What is cellranger doing in comparison to other methods? I've recently started working with the 10X-Genomics platform with Illumina (MiSeq and HiSeq) for single-cell RNA-Seq. I've been recommended the "cellranger" (version 2.1.0) which I understand handles the barcoding of the platform and performs processing of the data. For example:

1) convert raw Illumina .bcl files into .fastq (demultiplex)

cellranger mkfastq


2) perform alignment, barcode counting, and generate gene-barcode-martices

cellranger count


However, it is unclear which underlying bioinformatics procedure (with which parameters) are being used here. For this pipeline, I wish to know how the trimming, alignment and counts are being performed. This is important to understand whether further normalisation and quality checking is necessary. What steps would I need to do to perform a similar analysis? 
If for example, I wish to document in more detail what has been performed here in a reproducible manner or reproduce the analysis pipeline on a different platform (such as Nadia) to compare them.

Ideally, I would like to know the analogous pipeline using open-source tools but at the moment I am first seeking to understand what is going on under the hood when I am running the cellranger.
cellranger mkfastq is not necessary anymore.  It used to be that the cellranger software wanted the reads to be interleaved, and you could use cellranger to do that for you if you couldn't do it yourself.  Newer versions of cellranger will take the fastq files just like Illumina's bcl2fastq makes them.

Cellranger count aligns the reads, filters away duplicates based on UMIs, tries to figure out which cell barcodes really captured cells, and tells you for each cell barcode how many reads hit each gene.  You can go through the .bams, and see which reads were counted for what gene and what barcode, based on the flags and other tags in the .bam

If you want another pipeline to compare to, you can use the McCarroll lab's DropSeq pipeline, which is a bunch of little scripts, around the output of STAR aligner (just like 10XGenomics uses).  I have put the same data sets through both 10X and Dropseq, and they return about the same thing.