The best approach would be to contact 10x Genomics support: [email protected] . They usually respond within a few hours.
Otherwise, you can check the methods from the first 10x Genomics paper:
The Cell Ranger Single-Cell Software Suite was used to perform sample
demultiplexing, barcode processing and single-cell 3′ gene counting
(http://software.10xgenomics.com/single-cell/overview/welcome). First,
sample demultiplexing was performed based on the 8 bp sample index
read to generate FASTQs for the Read1 and Read2 paired-end reads, as
well as the 14 bp GemCode barcode. Ten basepair UMI tags were
extracted from Read2 (14 libraries were made with 5 bp UMI tags, as
noted in Supplementary Table 1, due to an earlier iteration of the
methods. For these samples, 5 bp UMI tags were extracted from Read2.).
Then, Read1, which contains the cDNA insert, was aligned to an
appropriate reference genome using STAR35. For mouse cells, mm10 was
used and for human cells, hg19 was used. For samples with mouse and
human cell mixtures, the union of hg19 and mm10 were used. For ERCC
samples, ERCC reference
(https://tools.thermofisher.com/content/sfs/manuals/cms_095047.txt)
was used.
Next, GemCode barcodes and UMIs were filtered. All of the known listed
of barcodes that are 1-Hamming-distance away from an observed barcode
are considered. Then, the posterior probability that the observed
barcode was produced by a sequencing error is computed, given the base
qualities of the observed barcode and the prior probability of
observing the candidate barcode (taken from the overall barcode count
distribution). If the posterior probability for any candidate barcode
is at least 0.975, then the barcode is corrected to the candidate
barcode with the highest posterior probability. If all candidate
sequences are equally probable, then the one appearing first by
lexical order is picked.
UMIs with sequencing quality score >10 were considered valid if they
were not homopolymers. Qual=10 implies 90% base call accuracy. A UMI
that is 1-Hamming-distance away from another UMI (with more reads) for
the same cell barcode and gene is corrected to the UMI with more
reads. This approach is nearly identical to that in Jaitin et al.4,
and is similar to that in Klein et al.8 (although Klein et al.8 also
used UMIs to resolve multimapped reads, which was not implemented
here).
Last, PCR duplicates were marked if two sets of read pairs shared a
barcode sequence, a UMI tag, and a gene ID (Ensembl GTFs GRCh37.82,
ftp://ftp.ensembl.org/pub/grch37/release-84/gtf/homo_sapiens/Homo_sapiens.GRCh37.82.gtf.gz
and GRCm38.84,
ftp://ftp.ensembl.org/pub/release-84/gtf/mus_musculus/Mus_musculus.GRCm38.84.gtf.gz,
were used). Only confidently mapped (MAPQ=255), non-PCR duplicates
with valid barcodes and UMIs were used to generate gene-barcode
matrix.
Cell barcodes were determined based on distribution of UMI counts. All
top barcodes within the same order of magnitude (>10% of the top nth
barcode, where n is 1% of the expected recovered cell count) were
considered cell barcodes. Number of reads that provide meaningful
information is calculated as the product of four metrics: (1) valid
barcodes; (2) valid UMI; (3) associated with a cell barcode; and (4)
confidently mapped to exons.
Of course, they have since updated the library prep and the Cell Ranger software, so the exact protocol is somewhat different.