Cell Ranger ATAC1.1, printed on 11/24/2024
The cellranger-atac pipeline performs cell calling where it determines whether each barcode is a cell of any species included in the reference. Based on mapping information, the pipelines also provides QC information associated with the fragments per barcode. Additionally, the pipeline computes the ATAC signal per barcode, captured by various targeting metrics such as number of fragments overlapping transcription start sites (TSS) annotated in the reference package. All of this per barcode information is collated and produced in a single output table: singlecell.csv.
The structure and contents of singlecell.csv from a single species analysis are shown below:
$ cd /home/jdoe/runs/sample345/outs $ head -5 singlecell.csv barcode,total,duplicate,chimeric,unmapped,lowmapq,mitochondrial,passed_filters,cell_id,is__cell_barcode,TSS_fragments,DNase_sensitive_region_fragments,enhancer_region_fragments,promoter_region_fragments,on_target_fragments,blacklist_region_fragments,peak_region_fragments,peak_region_cutsites NO_BARCODE,14876,7802,379,704,1149,0,4842,None,0,0,0,0,0,0,0,0,0 AAACGAAAGAACAGGA-1,1,0,0,0,0,0,1,None,0,0,1,0,0,1,0,0,0 AAACGAAAGAACCATA-1,27,10,0,0,1,0,16,None,0,2,12,1,0,12,0,1,1 AAACGAAAGACCATAA-1,8,4,0,0,2,0,2,None,0,2,2,0,2,2,0,0,0
The table contains many columns, including the primary barcode column. All the barcodes in the dataset are listed in this column. The NO_BARCODE row contains a summary of fragments that are not associated with any whitelisted barcodes. It usually forms a small fraction of all reads.
| Column | Type | Description | Pipeline specific changes | Reference specific changes |
|---|---|---|---|---|
barcode | key | barcodes present in input data | ||
total | sequencing | total read-pairs | absent in aggr, reanalyze | |
duplicate | mapping | number of duplicate read-pairs | ||
chimeric | mapping | number of chimerically mapped read-pairs | absent in aggr, reanalyze | |
unmapped | mapping | number of read-pairs with at least one end not mapped | absent in aggr, reanalyze | |
lowmapq | mapping | number of read-pairs with <30 mapq on at least one end | absent in aggr, reanalyze | |
mitochondrial | mapping | number of read-pairs mapping to mitochondria and non-nuclear contigs | absent in aggr, reanalyze | |
passed_filters | mapping | number of non-duplicate, usable read-pairs i.e. "fragments" | for multi species, for example hg19 and mm10, expect additional columns: passed_filters_hg19 and passed_filtered_mm10 | |
cell_id | cell calling | index of the barcode in cell barcodes. Appears as {species}_cell_{num}, otherwise None. | for multi species, for example hg19 and mm10, doublets will appear as hg19_cell_{num1}_mm10_cell_{num2}. | |
is__cell_barcode | cell calling | binary indicator of whether barcode is associated with a cell | for multi species, for example hg19 and mm10, expect columns is_hg19_cell_barcode and is_mm10_cell_barcode instead. | |
TSS_fragments | targeting | number of fragments overlapping with TSS regions | ||
DNase_sensitive_region_fragments | targeting | number of fragments overlapping with DNase sensitive regions | For custom references or references missing the dnase.bed file, this count is 0 | |
enhancer_region_fragments | targeting | number of fragments overlapping enhancer regions | For custom references or references missing the enhancer.bed file, this count is 0 | |
promoter_region_fragments | targeting | number of fragments overlapping promoter regions | For custom references or references missing the promoter.bed file, this count is 0 | |
on_target_fragments | targeting | number of fragments overlapping any of TSS, enhancer, promoter and DNase hypersensitivity sites (counted with multiplicity) | For custom references or references having only the tss.bed file, this count is simply equal to the TSS_fragments | |
blacklist_region_fragments | targeting | number of fragments overlapping blacklisted regions | ||
peak_region_fragments | denovo targeting | number of fragments overlapping peaks | for multi species, for example hg19 and mm10, expect additional columns: peak_region_fragments_hg19 and peak_region_fragments_mm10 | |
peak_region_cutsites | denovo targeting | number of ends of fragments in peak regions |
Note that the number of columns and the column names themselves change and depend on what pipeline and what reference was used to generate the output file. Briefly, as described in the last two columns in the table,
mapping type columns (whatever subset is present) will be equal to the total.singlecell.csv can be loaded easily in Python as a pandas dataframe:
import pandas as pd singlecell_file = "/home/jdoe/runs/sample345/outs/singlecell.csv" # load without index scdf = pd.read_csv(singlecell_file, sep=",") # load with barcode as index scdf2 = pd.read_csv(singlecell_file, sep=",", index_col="barcode" )
You can use this file in many ways. Below are some examples:
Assume you are analyzing data from a single species library, such as hg19. To reproduce the targeting plot on the right side in Targeting section of the websummary, you can do the following:
import matplotlib as plt cell_mask = (scdf['is__cell_barcode'] == 1) noncell_mask = (scdf['is__cell_barcode'] != 1 && scdf['barcode'] != 'NO_BARCODE') plt.plot(scdf[cell_mask]['passed_filters'], scdf[cell_mask]['peak_region_fragments'] / scdf[cell_mask]['passed_filters'], c='b') plt.plot(scdf[noncell_mask]['passed_filters'], scdf[noncell_mask]['peak_region_fragments'] / scdf[noncell_mask]['passed_filters'], c='r')
The singlecell.csv file captures the cell calling information in the is_{species}_cell_barcode field. The Cell Ranger ATAC aggr pipeline requires you to specify the singlecell.csv as part of the aggr_csv argument. On the other hand, the Cell Ranger ATAC reanalyze pipeline accepts an optional input for cell barcodes in the form of the singlecell.csv file. You can control what barcodes get analyzed as cells from each library by editing the cell calling columns in the singlecell.csv file. In particular, you only need to edit the is_{species}_cell_barcode columns. For example, if you have a list of barcodes you want to keep, for example, after editing the barcodes.tsv file produced as part of the matrices mex format, you can do the following:
barcodes_file = "/home/jdoe/runs/sample345/outs/filtered_peak_bc_matrix_mex/barcodes.tsv" with open(barcodes_file, 'r') as infile: keep_barcodes = [bc.strip("\n") for bc in infile] # keep_barcode must contain barcodes present in the singlecell.csv file scdf2.loc[keep_barcodes, 'is__cell_barcode'] = 1 scdf2.to_csv(out_file, sep=",", index=False)
Care must be taken while editing the singlecell.csv in multi-species samples, in which case, you want to edit the per species cell calling columns separately.