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This should be in mro/common for general use
Mito_Trace/Analysis/mtscATAC/2020_11_18_Croker/PBMC_P/_mrosource
Line 582 in ed20573
# # # Copyright (c) 2019 10x Genomics, Inc. All rights reserved. # filetype json; filetype bam; # # @include "_sort_and_mark_dups_stages.mro" # # # Copyright (c) 2019 10x Genomics, Inc. All rights reserved. # filetype bam; filetype bam.bai; filetype tsv.gz; filetype tsv.gz.tbi; filetype json; filetype csv; # # @include "_peak_caller_stages.mro" # # # Copyright (c) 2019 10x Genomics, Inc. All rights reserved. # filetype bedgraph; filetype pickle; filetype tsv.gz; filetype tsv.gz.tbi; filetype bed; filetype json; # # @include "_basic_sc_atac_counter_stages.mro" # # # Copyright (c) 2019 10x Genomics, Inc. All rights reserved. # filetype tsv.gz; filetype tsv.gz.tbi; filetype csv; filetype json; filetype bed; filetype pickle; filetype h5; # # @include "_produce_cell_barcodes_stages.mro" # # # Copyright (c) 2019 10x Genomics, Inc. All rights reserved. # filetype tsv.gz; filetype tsv.gz.tbi; filetype csv; filetype json; filetype bed; filetype pickle; filetype h5; filetype npy.gz; # # @include "_sc_atac_metric_collector_stages.mro" # # # Copyright (c) 2019 10x Genomics, Inc. All rights reserved. # filetype tsv.gz; filetype tsv.gz.tbi; filetype bed; filetype bam; filetype csv; filetype json; filetype h5; filetype txt; filetype pickle; # # @include "_peak_annotator_stages.mro" # # # Copyright (c) 2019 10x Genomics, Inc. All rights reserved. # filetype bed; filetype tsv; filetype h5; filetype gz; filetype pickle; # # @include "_sc_atac_analyzer_stages.mro" # # # Copyright (c) 2019 10x Genomics, Inc. All rights reserved. # filetype tsv; filetype h5; filetype pickle; filetype gz; filetype bed; filetype csv; # # @include "_sc_atac_reporter_stages.mro" # # # Copyright (c) 2019 10x Genomics, Inc. All rights reserved. # filetype json; filetype html; filetype csv; filetype h5; filetype bam; # # @include "_atac_cloupe_stages.mro" # # # Copyright (c) 2019 10x Genomics, Inc. All rights reserved. # filetype cloupe; filetype csv; filetype json; filetype h5; filetype bed; filetype tsv.gz.tbi; # # @include "_preflight_stages.mro" # # # Copyright (c) 2019 10x Genomics, Inc. All rights reserved. # filetype csv; filetype bed; filetype tsv.gz; filetype tsv.gz.tbi; # # @include "_aligner_stages.mro" # # SETUP_CHUNKS chunks up the input fastq data into sets of matched R1, R2, SI, and BC fastq files. # input_mode specifies how FASTQs were generated. There are two modes: # # 1. "BCL_PROCESSOR" # # FASTQs produced by the 10X BCL_PROCESSOR pipeline. This mode assumes the FASTQ files obey the internal # naming conventions and the reads have been interleaved into RA FASTQ files. # # 2. "ILMN_BCL2FASTQ" # # FASTQs produced directly by Illumina BCL2FASTQ v1.8.4. For this mode, BCL2FASTQ must be configured to emit the # index2 read, rather than using it for dual-index demultiplexing: # # configureBclToFastq.pl --no-eamss --use-bases-mask=Y100,I8,Y14,Y100 --input-dir=<basecalls_dir> \ # --output-dir=<output_dir> --sample-sheet=<sample_sheet.csv> # # The sample sheet must be formatted as per the BCL2FASTQ documentation (10 column csv), and must contain a row for # each sample index used. The sequencer must have been run in dual index mode, with the second index read (used to # read the 10X barcode) emitted as the R2 output file. The --use-bases-mask argument should be set to the read # length used. stage SETUP_CHUNKS( in string sample_id "id of the sample", in map[] sample_def "list of dictionary specifying input data", in string input_mode "configuration of the input fastqs", in map downsample "map specifies either subsample_rate (float) or gigabases (int)", out map[] chunks "map has barcode, barcode_reverse_complement, sample_index, read1, read2, gem_group, and read_group fields", out string[] read_groups "list of strings representing read groups", out json downsample_info "info about downsampling result", src py "stages/processing/setup_chunks", ) # Trims adapter sequences from reads and massages fastq output into a fixed format (interleaved R1 file, etc.) stage TRIM_READS( in map[] chunks, in string barcode_whitelist, in int max_read_num, in map trim_def, in map adapters, out map[] chunks, out json bc_counts, out json lot_info, out json read_counts, src py "stages/processing/trim_reads", ) split ( in map chunk, ) using ( volatile = strict, ) # Aligns the reads to the input reference, producing chunked bam files stage ALIGN_READS( in map[] chunks, in string aligner, in string aligner_method, in string reference_path, in string read_group_sample, in int num_threads, out bam[], src py "stages/processing/align_reads", ) split ( in map chunk, ) using ( # N.B. No index files are generated for the bam volatile = strict, ) # # @include "_aligner.mro" # # Takes input fastqs and chunks them, trims them, and aligns the trimmed reads to a reference pipeline _ALIGNER( in string sample_id, in string fastq_mode "configuration of the input fastqs", in map[] sample_def, in string reference_path "this is the reference_path", in string barcode_whitelist "name of barcode whitelist file", in map trim_def, in map adapters, in string read_group_sample "sample header for BAM file", in map downsample, out bam[] align, out map[] chunks, out json bc_counts, out json lot_info "gelbead lot detected", out json read_counts "total # of read pairs before and after adapter trimming", out json downsample_info "info on downsampling", ) { call SETUP_CHUNKS( sample_id = self.sample_id, input_mode = self.fastq_mode, sample_def = self.sample_def, downsample = self.downsample, ) using ( volatile = true, ) call TRIM_READS( chunks = SETUP_CHUNKS.chunks, max_read_num = 5000000, trim_def = self.trim_def, adapters = self.adapters, barcode_whitelist = self.barcode_whitelist, ) using ( volatile = true, ) call ALIGN_READS( chunks = TRIM_READS.chunks, aligner = "bwa", aligner_method = "MEM", reference_path = self.reference_path, read_group_sample = self.read_group_sample, num_threads = 4, ) using ( volatile = true, ) return ( align = ALIGN_READS, chunks = TRIM_READS.chunks, bc_counts = TRIM_READS.bc_counts, lot_info = TRIM_READS.lot_info, read_counts = TRIM_READS.read_counts, downsample_info = SETUP_CHUNKS.downsample_info, ) } # # @include "_sort_and_mark_dups_stages.mro" # # Attaches raw and corrected barcode sequences to the aligned reads stage ATTACH_BCS( in string barcode_whitelist, in bam[] align, in map[] chunks, in bool paired_end, in bool exclude_non_bc_reads, in float bc_confidence_threshold, in json bc_counts, out bam[] output, out int perfect_read_count, src py "stages/processing/attach_bcs", ) split ( in bam align_chunk, in map chunk, ) using ( # N.B. No index files are generated for the bam volatile = strict, ) stage SORT_READS_BY_POS( in bam[] input, out bam tagsorted_bam, src py "stages/processing/sort_reads_by_pos", ) split ( in bam chunk_input, ) using ( # N.B. No index files are generated for the bam volatile = strict, ) # Marks duplicates in the reads using barcodes and fragment alignments to detect PCR and optical/diffusion duplicates stage MARK_DUPLICATES( in bam input, in string reference_path, in json raw_barcode_counts, in string barcode_whitelist, out bam output, out bam.bai index, out csv singlecell_mapping, out tsv.gz fragments, out tsv.gz.tbi fragments_index, src py "stages/processing/mark_duplicates", ) split ( in map lane_map, in string chunk_start, in string chunk_end, in int chunk_num, ) using ( # N.B. BAM/BED index files are explicitly bound where used volatile = strict, ) # # @include "_sort_and_mark_dups.mro" # # Attaches barcodes to the aligned reads, marks duplicate reads, and produces a barcode-sorted and position-sorted # output BAM pipeline _SORT_AND_MARK_DUPS( in bam[] align, in map[] chunks, in string barcode_whitelist, in json bc_counts, in string reference_path, out bam possorted_bam "bam file sorted by position", out bam.bai possorted_bam_index "position-sorted bam index", out tsv.gz fragments, out tsv.gz.tbi fragments_index, out csv singlecell_mapping, out bam[] read_paired_bam, ) { call ATTACH_BCS( align = self.align, chunks = self.chunks, paired_end = true, barcode_whitelist = self.barcode_whitelist, exclude_non_bc_reads = false, bc_confidence_threshold = 0.975, bc_counts = self.bc_counts, ) using ( volatile = true, ) call SORT_READS_BY_POS( input = ATTACH_BCS.output, ) using ( volatile = true, ) call MARK_DUPLICATES( input = SORT_READS_BY_POS.tagsorted_bam, reference_path = self.reference_path, barcode_whitelist = self.barcode_whitelist, raw_barcode_counts = self.bc_counts, ) using ( volatile = true, ) return ( possorted_bam = MARK_DUPLICATES.output, possorted_bam_index = MARK_DUPLICATES.index, singlecell_mapping = MARK_DUPLICATES.singlecell_mapping, fragments = MARK_DUPLICATES.fragments, fragments_index = MARK_DUPLICATES.fragments_index, read_paired_bam = ATTACH_BCS.output, ) } # # @include "_peak_caller_stages.mro" # stage COUNT_CUT_SITES( in path reference_path, in tsv.gz fragments, in tsv.gz.tbi fragments_index, out bedgraph cut_sites, out pickle count_dict, src py "stages/processing/count_cut_sites", ) split ( in string contig, ) using ( # N.B. We explicitly bind the index file volatile = strict, ) stage DETECT_PEAKS( in bedgraph cut_sites, in path reference_path, in pickle count_dict, out bed peaks, out json peak_metrics, src py "stages/processing/detect_peaks", ) split ( in string contig, in float[] params, in float threshold, ) using ( mem_gb = 6, # N.B. We explicitly bind the index file volatile = strict, ) # # @include "_peak_caller.mro" # pipeline _PEAK_CALLER( in path reference_path, in tsv.gz fragments, in tsv.gz.tbi fragments_index, out bedgraph cut_sites, out bed peaks, out json peak_metrics, ) { call COUNT_CUT_SITES( reference_path = self.reference_path, fragments = self.fragments, fragments_index = self.fragments_index, ) call DETECT_PEAKS( reference_path = self.reference_path, cut_sites = COUNT_CUT_SITES.cut_sites, count_dict = COUNT_CUT_SITES.count_dict, ) return ( cut_sites = COUNT_CUT_SITES.cut_sites, peaks = DETECT_PEAKS.peaks, peak_metrics = DETECT_PEAKS.peak_metrics, ) } # # @include "_basic_sc_atac_counter_stages.mro" # stage GENERATE_PEAK_MATRIX( in string reference_path, in tsv.gz fragments, in bed peaks, out h5 raw_matrix, out path raw_matrix_mex, src py "stages/processing/generate_peak_matrix", ) split ( in file barcodes, ) using ( mem_gb = 4, # N.B. we don't explicitly need the fragment index volatile = strict, ) stage FILTER_PEAK_MATRIX( in h5 raw_matrix, in int num_analysis_bcs, in int random_seed, in csv cell_barcodes, out h5 filtered_matrix, out path filtered_matrix_mex, src py "stages/processing/filter_peak_matrix", ) split ( ) using ( volatile = strict, ) # # @include "_produce_cell_barcodes_stages.mro" # stage REMOVE_LOW_TARGETING_BARCODES( in bed peaks, in tsv.gz fragments, in tsv.gz.tbi fragments_index, in string reference_path, out json barcode_counts, out json low_targeting_barcodes, out json low_targeting_summary, out json fragment_lengths, out json covered_bases, src py "stages/processing/cell_calling/remove_low_targeting_barcodes", ) split ( in string contig, out pickle fragment_counts, out pickle targeted_counts, out int peak_coverage, ) using ( mem_gb = 4, volatile = strict, ) stage REMOVE_GEL_BEAD_DOUBLET_BARCODES( in tsv.gz fragments, in tsv.gz.tbi fragments_index, in string reference_path, in json barcode_counts, out json gel_bead_doublet_barcodes, out json gel_bead_doublet_summary, out csv connect_matrix, src py "stages/processing/cell_calling/remove_gel_bead_doublet_barcodes", ) split ( in string contig, in file valid_barcodes, ) using ( mem_gb = 4, volatile = strict, ) stage REMOVE_BARCODE_MULTIPLETS( in tsv.gz fragments, in tsv.gz.tbi fragments_index, in string reference_path, in string barcode_whitelist, in json barcode_counts, out json barcode_multiplets, out json barcode_multiplets_summary, src py "stages/processing/cell_calling/remove_barcode_multiplets", ) split ( in string contig, in string gem_group, out npy.gz part_a_linkage_matrix, out npy.gz part_b_linkage_matrix, ) using ( mem_gb = 4, volatile = strict, ) stage MERGE_EXCLUDED_BARCODES( in json[] barcode_exclusions, out json excluded_barcodes, src py "stages/processing/cell_calling/merge_excluded_barcodes", ) stage DETECT_CELL_BARCODES( in tsv.gz fragments, in tsv.gz.tbi fragments_index, in string barcode_whitelist, in json excluded_barcodes, in map force_cells, in string reference_path, in bed peaks, out csv cell_barcodes, out csv singlecell, out json cell_calling_summary, src py "stages/processing/cell_calling/detect_cell_barcodes", ) split ( in string contig, out pickle barcode_counts, out pickle targeted_counts, out int fragment_depth, ) using ( mem_gb = 4, volatile = strict, ) # TODO: This should be in mro/common for general use stage MERGE_SUMMARY_METRICS( in json[] summary_jsons, out json merged_summary, src py "stages/processing/cell_calling/merge_summary_metrics", ) # # @include "_produce_cell_barcodes.mro" # pipeline _PRODUCE_CELL_BARCODES( in bed peaks, in tsv.gz fragments, in tsv.gz.tbi fragments_index, in string reference_path, in string barcode_whitelist, in map force_cells, out csv cell_barcodes, out csv singlecell, out json cell_calling_summary, out json excluded_barcodes, out json fragment_lengths, out json covered_bases, ) { call REMOVE_LOW_TARGETING_BARCODES( fragments = self.fragments, fragments_index = self.fragments_index, peaks = self.peaks, reference_path = self.reference_path, ) call REMOVE_GEL_BEAD_DOUBLET_BARCODES( fragments = self.fragments, fragments_index = self.fragments_index, reference_path = self.reference_path, barcode_counts = REMOVE_LOW_TARGETING_BARCODES.barcode_counts, ) call REMOVE_BARCODE_MULTIPLETS( fragments = self.fragments, fragments_index = self.fragments_index, reference_path = self.reference_path, barcode_whitelist = self.barcode_whitelist, barcode_counts = REMOVE_LOW_TARGETING_BARCODES.barcode_counts, ) call MERGE_EXCLUDED_BARCODES( barcode_exclusions = [ REMOVE_BARCODE_MULTIPLETS.barcode_multiplets, REMOVE_GEL_BEAD_DOUBLET_BARCODES.gel_bead_doublet_barcodes, REMOVE_LOW_TARGETING_BARCODES.low_targeting_barcodes, ], ) call DETECT_CELL_BARCODES( fragments = self.fragments, fragments_index = self.fragments_index, barcode_whitelist = self.barcode_whitelist, force_cells = self.force_cells, excluded_barcodes = MERGE_EXCLUDED_BARCODES.excluded_barcodes, reference_path = self.reference_path, peaks = self.peaks, ) call MERGE_SUMMARY_METRICS as MERGE_CELL_METRICS( summary_jsons = [ REMOVE_LOW_TARGETING_BARCODES.low_targeting_summary, REMOVE_GEL_BEAD_DOUBLET_BARCODES.gel_bead_doublet_summary, REMOVE_BARCODE_MULTIPLETS.barcode_multiplets_summary, DETECT_CELL_BARCODES.cell_calling_summary, ], ) return ( cell_barcodes = DETECT_CELL_BARCODES.cell_barcodes, excluded_barcodes = MERGE_EXCLUDED_BARCODES.excluded_barcodes, singlecell = DETECT_CELL_BARCODES.singlecell, cell_calling_summary = MERGE_CELL_METRICS.merged_summary, fragment_lengths = REMOVE_LOW_TARGETING_BARCODES.fragment_lengths, covered_bases = REMOVE_LOW_TARGETING_BARCODES.covered_bases, ) } # # @include "_basic_sc_atac_counter.mro" # pipeline _BASIC_SC_ATAC_COUNTER( in string sample_id, in string fastq_mode "configuration of the input fastqs", in map[] sample_def, in string reference_path "this is the reference_path", in string barcode_whitelist "name of barcode whitelist file", in map trim_def, in map adapters, in map downsample, in map force_cells, out bam possorted_bam "bam file sorted by position", out bam.bai possorted_bam_index "position-sorted bam index", out tsv.gz fragments, out tsv.gz.tbi fragments_index, out json lot_info "gelbead lot detected", out json read_counts "total # of read pairs before and after adapter trimming", out json downsample_info "info on downsampling", out csv cell_barcodes, out json excluded_barcodes, out json cell_calling_summary, out bed peaks, out bedgraph cut_sites, out csv singlecell_mapping, out csv singlecell_cells, out json peak_metrics, out bam[] read_paired_bam, out h5 raw_peak_bc_matrix, out path raw_peak_bc_matrix_mex, out h5 filtered_peak_bc_matrix, out path filtered_peak_bc_matrix_mex, ) { call _ALIGNER( sample_id = self.sample_id, fastq_mode = self.fastq_mode, sample_def = self.sample_def, read_group_sample = self.sample_id, trim_def = self.trim_def, adapters = self.adapters, reference_path = self.reference_path, barcode_whitelist = self.barcode_whitelist, downsample = self.downsample, ) call _SORT_AND_MARK_DUPS( align = _ALIGNER.align, chunks = _ALIGNER.chunks, reference_path = self.reference_path, barcode_whitelist = self.barcode_whitelist, bc_counts = _ALIGNER.bc_counts, ) call _PEAK_CALLER( fragments = _SORT_AND_MARK_DUPS.fragments, fragments_index = _SORT_AND_MARK_DUPS.fragments_index, reference_path = self.reference_path, ) call _PRODUCE_CELL_BARCODES( fragments = _SORT_AND_MARK_DUPS.fragments, fragments_index = _SORT_AND_MARK_DUPS.fragments_index, peaks = _PEAK_CALLER.peaks, force_cells = self.force_cells, reference_path = self.reference_path, barcode_whitelist = self.barcode_whitelist, ) call GENERATE_PEAK_MATRIX( reference_path = self.reference_path, fragments = _SORT_AND_MARK_DUPS.fragments, peaks = _PEAK_CALLER.peaks, ) call FILTER_PEAK_MATRIX( num_analysis_bcs = null, cell_barcodes = _PRODUCE_CELL_BARCODES.cell_barcodes, raw_matrix = GENERATE_PEAK_MATRIX.raw_matrix, random_seed = null, ) return ( possorted_bam = _SORT_AND_MARK_DUPS.possorted_bam, possorted_bam_index = _SORT_AND_MARK_DUPS.possorted_bam_index, singlecell_mapping = _SORT_AND_MARK_DUPS.singlecell_mapping, singlecell_cells = _PRODUCE_CELL_BARCODES.singlecell, lot_info = _ALIGNER.lot_info, read_counts = _ALIGNER.read_counts, downsample_info = _ALIGNER.downsample_info, cell_barcodes = _PRODUCE_CELL_BARCODES.cell_barcodes, excluded_barcodes = _PRODUCE_CELL_BARCODES.excluded_barcodes, cell_calling_summary = _PRODUCE_CELL_BARCODES.cell_calling_summary, peak_metrics = _PEAK_CALLER.peak_metrics, cut_sites = _PEAK_CALLER.cut_sites, peaks = _PEAK_CALLER.peaks, fragments = _SORT_AND_MARK_DUPS.fragments, fragments_index = _SORT_AND_MARK_DUPS.fragments_index, read_paired_bam = _SORT_AND_MARK_DUPS.read_paired_bam, raw_peak_bc_matrix = GENERATE_PEAK_MATRIX.raw_matrix, raw_peak_bc_matrix_mex = GENERATE_PEAK_MATRIX.raw_matrix_mex, filtered_peak_bc_matrix = FILTER_PEAK_MATRIX.filtered_matrix, filtered_peak_bc_matrix_mex = FILTER_PEAK_MATRIX.filtered_matrix_mex, ) } # # @include "_sc_atac_metric_collector_stages.mro" # stage ESTIMATE_LIBRARY_COMPLEXITY( in json sequencing_summary, in tsv.gz fragments, in csv cell_barcodes, out json bulk_complexity, out json complexity_summary, out json singlecell_complexity, src py "stages/metrics/estimate_library_complexity", ) split ( in file barcodes, ) using ( mem_gb = 6, volatile = strict, ) stage GENERATE_SEQUENCING_METRICS( in bam[] input, out txt misc_sm, out json summary, src py "stages/metrics/generate_sequencing_metrics", ) split ( in bam chunk_bam, ) using ( volatile = strict, ) stage GENERATE_SINGLECELL_TARGETING( in tsv.gz fragments, in tsv.gz.tbi fragments_index, in bed peaks, in string reference_path, out csv singlecell, out json summary, out csv tss_relpos, out csv ctcf_relpos, src py "stages/metrics/generate_singlecell_targeting", ) split ( in string contig, out int read_count, out pickle target_counts_by_barcode, out pickle chunk_tss, out pickle chunk_ctcf, ) using ( mem_gb = 6, volatile = strict, ) stage MERGE_SINGLECELL_METRICS( in string reference_path, in csv singlecell_mapping, in csv singlecell_targets, in csv singlecell_cells, out csv singlecell, out json summary, src py "stages/metrics/merge_singlecell_metrics", ) using ( mem_gb = 8, volatile = strict, ) stage REPORT_INSERT_SIZES( in tsv.gz fragments, in bool exclude_non_nuclear, in string reference_path, out csv insert_sizes, out json insert_summary, src py "stages/metrics/report_insert_sizes", ) split ( in file barcode, out file total, ) using ( volatile = strict, ) stage REPORT_TSS_CTCF( in csv tss_relpos, in csv ctcf_relpos, out json summary_metrics, src py "stages/metrics/report_tss_ctcf", ) using ( volatile = strict, ) # # @include "_sc_atac_metric_collector.mro" # pipeline _SC_ATAC_METRIC_COLLECTOR( in bam[] read_paired_bam, in tsv.gz fragments, in tsv.gz.tbi fragments_index, in bed peaks, in string reference_path "this is the reference_path", in csv cell_barcodes, in csv singlecell_mapping, in csv singlecell_cells, out json singlecell_results, out csv singlecell, out json enrichment_results, out json basic_summary, out json insert_summary, out csv insert_sizes, out json bulk_complexity, out json singlecell_complexity, out json complexity_summary, out csv tss_relpos, out csv ctcf_relpos, ) { call GENERATE_SINGLECELL_TARGETING( fragments = self.fragments, fragments_index = self.fragments_index, peaks = self.peaks, reference_path = self.reference_path, ) call MERGE_SINGLECELL_METRICS( reference_path = self.reference_path, singlecell_mapping = self.singlecell_mapping, singlecell_cells = self.singlecell_cells, singlecell_targets = GENERATE_SINGLECELL_TARGETING.singlecell, ) call GENERATE_SEQUENCING_METRICS( input = self.read_paired_bam, ) call ESTIMATE_LIBRARY_COMPLEXITY( sequencing_summary = GENERATE_SEQUENCING_METRICS.summary, fragments = self.fragments, cell_barcodes = self.cell_barcodes, ) call REPORT_INSERT_SIZES( fragments = self.fragments, reference_path = self.reference_path, exclude_non_nuclear = true, ) call REPORT_TSS_CTCF( tss_relpos = GENERATE_SINGLECELL_TARGETING.tss_relpos, ctcf_relpos = GENERATE_SINGLECELL_TARGETING.ctcf_relpos, ) return ( ### singlecell = MERGE_SINGLECELL_METRICS.singlecell, singlecell_results = MERGE_SINGLECELL_METRICS.summary, ### enrichment_results = REPORT_TSS_CTCF.summary_metrics, basic_summary = GENERATE_SEQUENCING_METRICS.summary, insert_summary = REPORT_INSERT_SIZES.insert_summary, insert_sizes = REPORT_INSERT_SIZES.insert_sizes, bulk_complexity = ESTIMATE_LIBRARY_COMPLEXITY.bulk_complexity, singlecell_complexity = ESTIMATE_LIBRARY_COMPLEXITY.singlecell_complexity, complexity_summary = ESTIMATE_LIBRARY_COMPLEXITY.complexity_summary, tss_relpos = GENERATE_SINGLECELL_TARGETING.tss_relpos, ctcf_relpos = GENERATE_SINGLECELL_TARGETING.ctcf_relpos, ) } # # @include "_peak_annotator_stages.mro" # stage ANNOTATE_PEAKS( in bed peaks, in string reference_path, out tsv peak_annotation, src py "stages/analysis/annotate_peaks", ) split ( in int chunk_start, in int chunk_end, ) using ( mem_gb = 5, volatile = strict, ) stage COMPUTE_GC_DISTRIBUTION( in bed peaks, in string reference_path, out pickle GCdict, src py "stages/analysis/compute_gc_dist", ) split ( ) using ( volatile = strict, ) stage SCAN_MOTIFS( in pickle globalGCdict, in bed peaks, in string reference_path, in float pwm_threshold, out bed peak_motif_hits, src py "stages/analysis/scan_motifs", ) split ( in file GCdict, ) using ( volatile = strict, ) stage GENERATE_TF_MATRIX( in path reference_path, in bed peaks, in bed peak_motif_hits, in h5 filtered_matrix, out h5 filtered_tf_bc_matrix, out path filtered_tf_bc_matrix_mex, out gz tf_propZ_matrix, src py "stages/analysis/generate_tf_matrix", ) split ( ) using ( volatile = strict, ) # # @include "_peak_annotator.mro" # pipeline _PEAK_ANNOTATOR( in string reference_path, in bed peaks, in h5 filtered_peak_bc_matrix, in float pwm_threshold, out h5 filtered_tf_bc_matrix, out path filtered_tf_bc_matrix_mex, out gz tf_propZ_matrix, out tsv peak_annotation, ) { call ANNOTATE_PEAKS( peaks = self.peaks, reference_path = self.reference_path, ) call COMPUTE_GC_DISTRIBUTION( peaks = self.peaks, reference_path = self.reference_path, ) call SCAN_MOTIFS( globalGCdict = COMPUTE_GC_DISTRIBUTION.GCdict, peaks = self.peaks, reference_path = self.reference_path, pwm_threshold = self.pwm_threshold, ) call GENERATE_TF_MATRIX( reference_path = self.reference_path, peaks = self.peaks, filtered_matrix = self.filtered_peak_bc_matrix, peak_motif_hits = SCAN_MOTIFS.peak_motif_hits, ) return ( filtered_tf_bc_matrix = GENERATE_TF_MATRIX.filtered_tf_bc_matrix, filtered_tf_bc_matrix_mex = GENERATE_TF_MATRIX.filtered_tf_bc_matrix_mex, tf_propZ_matrix = GENERATE_TF_MATRIX.tf_propZ_matrix, peak_annotation = ANNOTATE_PEAKS.peak_annotation, ) } # # @include "_sc_atac_analyzer_stages.mro" # stage ANALYZER_PREFLIGHT( in bed peaks, in h5 filtered_peak_bc_matrix, in string[] factorization, in int tsne_perplexity, in int random_seed, in float tsne_theta, in int tsne_mom_switch_iter, in int tsne_stop_lying_iter, in int tsne_max_dims, in int tsne_input_pcs, in int tsne_max_iter, in int max_clusters, in int num_components, in int num_dr_bcs, in int num_dr_features, in float neighbor_a, in float neighbor_b, in int graphclust_neighbors, src py "stages/preflight/atac_analyzer", ) stage REDUCE_DIMENSIONS( in h5 filtered_matrix, in string[] factorization, in int num_dims, in int num_bcs, in int num_features, in int random_seed, out path reduced_data, out map reduction_summary, src py "stages/analysis/reduce_dimensions", ) split ( in string method, ) using ( volatile = strict, ) stage CLUSTER_CELLS( in h5 filtered_matrix, in path reduced_data, in map reduction_summary, in string[] factorization, in int minclusters, in int maxclusters, in int num_dims, in int random_seed, out path clustered_data, out map clustering_summary, src py "stages/analysis/cluster_cells", ) split ( in int n_clusters, ) using ( volatile = strict, ) stage PROJECT_TSNE( in h5 filtered_matrix, in path reduced_data, in map reduction_summary, in int tsne_perplexity, in int tsne_max_dims, in int tsne_input_pcs, in float tsne_theta, in int tsne_max_iter, in int tsne_stop_lying_iter, in int tsne_mom_switch_iter, in int random_seed, in string[] factorization, out path tsne, out map tsne_summary, src py "stages/analysis/project_tsne", ) split ( in string method, in int tsne_dims, ) using ( volatile = strict, ) stage RUN_GRAPH_CLUSTERING( in h5 matrix_h5 "Processed matrix", in string[] factorization, in path reduced_data, in map reduction_summary, in int num_neighbors "Use this many neighbors", in float neighbor_a "Use larger of (a+b*log10(n_cells) neighbors or num_neighbors", in float neighbor_b "Use larger of (a+b*log10(n_cells) neighbors or num_neighbors", in int balltree_leaf_size, in string similarity_type "Type of similarity to use (nn or snn)", out h5 chunked_neighbors, out path knn_clusters, out map graph_clustering_summary, src py "stages/analysis/run_graph_clustering", ) split ( in string method, in pickle neighbor_index, in h5 submatrix, in int row_start, in int total_rows, in int k_nearest, in h5 use_bcs, ) using ( volatile = strict, ) stage COMBINE_CLUSTERING( in h5 filtered_matrix, in map clustering_summary, in path clustered_data, in map graph_clustering_summary, in path knn_clusters, out path clustering, out map clustering_summary, src py "stages/analysis/combine_clustering", ) using ( volatile = strict, ) stage SUMMARIZE_ANALYSIS( in tsv peak_annotation, in h5 filtered_peak_bc_matrix, in h5 filtered_tf_bc_matrix, in gz tf_propZ_matrix, in path reduced_data, in map reduction_summary, in path clustering, in map clustering_summary, in path tsne, in map tsne_summary, in path enrichment_analysis, in map enrichment_analysis_summary, out h5 analysis, out path analysis_csv, out h5 feature_bc_matrix, src py "stages/analysis/summarize_analysis", ) split ( ) using ( volatile = strict, ) stage PERFORM_DIFFERENTIAL_ANALYSIS( in bed peaks, in string reference_path, in h5 filtered_peak_bc_matrix, in h5 filtered_tf_bc_matrix, in string[] factorization, in path clustering, in map clustering_summary, out path enrichment_analysis, out map enrichment_analysis_summary, src py "stages/analysis/perform_differential_analysis", ) split ( in string method, in string clustering_key, in int cluster, out csv tmp_diffexp, ) using ( volatile = strict, ) # # @include "_sc_atac_analyzer.mro" # pipeline _SC_ATAC_ANALYZER( in string reference_path, in bed peaks, in h5 filtered_peak_bc_matrix, in string[] factorization, in int tsne_perplexity, in int random_seed, in float tsne_theta, in int tsne_mom_switch_iter, in int tsne_stop_lying_iter, in int tsne_max_dims, in int tsne_input_pcs, in int tsne_max_iter, in int max_clusters, in int num_components, in int num_dr_bcs, in int num_dr_features, in float neighbor_a, in float neighbor_b, in int graphclust_neighbors, out h5 analysis, out path analysis_csv, out h5 filtered_tf_bc_matrix, out path filtered_tf_bc_matrix_mex, out h5 feature_bc_matrix, out tsv peak_annotation, ) { call ANALYZER_PREFLIGHT( peaks = self.peaks, filtered_peak_bc_matrix = self.filtered_peak_bc_matrix, factorization = self.factorization, tsne_perplexity = self.tsne_perplexity, random_seed = self.random_seed, tsne_theta = self.tsne_theta, tsne_mom_switch_iter = self.tsne_mom_switch_iter, tsne_stop_lying_iter = self.tsne_stop_lying_iter, tsne_max_dims = self.tsne_max_dims, tsne_input_pcs = self.tsne_input_pcs, tsne_max_iter = self.tsne_max_iter, max_clusters = self.max_clusters, num_components = self.num_components, num_dr_bcs = self.num_dr_bcs, num_dr_features = self.num_dr_features, neighbor_a = self.neighbor_a, neighbor_b = self.neighbor_b, graphclust_neighbors = self.graphclust_neighbors, ) using ( volatile = true, ) call _PEAK_ANNOTATOR( reference_path = self.reference_path, peaks = self.peaks, filtered_peak_bc_matrix = self.filtered_peak_bc_matrix, pwm_threshold = null, ) call REDUCE_DIMENSIONS( filtered_matrix = self.filtered_peak_bc_matrix, factorization = self.factorization, num_dims = self.num_components, num_bcs = self.num_dr_bcs, num_features = self.num_dr_features, random_seed = self.random_seed, ) call CLUSTER_CELLS( filtered_matrix = self.filtered_peak_bc_matrix, reduced_data = REDUCE_DIMENSIONS.reduced_data, reduction_summary = REDUCE_DIMENSIONS.reduction_summary, factorization = self.factorization, minclusters = 2, maxclusters = self.max_clusters, num_dims = null, random_seed = self.random_seed, ) call PROJECT_TSNE( filtered_matrix = self.filtered_peak_bc_matrix, reduced_data = REDUCE_DIMENSIONS.reduced_data, reduction_summary = REDUCE_DIMENSIONS.reduction_summary, tsne_perplexity = self.tsne_perplexity, tsne_max_dims = self.tsne_max_dims, tsne_input_pcs = self.tsne_input_pcs, tsne_theta = self.tsne_theta, tsne_max_iter = self.tsne_max_iter, tsne_stop_lying_iter = self.tsne_stop_lying_iter, tsne_mom_switch_iter = self.tsne_mom_switch_iter, random_seed = self.random_seed, factorization = self.factorization, ) call RUN_GRAPH_CLUSTERING( matrix_h5 = self.filtered_peak_bc_matrix, factorization = self.factorization, reduced_data = REDUCE_DIMENSIONS.reduced_data, reduction_summary = REDUCE_DIMENSIONS.reduction_summary, num_neighbors = self.graphclust_neighbors, neighbor_a = self.neighbor_a, neighbor_b = self.neighbor_b, balltree_leaf_size = null, similarity_type = "nn", ) call COMBINE_CLUSTERING( filtered_matrix = self.filtered_peak_bc_matrix, clustering_summary = CLUSTER_CELLS.clustering_summary, clustered_data = CLUSTER_CELLS.clustered_data, graph_clustering_summary = RUN_GRAPH_CLUSTERING.graph_clustering_summary, knn_clusters = RUN_GRAPH_CLUSTERING.knn_clusters, ) call PERFORM_DIFFERENTIAL_ANALYSIS( reference_path = self.reference_path, peaks = self.peaks, filtered_peak_bc_matrix = self.filtered_peak_bc_matrix, filtered_tf_bc_matrix = _PEAK_ANNOTATOR.filtered_tf_bc_matrix, factorization = self.factorization, clustering = COMBINE_CLUSTERING.clustering, clustering_summary = COMBINE_CLUSTERING.clustering_summary, ) call SUMMARIZE_ANALYSIS( peak_annotation = _PEAK_ANNOTATOR.peak_annotation, filtered_peak_bc_matrix = self.filtered_peak_bc_matrix, filtered_tf_bc_matrix = _PEAK_ANNOTATOR.filtered_tf_bc_matrix, tf_propZ_matrix = _PEAK_ANNOTATOR.tf_propZ_matrix, reduced_data = REDUCE_DIMENSIONS.reduced_data, reduction_summary = REDUCE_DIMENSIONS.reduction_summary, clustering = COMBINE_CLUSTERING.clustering, clustering_summary = COMBINE_CLUSTERING.clustering_summary, tsne = PROJECT_TSNE.tsne, tsne_summary = PROJECT_TSNE.tsne_summary, enrichment_analysis = PERFORM_DIFFERENTIAL_ANALYSIS.enrichment_analysis, enrichment_analysis_summary = PERFORM_DIFFERENTIAL_ANALYSIS.enrichment_analysis_summary, ) return ( analysis = SUMMARIZE_ANALYSIS.analysis, analysis_csv = SUMMARIZE_ANALYSIS.analysis_csv, filtered_tf_bc_matrix = _PEAK_ANNOTATOR.filtered_tf_bc_matrix, filtered_tf_bc_matrix_mex = _PEAK_ANNOTATOR.filtered_tf_bc_matrix_mex, feature_bc_matrix = SUMMARIZE_ANALYSIS.feature_bc_matrix, peak_annotation = _PEAK_ANNOTATOR.peak_annotation, ) } # # @include "_sc_atac_reporter_stages.mro" # stage SUMMARIZE_REPORTS_SINGLECELL( in string reference_path, in json complexity_summary, in json cell_calling_summary, in json peak_results, in json basic_results, in json error_results_summary, in json insert_summary, in json singlecell_results, in json contam_results, in json downsample_info, in json enrichment_results, out json analysis_params, out json summary, out csv summary_csv, src py "stages/reporter/summarize_reports_singlecell", ) using ( mem_gb = 4, ) stage CREATE_WEBSUMMARY( in string reference_path, in string barcode_whitelist, in json summary_results, in json bulk_complexity, in json singlecell_complexity, in string sample_id, in string sample_desc, in map[] sample_def, in bool debug, in csv singlecell, in csv insert_sizes, in csv tss_relpos, in csv ctcf_relpos, in h5 filtered_peak_bc_matrix, in h5 analysis, in json excluded_barcodes, out html web_summary, src py "stages/reporter/create_websummary", ) using ( mem_gb = 16, ) # # @include "_sc_atac_reporter.mro" # pipeline _SC_ATAC_REPORTER( in string reference_path, in string barcode_whitelist, in json bulk_complexity, in json cell_calling_summary, in json complexity_summary, in json basic_summary, in json peak_summary, in json singlecell_results, in json insert_summary, in json downsample_info, in json singlecell_complexity, in csv singlecell, in csv tss_relpos, in csv ctcf_relpos, in string sample_id, in string sample_desc, in map[] sample_def, in csv sc_insert_sizes, in json enrichment_results, in h5 filtered_peak_bc_matrix, in h5 analysis, in json excluded_barcodes, # out json summary, out html web_summary, out csv summary_csv, ) { call SUMMARIZE_REPORTS_SINGLECELL( reference_path = self.reference_path, complexity_summary = self.complexity_summary, cell_calling_summary = self.cell_calling_summary, peak_results = self.peak_summary, basic_results = self.basic_summary, error_results_summary = null, insert_summary = self.insert_summary, singlecell_results = self.singlecell_results, contam_results = null, downsample_info = self.downsample_info, enrichment_results = self.enrichment_results, ) call CREATE_WEBSUMMARY( reference_path = self.reference_path, barcode_whitelist = self.barcode_whitelist, singlecell = self.singlecell, tss_relpos = self.tss_relpos, ctcf_relpos = self.ctcf_relpos, sample_id = self.sample_id, sample_desc = self.sample_desc, sample_def = self.sample_def, insert_sizes = self.sc_insert_sizes, summary_results = SUMMARIZE_REPORTS_SINGLECELL.summary, bulk_complexity = self.bulk_complexity, singlecell_complexity = self.singlecell_complexity, analysis = self.analysis, filtered_peak_bc_matrix = self.filtered_peak_bc_matrix, excluded_barcodes = self.excluded_barcodes, debug = false, ) return ( summary = SUMMARIZE_REPORTS_SINGLECELL.summary, web_summary = CREATE_WEBSUMMARY.web_summary, summary_csv = SUMMARIZE_REPORTS_SINGLECELL.summary_csv, ) } # # @include "_atac_cloupe_stages.mro" # stage CLOUPE_PREPROCESS( in string pipestance_type, in string sample_id, in string sample_desc, in string reference_path, in h5 analysis, in h5 feature_barcode_matrix, in bed peaks, in tsv.gz.tbi fragments_index, in json metrics_json, in csv aggregation_csv, in json gem_group_index_json, in bool no_secondary_analysis, out cloupe output_for_cloupe, out json gem_group_index_json, src py "stages/cloupe/atac_cloupe_preprocess", ) split ( ) # # @include "_preflight_stages.mro" # stage ATAC_COUNTER_PREFLIGHT( in string sample_id, in string fastq_mode, in map[] sample_def, in string reference_path, in map force_cells, in string[] factorization, in map downsample, in bool check_executables, in map trim_def, src py "stages/preflight/atac_counter", ) split ( ) stage ATAC_AGGR_PREFLIGHT( in string sample_id, in string reference_path, in csv aggr_csv, in string normalization, in string[] factorization, in bool check_executables, src py "stages/preflight/atac_aggr", ) split ( ) stage ATAC_REANALYZER_PREFLIGHT( in string sample_id, in string reference_path, in string barcode_whitelist, in bed peaks, in csv parameters, in map force_cells, in csv cell_barcodes, in tsv.gz fragments, in tsv.gz.tbi fragments_index, in csv aggregation_csv, in bool check_executables, src py "stages/preflight/atac_reanalyzer", ) split ( ) # # @include "sc_atac_counter.mro" # pipeline SC_ATAC_COUNTER( in string fastq_mode, in string sample_id, in map[] sample_def, in map downsample, in string sample_desc, in string reference_path, in map trim_def, in string barcode_whitelist, in map adapters, in string[] factorization, in map force_cells, # out csv singlecell, out bam possorted_bam, out bam.bai possorted_bam_index, out json summary, out html web_summary, out bed peaks, out h5 raw_peak_bc_matrix, out path raw_peak_bc_matrix_mex, out path analysis_csv, out h5 filtered_peak_bc_matrix, out path filtered_peak_bc_matrix_mex, out tsv.gz fragments, out tsv.gz.tbi fragments_index, out h5 filtered_tf_bc_matrix, out path filtered_tf_bc_matrix_mex, out cloupe cloupe, out csv summary_csv, out tsv peak_annotation, ) { call ATAC_COUNTER_PREFLIGHT as ATAC_COUNTER_PREFLIGHT_LOCAL( sample_id = self.sample_id, fastq_mode = self.fastq_mode, sample_def = self.sample_def, reference_path = self.reference_path, force_cells = self.force_cells, factorization = self.factorization, downsample = self.downsample, trim_def = self.trim_def, check_executables = false, ) using ( local = true, preflight = true, ) call ATAC_COUNTER_PREFLIGHT( sample_id = self.sample_id, fastq_mode = self.fastq_mode, sample_def = self.sample_def, reference_path = self.reference_path, force_cells = self.force_cells, factorization = self.factorization, downsample = self.downsample, trim_def = self.trim_def, check_executables = true, ) using ( preflight = true, ) call _BASIC_SC_ATAC_COUNTER( sample_id = self.sample_id, fastq_mode = self.fastq_mode, sample_def = self.sample_def, trim_def = self.trim_def, adapters = self.adapters, reference_path = self.reference_path, barcode_whitelist = self.barcode_whitelist, downsample = self.downsample, force_cells = self.force_cells, ) call _SC_ATAC_METRIC_COLLECTOR( read_paired_bam = _BASIC_SC_ATAC_COUNTER.read_paired_bam, fragments = _BASIC_SC_ATAC_COUNTER.fragments, fragments_index = _BASIC_SC_ATAC_COUNTER.fragments_index, peaks = _BASIC_SC_ATAC_COUNTER.peaks, reference_path = self.reference_path, cell_barcodes = _BASIC_SC_ATAC_COUNTER.cell_barcodes, singlecell_cells = _BASIC_SC_ATAC_COUNTER.singlecell_cells, singlecell_mapping = _BASIC_SC_ATAC_COUNTER.singlecell_mapping, ) call _SC_ATAC_ANALYZER( peaks = _BASIC_SC_ATAC_COUNTER.peaks, filtered_peak_bc_matrix = _BASIC_SC_ATAC_COUNTER.filtered_peak_bc_matrix, reference_path = self.reference_path, factorization = self.factorization, tsne_perplexity = 30, tsne_max_dims = null, tsne_input_pcs = null, tsne_max_iter = null, tsne_stop_lying_iter = null, tsne_mom_switch_iter = null, tsne_theta = null, random_seed = null, max_clusters = 10, neighbor_a = null, neighbor_b = null, graphclust_neighbors = null, num_components = 15, num_dr_bcs = null, num_dr_features = null, ) call CLOUPE_PREPROCESS( pipestance_type = "SC_ATAC_COUNTER_CS", reference_path = self.reference_path, sample_id = self.sample_id, sample_desc = self.sample_desc, analysis = _SC_ATAC_ANALYZER.analysis, feature_barcode_matrix = _SC_ATAC_ANALYZER.feature_bc_matrix, metrics_json = _SC_ATAC_METRIC_COLLECTOR.basic_summary, peaks = _BASIC_SC_ATAC_COUNTER.peaks, fragments_index = _BASIC_SC_ATAC_COUNTER.fragments_index, aggregation_csv = null, gem_group_index_json = null, no_secondary_analysis = false, ) call _SC_ATAC_REPORTER( reference_path = self.reference_path, barcode_whitelist = self.barcode_whitelist, bulk_complexity = _SC_ATAC_METRIC_COLLECTOR.bulk_complexity, singlecell_complexity = _SC_ATAC_METRIC_COLLECTOR.singlecell_complexity, cell_calling_summary = _BASIC_SC_ATAC_COUNTER.cell_calling_summary, complexity_summary = _SC_ATAC_METRIC_COLLECTOR.complexity_summary, basic_summary = _SC_ATAC_METRIC_COLLECTOR.basic_summary, peak_summary = _BASIC_SC_ATAC_COUNTER.peak_metrics, singlecell_results = _SC_ATAC_METRIC_COLLECTOR.singlecell_results, insert_summary = _SC_ATAC_METRIC_COLLECTOR.insert_summary, downsample_info = _BASIC_SC_ATAC_COUNTER.downsample_info, singlecell = _SC_ATAC_METRIC_COLLECTOR.singlecell, tss_relpos = _SC_ATAC_METRIC_COLLECTOR.tss_relpos, ctcf_relpos = _SC_ATAC_METRIC_COLLECTOR.ctcf_relpos, sample_id = self.sample_id, sample_desc = self.sample_desc, sample_def = self.sample_def, sc_insert_sizes = _SC_ATAC_METRIC_COLLECTOR.insert_sizes, enrichment_results = _SC_ATAC_METRIC_COLLECTOR.enrichment_results, filtered_peak_bc_matrix = _BASIC_SC_ATAC_COUNTER.filtered_peak_bc_matrix, analysis = _SC_ATAC_ANALYZER.analysis, excluded_barcodes = _BASIC_SC_ATAC_COUNTER.excluded_barcodes, ) return ( singlecell = _SC_ATAC_METRIC_COLLECTOR.singlecell, possorted_bam = _BASIC_SC_ATAC_COUNTER.possorted_bam, possorted_bam_index = _BASIC_SC_ATAC_COUNTER.possorted_bam_index, summary = _SC_ATAC_REPORTER.summary, web_summary = _SC_ATAC_REPORTER.web_summary, peaks = _BASIC_SC_ATAC_COUNTER.peaks, raw_peak_bc_matrix = _BASIC_SC_ATAC_COUNTER.raw_peak_bc_matrix, raw_peak_bc_matrix_mex = _BASIC_SC_ATAC_COUNTER.raw_peak_bc_matrix_mex, analysis_csv = _SC_ATAC_ANALYZER.analysis_csv, filtered_peak_bc_matrix = _BASIC_SC_ATAC_COUNTER.filtered_peak_bc_matrix, filtered_peak_bc_matrix_mex = _BASIC_SC_ATAC_COUNTER.filtered_peak_bc_matrix_mex, fragments = _BASIC_SC_ATAC_COUNTER.fragments, fragments_index = _BASIC_SC_ATAC_COUNTER.fragments_index, filtered_tf_bc_matrix = _SC_ATAC_ANALYZER.filtered_tf_bc_matrix, filtered_tf_bc_matrix_mex = _SC_ATAC_ANALYZER.filtered_tf_bc_matrix_mex, cloupe = CLOUPE_PREPROCESS.output_for_cloupe, summary_csv = _SC_ATAC_REPORTER.summary_csv, peak_annotation = _SC_ATAC_ANALYZER.peak_annotation, ) } # # @include "sc_atac_counter_cs.mro" # # Customer-facing (CS) pipeline pipeline SC_ATAC_COUNTER_CS( in string fastq_mode "Input fastq configuration", in string sample_id, in map[] sample_def, in map downsample, in string sample_desc "Sample description", in string reference_path "Path to 10X reference package", in string[] factorization "Dimensionality reduction method (lsa, plsa, or pca)", in map force_cells "Force cell calling to a fixed number", # out csv singlecell "Per-barcode fragment counts & metrics", out bam possorted_bam "Position sorted BAM file" "possorted_bam.bam", out bam.bai possorted_bam_index "Position sorted BAM index" "possorted_bam.bam.bai", out json summary "Summary of all data metrics", out html web_summary "HTML file summarizing data & analysis", out bed peaks "Bed file of all called peak locations", out h5 raw_peak_bc_matrix "Raw peak barcode matrix in hdf5 format", out path raw_peak_bc_matrix_mex "Raw peak barcode matrix in mex format" "raw_peak_bc_matrix", out path analysis_csv "Directory of analysis files" "analysis", out h5 filtered_peak_bc_matrix "Filtered peak barcode matrix in hdf5 format", out path filtered_peak_bc_matrix_mex "Filtered peak barcode matrix in mex format" "filtered_peak_bc_matrix", out tsv.gz fragments "Barcoded and aligned fragment file" "fragments.tsv.gz", out tsv.gz.tbi fragments_index "Fragment file index" "fragments.tsv.gz.tbi", out h5 filtered_tf_bc_matrix "Filtered tf barcode matrix in hdf5 format", out path filtered_tf_bc_matrix_mex "Filtered tf barcode matrix in mex format" "filtered_tf_bc_matrix", out cloupe cloupe "Loupe Cell Browser input file", out csv summary_csv "csv summarizing important metrics and values" "summary.csv", out tsv peak_annotation "Annotation of peaks with genes", ) { call SC_ATAC_COUNTER( fastq_mode = self.fastq_mode, sample_id = self.sample_id, sample_def = self.sample_def, downsample = self.downsample, sample_desc = self.sample_desc, reference_path = self.reference_path, trim_def = { "R1": { "3prime": ["MErc"], }, "R2": { "3prime": ["MErc"], }, "discard_untrimmed": false, }, barcode_whitelist = "737K-cratac-v1", adapters = { "ME": "AGATGTGTATAAGAGACAG", "MErc": "CTGTCTCTTATACACATCT", }, factorization = self.factorization, force_cells = self.force_cells, ) return ( singlecell = SC_ATAC_COUNTER.singlecell, possorted_bam = SC_ATAC_COUNTER.possorted_bam, possorted_bam_index = SC_ATAC_COUNTER.possorted_bam_index, summary = SC_ATAC_COUNTER.summary, web_summary = SC_ATAC_COUNTER.web_summary, peaks = SC_ATAC_COUNTER.peaks, raw_peak_bc_matrix = SC_ATAC_COUNTER.raw_peak_bc_matrix, raw_peak_bc_matrix_mex = SC_ATAC_COUNTER.raw_peak_bc_matrix_mex, analysis_csv = SC_ATAC_COUNTER.analysis_csv, filtered_peak_bc_matrix = SC_ATAC_COUNTER.filtered_peak_bc_matrix, filtered_peak_bc_matrix_mex = SC_ATAC_COUNTER.filtered_peak_bc_matrix_mex, fragments = SC_ATAC_COUNTER.fragments, fragments_index = SC_ATAC_COUNTER.fragments_index, filtered_tf_bc_matrix = SC_ATAC_COUNTER.filtered_tf_bc_matrix, filtered_tf_bc_matrix_mex = SC_ATAC_COUNTER.filtered_tf_bc_matrix_mex, cloupe = SC_ATAC_COUNTER.cloupe, summary_csv = SC_ATAC_COUNTER.summary_csv, peak_annotation = SC_ATAC_COUNTER.peak_annotation, ) } # # @include "__PBMC_P.mro" # call SC_ATAC_COUNTER_CS( fastq_mode = "ILMN_BCL2FASTQ", sample_id = "PBMC_P", sample_def = [{ "bc_in_read": 1, "bc_length": 16, "gem_group": null, "lanes": null, "library": "LibraryNotSpecified", "read_path": "/data/isshamie/dropbox/ATACseq/2020_11_18_Croker/igm-storage2.ucsd.edu/201113_A00953_0185_AHN7TMDSXY", "sample_indices": ["any"], "sample_names": ["BC_10xATAC_PMBC_P"], }], reference_path = "/data/isshamie/mito_lineage/data/external/GRCh38_MT_blacklist", downsample = null, sample_desc = "", factorization = ["lsa"], force_cells = null, ) ew file mode 100644 ndex 0000000..9c51e87 ++ b/Analysis/mtscATAC/2020_11_18_Croker/PBMC_P/_sitecheck
05b9acd82df078b27bb85950bc781cd1992f995d
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This should be in mro/common for general use
Mito_Trace/Analysis/mtscATAC/2020_11_18_Croker/PBMC_P/_mrosource
Line 582 in ed20573
05b9acd82df078b27bb85950bc781cd1992f995d
The text was updated successfully, but these errors were encountered: