单细胞转录组RNA速率分析

探序基因

已于 2025-01-16 21:14:33 修改

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分类专栏：单细胞分析文章标签： linux 运维服务器

于 2025-01-12 22:19:43 首次发布

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探序基因肿瘤研究院整理

python版本的参考手册：

Welcome to velocyto.py! — velocyto 0.17.16 documentation

运行代码：

velocyto run10x [OPTIONS] SAMPLEFOLDER GTFFILE

eg: velocyto run10x -@ 6 /xxx/test-sample /xxxx/genes.gtf

参数说明：

Runs the velocity analysis for a Chromium 10X Sample

10XSAMPLEFOLDER specifies the cellranger sample folder

GTFFILE genome annotation file

Options:
-s, --metadatatable FILE        Table containing metadata of the various samples (csv fortmated rows are samples and cols are entries)
-m, --mask FILE                 .gtf file containing intervals to mask
-l, --logic TEXT                The logic to use for the filtering (default: Default)
-M, --multimap                  Consider not unique mappings (not reccomended)
-@, --samtools-threads INTEGER The number of threads to use to sort the bam by cellID file using samtools
--samtools-memory INTEGER       The number of MB used for every thread by samtools to sort the bam file
-t, --dtype TEXT                The dtype of the loom file layers - if more than 6000 molecules/reads per gene per cell are expected set uint32 to avoid truncation (default run_10x: uint16)
-d, --dump TEXT                 For debugging purposes only: it will dump a molecular mapping report to hdf5. --dump N, saves a cell every N cells. If p is prepended a more complete (but huge) pickle report is printed (default: 0)
-v, --verbose                   Set the vebosity level: -v (only warinings) -vv (warinings and info) -vvv (warinings, info and debug)
--help                          Show this message and exit.

关于读入所需要的文件

查看/root/miniconda3/lib/python3.12/site-packages/velocyto/commands/run10x.py文件，发现：

bamfile = os.path.join(samplefolder, "outs", "possorted_genome_bam.bam")

    bcmatches = glob.glob(os.path.join(samplefolder, os.path.normcase("outs/filtered_gene_bc_matrices/*/barcodes.tsv")))
    if len(bcmatches) == 0:
        bcmatches = glob.glob(os.path.join(samplefolder, os.path.normcase("outs/filtered_feature_bc_matrix/barcodes.tsv.gz")))
    if len(bcmatches) == 0:
        logging.error("Can not locate the barcodes.tsv file!")
    bcfile = bcmatches[0]

    outputfolder = os.path.join(samplefolder, "velocyto")
    sampleid = os.path.basename(samplefolder.rstrip("/").rstrip("\\"))
    assert not os.path.exists(os.path.join(outputfolder, f"{sampleid}.loom")), "The output already exist. Aborted!"
    additional_ca = {}

由此可知，需要outs文件夹下的possorted_genome_bam.bam文件，以及outs文件夹下的filtered_feature_bc_matrix文件夹下的barcodes.tsv.gz文件。

运行完后，会在 /xxx/test-sample生成一个velocyto文件夹，里面有loom文件

在R中安装velocyto.R：

library(devtools)

install_github("velocyto-team/velocyto.R")

发现报错：ERROR: dependency ‘pcaMethods’ is not available for package ‘velocyto.R’

再安装pcaMethods包：Bioconductor - pcaMethods

再接着安装velocyto.R，发现报错：

/usr/bin/ld: 找不到 -lboost_filesystem
/usr/bin/ld: 找不到 -lboost_system
collect2: 错误：ld 返回 1

于是安装boost库，去Boost C++ Libraries 下载安装包，boost_1_87_0.tar.gz，安装即可。整个过程很快也不复杂。

过程如下：

./bootstrap.sh
./b2 install --prefix=/usr/local
最后配置自己的系统环境变量vi ~/.bashrc或者系统环境变量vi /etc/profile：
export BOOST_ROOT=/usr/local
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib

更新环境变量后，顺利安装好。