq_combined_file, ".dexseq")
        dexseq.create_dexseq_annotation(dexseq_gff, dexseq_combined)
    else:
        dexseq_combined = None
    samples = spikein.combine_spikein(samples)
    updated_samples = []
    for data in dd.sample_data_iterator(samples):
        data = dd.set_combined_counts(data, combined)
        if annotated:
            data = dd.set_annotated_combined_counts(data, annotated)
        if fpkm_combined:
            data = dd.set_combined_fpkm(data, fpkm_combined)
        if fpkm_isoform_combined:
            data = dd.set_combined_fpkm_isoform(data, fpkm_isoform_combined)
        if express_counts_combined:
            data = dd.set_express_counts(data, express_counts_combined['counts'])
            data = dd.set_express_tpm(data, express_counts_combined['tpm'])
            data = dd.set_express_fpkm(data, express_counts_combined['fpkm'])
            data = dd.set_isoform_to_gene(data, express_counts_combined['isoform_to_gene'])
        if dexseq_combined:
            data = dd.set_dexseq_counts(data, dexseq_combined_file)
        updated_samples.append([data])
    return updated_samples
コード例 #4
0
def combine_files(samples):
    """
    after quantitation, combine the counts/FPKM/TPM/etc into a single table with
    all samples
    """
    gtf_file = dd.get_gtf_file(samples[0][0], None)
    dexseq_gff = dd.get_dexseq_gff(samples[0][0])

    # combine featureCount files
    count_files = filter_missing([dd.get_count_file(x[0]) for x in samples])
    combined = count.combine_count_files(count_files, ext=".counts")
    annotated = count.annotate_combined_count_file(combined, gtf_file)

    # combine eXpress files
    express_counts_combined = combine_express(samples, combined)

    # combine Cufflinks files
    fpkm_combined_file = os.path.splitext(combined)[0] + ".fpkm"
    fpkm_files = filter_missing([dd.get_fpkm(x[0]) for x in samples])
    if fpkm_files:
        fpkm_combined = count.combine_count_files(fpkm_files, fpkm_combined_file)
    else:
        fpkm_combined = None
    fpkm_isoform_combined_file = os.path.splitext(combined)[0] + ".isoform.fpkm"
    isoform_files = filter_missing([dd.get_fpkm_isoform(x[0]) for x in samples])
    if isoform_files:
        fpkm_isoform_combined = count.combine_count_files(isoform_files,
                                                          fpkm_isoform_combined_file,
                                                          ".isoform.fpkm")
    else:
        fpkm_isoform_combined = None
    # combine DEXseq files
    dexseq_combined_file = os.path.splitext(combined)[0] + ".dexseq"
    to_combine_dexseq = filter_missing([dd.get_dexseq_counts(data[0]) for data in samples])
    if to_combine_dexseq:
        dexseq_combined = count.combine_count_files(to_combine_dexseq,
                                                    dexseq_combined_file, ".dexseq")
        dexseq.create_dexseq_annotation(dexseq_gff, dexseq_combined)
    else:
        dexseq_combined = None
    samples = spikein.combine_spikein(samples)
    updated_samples = []
    for data in dd.sample_data_iterator(samples):
        data = dd.set_combined_counts(data, combined)
        if annotated:
            data = dd.set_annotated_combined_counts(data, annotated)
        if fpkm_combined:
            data = dd.set_combined_fpkm(data, fpkm_combined)
        if fpkm_isoform_combined:
            data = dd.set_combined_fpkm_isoform(data, fpkm_isoform_combined)
        if express_counts_combined:
            data = dd.set_express_counts(data, express_counts_combined['counts'])
            data = dd.set_express_tpm(data, express_counts_combined['tpm'])
            data = dd.set_express_fpkm(data, express_counts_combined['fpkm'])
            data = dd.set_isoform_to_gene(data, express_counts_combined['isoform_to_gene'])
        if dexseq_combined:
            data = dd.set_dexseq_counts(data, dexseq_combined_file)
        updated_samples.append([data])
    return updated_samples
コード例 #5
0
ファイル: rnaseq.py プロジェクト: kern3020/bcbio-nextgen
def estimate_expression(samples, run_parallel):
    samples = run_parallel("generate_transcript_counts", samples)
    count_files = filter_missing([dd.get_count_file(x[0]) for x in samples])
    combined = count.combine_count_files(count_files)
    gtf_file = dd.get_gtf_file(samples[0][0], None)
    annotated = count.annotate_combined_count_file(combined, gtf_file)

    samples = run_parallel("run_express", samples)
    express_counts_combined = combine_express(samples, combined)

    samples = run_parallel("run_cufflinks", samples)
    #gene
    fpkm_combined_file = os.path.splitext(combined)[0] + ".fpkm"
    fpkm_files = filter_missing([dd.get_fpkm(x[0]) for x in samples])
    fpkm_combined = count.combine_count_files(fpkm_files, fpkm_combined_file)
    #isoform
    fpkm_isoform_combined_file = os.path.splitext(combined)[0] + ".isoform.fpkm"
    isoform_files = filter_missing([dd.get_fpkm_isoform(x[0]) for x in samples])
    fpkm_isoform_combined = count.combine_count_files(isoform_files,
                                                      fpkm_isoform_combined_file,
                                                      ".isoform.fpkm")
    dexseq_combined_file = os.path.splitext(combined)[0] + ".dexseq"
    to_combine_dexseq = filter_missing([dd.get_dexseq_counts(data[0]) for data in samples])
    if to_combine_dexseq:
        dexseq_combined = count.combine_count_files(to_combine_dexseq,
                                                    dexseq_combined_file, ".dexseq")
    else:
        dexseq_combined = None

    updated_samples = []
    for data in dd.sample_data_iterator(samples):
        data = dd.set_combined_counts(data, combined)
        if annotated:
            data = dd.set_annotated_combined_counts(data, annotated)
        if fpkm_combined:
            data = dd.set_combined_fpkm(data, fpkm_combined)
        if fpkm_isoform_combined:
            data = dd.set_combined_fpkm_isoform(data, fpkm_combined)
        if express_counts_combined:
            data = dd.set_express_counts(data, express_counts_combined['counts'])
            data = dd.set_express_tpm(data, express_counts_combined['tpm'])
            data = dd.set_express_fpkm(data, express_counts_combined['fpkm'])
        if dexseq_combined:
            data = dd.set_dexseq_counts(data, dexseq_combined_file)
        updated_samples.append([data])
    return updated_samples
コード例 #6
0
def combine_files(samples):
    """
    after quantitation, combine the counts/FPKM/TPM/etc into a single table with
    all samples
    """
    data = samples[0][0]
    # prefer the supplied transcriptome gtf file
    gtf_file = dd.get_transcriptome_gtf(data, None)
    if not gtf_file:
        gtf_file = dd.get_gtf_file(data, None)
    dexseq_gff = dd.get_dexseq_gff(data)

    # combine featureCount files
    count_files = filter_missing([dd.get_count_file(x[0]) for x in samples])
    combined = count.combine_count_files(count_files, ext=".counts")
    annotated = count.annotate_combined_count_file(combined, gtf_file)

    # add tx2gene file
    tx2gene_file = os.path.join(dd.get_work_dir(data), "annotation",
                                "tx2gene.csv")
    if gtf_file:
        tx2gene_file = sailfish.create_combined_tx2gene(data)

    # combine eXpress files
    express_counts_combined = combine_express(samples, combined)

    # combine Cufflinks files
    fpkm_files = filter_missing([dd.get_fpkm(x[0]) for x in samples])
    if fpkm_files:
        fpkm_combined_file = os.path.splitext(combined)[0] + ".fpkm"
        fpkm_combined = count.combine_count_files(fpkm_files,
                                                  fpkm_combined_file)
    else:
        fpkm_combined = None
    isoform_files = filter_missing(
        [dd.get_fpkm_isoform(x[0]) for x in samples])
    if isoform_files:
        fpkm_isoform_combined_file = os.path.splitext(
            combined)[0] + ".isoform.fpkm"
        fpkm_isoform_combined = count.combine_count_files(
            isoform_files, fpkm_isoform_combined_file, ".isoform.fpkm")
    else:
        fpkm_isoform_combined = None
    # combine DEXseq files
    to_combine_dexseq = filter_missing(
        [dd.get_dexseq_counts(data[0]) for data in samples])
    if to_combine_dexseq:
        dexseq_combined_file = os.path.splitext(combined)[0] + ".dexseq"
        dexseq_combined = count.combine_count_files(to_combine_dexseq,
                                                    dexseq_combined_file,
                                                    ".dexseq")
        dexseq.create_dexseq_annotation(dexseq_gff, dexseq_combined)
    else:
        dexseq_combined = None
    samples = spikein.combine_spikein(samples)
    updated_samples = []
    for data in dd.sample_data_iterator(samples):
        if combined:
            data = dd.set_combined_counts(data, combined)
        if annotated:
            data = dd.set_annotated_combined_counts(data, annotated)
        if fpkm_combined:
            data = dd.set_combined_fpkm(data, fpkm_combined)
        if fpkm_isoform_combined:
            data = dd.set_combined_fpkm_isoform(data, fpkm_isoform_combined)
        if express_counts_combined:
            data = dd.set_express_counts(data,
                                         express_counts_combined['counts'])
            data = dd.set_express_tpm(data, express_counts_combined['tpm'])
            data = dd.set_express_fpkm(data, express_counts_combined['fpkm'])
            data = dd.set_isoform_to_gene(
                data, express_counts_combined['isoform_to_gene'])
        if dexseq_combined:
            data = dd.set_dexseq_counts(data, dexseq_combined_file)
        if gtf_file:
            data = dd.set_tx2gene(data, tx2gene_file)
        updated_samples.append([data])
    return updated_samples
コード例 #7
0
ファイル: rnaseq.py プロジェクト: chapmanb/bcbio-nextgen
def combine_files(samples):
    """
    after quantitation, combine the counts/FPKM/TPM/etc into a single table with
    all samples
    """
    data = samples[0][0]
    # prefer the supplied transcriptome gtf file
    gtf_file = dd.get_transcriptome_gtf(data, None)
    if not gtf_file:
        gtf_file = dd.get_gtf_file(data, None)
    dexseq_gff = dd.get_dexseq_gff(data)

    # combine featureCount files
    count_files = filter_missing([dd.get_count_file(x[0]) for x in samples])
    combined = count.combine_count_files(count_files, ext=".counts")
    annotated = count.annotate_combined_count_file(combined, gtf_file)

    # add tx2gene file
    tx2gene_file = os.path.join(dd.get_work_dir(data), "annotation", "tx2gene.csv")
    if gtf_file:
        tx2gene_file = sailfish.create_combined_tx2gene(data)

    # combine eXpress files
    express_counts_combined = combine_express(samples, combined)

    # combine Cufflinks files
    fpkm_files = filter_missing([dd.get_fpkm(x[0]) for x in samples])
    if fpkm_files and combined:
        fpkm_combined_file = os.path.splitext(combined)[0] + ".fpkm"
        fpkm_combined = count.combine_count_files(fpkm_files, fpkm_combined_file)
    else:
        fpkm_combined = None
    isoform_files = filter_missing([dd.get_fpkm_isoform(x[0]) for x in samples])
    if isoform_files and combined:
        fpkm_isoform_combined_file = os.path.splitext(combined)[0] + ".isoform.fpkm"
        fpkm_isoform_combined = count.combine_count_files(isoform_files,
                                                          fpkm_isoform_combined_file,
                                                          ".isoform.fpkm")
    else:
        fpkm_isoform_combined = None
    # combine DEXseq files
    to_combine_dexseq = filter_missing([dd.get_dexseq_counts(data[0]) for data
                                        in samples])
    if to_combine_dexseq and combined:
        dexseq_combined_file = os.path.splitext(combined)[0] + ".dexseq"
        dexseq_combined = count.combine_count_files(to_combine_dexseq,
                                                    dexseq_combined_file, ".dexseq")
        if dexseq_combined:
            dexseq.create_dexseq_annotation(dexseq_gff, dexseq_combined)
    else:
        dexseq_combined = None
    samples = spikein.combine_spikein(samples)
    updated_samples = []
    for data in dd.sample_data_iterator(samples):
        if combined:
            data = dd.set_combined_counts(data, combined)
        if annotated:
            data = dd.set_annotated_combined_counts(data, annotated)
        if fpkm_combined:
            data = dd.set_combined_fpkm(data, fpkm_combined)
        if fpkm_isoform_combined:
            data = dd.set_combined_fpkm_isoform(data, fpkm_isoform_combined)
        if express_counts_combined:
            data = dd.set_express_counts(data, express_counts_combined['counts'])
            data = dd.set_express_tpm(data, express_counts_combined['tpm'])
            data = dd.set_express_fpkm(data, express_counts_combined['fpkm'])
            data = dd.set_isoform_to_gene(data, express_counts_combined['isoform_to_gene'])
        if dexseq_combined:
            data = dd.set_dexseq_counts(data, dexseq_combined_file)
        if gtf_file:
            data = dd.set_tx2gene(data, tx2gene_file)
        updated_samples.append([data])
    return updated_samples