单细胞工具箱|Cell Ranger-V6.0 开启单细胞之旅（上）
单细胞工具箱|Cell Ranger-V6.0 开启单细胞之旅（上）
Cell Ranger是一个10X genomics公司的单细胞分析软件，将原始的fastq文件生成后续分析的feature-barcode表达矩阵。
其中包括很多模块，本次主要介绍cellranger mkfastq、cellranger count，cellranger aggr 和 cellranger reanalyze四个功能模块。
一 Cell Ranger下载安装
1.1 下载
进入cellranger官网（https://support.10xgenomics.com/）后，发现支持的分析模块有很多，先介绍单细胞转录组。选择单细胞转录组模块，点击进入
软件-下载-选择你想要的cellranger版本，
https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest
1）curl ，wget 和 直接网页下载，三种方式均可；
2）记得下载注释文件
3）注意查看md5值（很重要）
1.2 安装
Step1：解压下载的软件安装包
#进入文件存放的位置，示例为opt
$ cd /opt
#解压
$ tar -xzvf cellranger-6.0.1.tar.gz解压缩到一个名为cellranger-6.0.1的新目录，包含Cell Ranger及其依赖项和Cell Ranger脚本。
Step2：同样的方式解压参考文件
$ tar -xzvf refdata-gex-GRCh38-2020-A.tar.gzStep3：配置环境
将Cell Ranger目录添加到$PATH中，注意路径要准确，示例为/opt ，
$ export PATH=/opt/cellranger-6.0.1:$PATH为使用方便可以添加到.bashrc文件中。
1.3 测试安装
可以查看一下版本和帮助，或者参考官网的Site Check Script 的方式。
cellranger -V
cellranger -h下载：https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest
安装：https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/installation
二 mkfastq模块
cellranger使用mkfastq功能来拆分Illumina 原始数据（raw base call (BCL)），输出 FASTQ 文件。
2.1 下载示例数据
点击下载即可
2.2 Running mkfastq with a Simple CSV Samplesheet
1）首先示例矩阵数据解压缩，当前目录下生成cellranger-tiny-bcl-1.2.0文件夹
tar -xvzf cellranger-tiny-bcl-1.2.0.tar.gz2）Simple CSV Samplesheet文件
格式：三列(Lane、Sample、Index)，逗号分隔，不太容易出现格式错误。示例数据cellrangerver -tiny-bcl-simple-1.2.0.csv如下:
Lane,Sample,Index
1,test_sample,SI-TT-D9Lane | Which lane(s) of the flowcell to process. Can be either a single lane, a range (e.g., 2-4) or '*' for all lanes in the flowcell. |
|---|---|
Sample | The name of the sample. This name is the prefix to all the generated FASTQs, and corresponds to the --sample argument in all downstream 10x pipelines.Sample names must conform to the Illumina bcl2fastq naming requirements. Only letters, numbers, underscores and hyphens area allowed; no other symbols, including dots (".") are allowed. |
Index | The 10x sample index that was used in library construction, e.g., SI-TT-D9 or SI-GA-A1 |
3）run mkfastq
需要安装且配置bcl2fastq软件
$ cellranger mkfastq --id=cellranger-tiny-bcl-1.2.0 \
--run=/path/to/cellranger-tiny-bcl-1.2.0 \
--csv=cellranger-tiny-bcl-simple-1.2.0.csvid ：即为解压后的文件夹名字
run：为解压后的文件夹的绝对路径
在id名的新文件夹中既有生成的fastq文件了，可以用于后续的count分析。
另一种请参考https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/mkfastq
三 count 模块
此处使用转录组数据进行count分析，通过fastq文件得到细胞和基因的定量结果。
3.1 必要参数
$ cellranger count --id=sample345 \
--transcriptome=/opt/refdata-gex-GRCh38-2020-A \
--fastqs=/home/jdoe/runs/HAWT7ADXX/outs/fastq_path \
--sample=mysample \
--expect-cells=1000 \--id= 名称 --fastqs= fastq.gz文件保存的绝对路径 --sample= fastq.gz文件名"-"之前的字段 --transcriptome= 参考基因组路径
--expect-cells= 期望细胞数（可选）
3.2 参数列表
参数详细介绍详见：
https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/count#args中的Command-Line Argument Reference 部分
可以注意下以下参数：
--expect-cells | (optional) Expected number of recovered cells. Default: 3,000 cells. | 和实验匹配 |
|---|---|---|
--nosecondary | (optional) Add this flag to skip secondary analysis of the feature-barcode matrix (dimensionality reduction, clustering and visualization). Set this if you plan to use cellranger reanalyze or your own custom analysis. | 仅获得表达矩阵，不进行后续的降维，聚类和可视化分析 |
--chemistry | (optional) Assay configuration. NOTE: by default the assay configuration is detected automatically, which is the recommended mode. You should only specify chemistry if there is an error in automatic detection. Select one of:auto for auto-detection (default),... |
- auto for auto-detection (default),
- ...
3.3 结果文件
结果文件列表以及简要描述说明
File Name | Description | |
|---|---|---|
web_summary.html | Run summary metrics and charts in HTML format | 网页简版报告以及可视化 |
metrics_summary.csv | Run summary metrics in CSV format | |
possorted_genome_bam.bam | Reads aligned to the genome and transcriptome annotated with barcode information | |
possorted_genome_bam.bam.bai | Index for possorted_genome_bam.bam | |
filtered_feature_bc_matrix | Filtered feature-barcode matrices containing only cellular barcodes in MEX format. (In Targeted Gene Expression samples, the non-targeted genes are not present.) | 过滤掉的barcode信息 |
filtered_feature_bc_matrix_h5.h5 | Filtered feature-barcode matrices containing only cellular barcodes in HDF5 format. (In Targeted Gene Expression samples, the non-targeted genes are not present.) | 过滤掉的barcode信息HDF5 format； |
raw_feature_bc_matrices | Unfiltered feature-barcode matrices containing all barcodes in MEX format | 原始barcode信息 |
raw_feature_bc_matrix_h5.h5 | Unfiltered feature-barcode matrices containing all barcodes in HDF5 format | 原始barcode信息HDF5 format |
analysis | Secondary analysis data including dimensionality reduction, cell clustering, and differential expression | |
molecule_info.h5 | Molecule-level information used by cellranger aggr to aggregate samples into larger datasets | |
cloupe.cloupe | Loupe Browser visualization and analysis file | Loupe Cell Browser 输入文件 |
feature_reference.csv | (Feature Barcode only) Feature Reference CSV file | |
target_panel.csv | (Targeted GEX only) Targed panel CSV file |
参考资料：https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/mkfastq