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How to use cell ranger for single-cell transcriptome quantitative analysis, in view of this problem, this article introduces the corresponding analysis and solution in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

In the quantitative analysis of RNA_seq data, we first compare reads to the reference genome, and then use quantitative software for quantitative analysis, such as the classical hisat+stringTie analysis strategy, for single-cell transcriptome, the quantitative principle is the same, but due to the introduction of the design of UMI tags, we need to consider that the same UMI tags come from the same transcript, so it is not appropriate to use traditional analysis software directly.

The official cell ranger software provides not only data splitting, but also quantitative analysis.

The quantitative premise is that the reads needs to be compared to the reference genome. for comparison, the first step is to index the reference genome. the reference genomes of humans and mice are available for download on the official website.

Https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest

For other species, we only need to have the fasta file of the genome and the gtf file of the transcript to customize the reference genome, as follows

1. Filter GTF files

The original GTF file will contain many types of genes, and you can screen the genes of interest through the mkgtf subcommand, using the following usage

Cellranger mkgtf\ hg38.ensembl.gtf\ hg38.ensembl.filtered.gtf\-- attribute=gene_biotype:protein_coding

Screened by the attribute attribute, only the corresponding records of the protein-coding genes were screened in the above example.

two。 Build an index

Index through the mkref subcommand, using the following

Cellranger mkref\-genome=output_genome\-nthreads=10\-fasta=input.fa\-genes=input.gtf

The genome parameter specifies the directory of the output result. After indexing, the directory structure is as follows

. ├── fasta │ ├── genome.fa │ └── genome.fa.fai ├── genes │ └── genes.gtf ├── pickle │ └── genes.pickle ├── reference.json └── star

As you can see, cell ranger establishes the index of STAR on the genome and then compares the reads to the reference genome through STAR.

Quantitative analysis is achieved through the count subcommand, which is used as follows

Cellranger count\-id=sample345\-transcriptome=database_path\-fastqs=fastq_path\-sample=mysample\

The id parameter specifies the name of the output directory, the transcriptome parameter specifies the directory where the genome index is located, the fastqs specifies the directory where the sequence files generated by the mkfastq command are located, and the sample parameter specifies the sample to be analyzed, which corresponds to a subdirectory under fastq_path.

The count subcommand can not only carry out quantitative analysis, but also provide a series of analysis results such as clustering, PCA, tSNE and so on. The output results are intended to record a lot of files, and we will interpret the output results of the command in detail later.

This is the answer to the question on how to use cell ranger for quantitative analysis of single-cell transcriptome. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel for more related knowledge.

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