> For the complete documentation index, see [llms.txt](https://zpliu.gitbook.io/booknote/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://zpliu.gitbook.io/booknote/cell-ranger/dan-ge-yang-ben-de-fen-xi.md).

# single sample Analysis

## 大致流程主要分为以下几步

1. `cellranger mkfastq`将下机数据转换为fastq数据格式
2. 使用`cellranger count`将单个通道**GEM well**的fastq数据进行分析
3. 如果一个样品在多个通道中进行了测序，还可以选择`cellranger aggr`将多个通道的数据进行聚合生成单个样品的数据
4. 最后如果需要的话可以使用`cellranger reanalyze`对**libraries**中的数据进行重新分析例如 **PCA t-SNE clustering** 等主要是对参数进行一些微调

## 1. `cellranger mkfastq`

这里就不做详细的介绍，因为公司返回的数据经过处理了已经是fastq的格式了，不过需要注意的是，在进行`cellranger count`分析时，对fastq文件的命名有一定的要求

**e.g. SampleName\_S1\_L001\_R1\_001.fastq.gz**

**SampleName\_S1\_L001\_R2\_001.fastq.gz**

## 2.`cellranger count`

最主要的就一下几个参数

* \--id 指定输出文件夹的名字
* \--transcriptome 指定参考基因组的路径
* \--sample 指定需要处理的fastq文件的前缀
* \--expect-cell=1000 指定预期的细胞数目
* \--localcores 指定计算的核心数
* \--mempercore 指定内存大小 GB

基本的命令格式

```bash
cellranger count --id=J668   --transcriptome=~/genome_data/Ghirsutum_genome_HAU_v1.1/cellranger_reference/Ghirsutum/ --fastqs=~/work/scRNA-seq/Rawdata/   --localcores=20  --sample=J668 --mempercore=20  --expect-cell=1000
```


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