单细胞转录组CNV分析 | inferCNV | epiAneufinder | Gene amplification (copy number gain) | Gene deletion (copy number loss)

2023年11月09日

明确定义 CNV有两个方向：gain和loss

Gene amplification (copy number gain) and deletion (copy number loss) are common in cancer cells and contribute to cancer cell growth, drug sensitivity and resistance.

The Importance of Detecting Copy Number Variants (CNVs) in the Cancer Genome

2023年10月18日

epiAneufinder

2023年01月19日

安装（https://github.com/broadinstitute/infercnv）

conda install -c conda-forge r-rjags
 
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
 
BiocManager::install("infercnv")

1	`conda install -c conda-forge r-magick`

分析目录：projects/ApcKO_multiomics/ApcKO_CNV/ApcKO-CNV.ipynb

终于找到一篇个性化的CNV分析教程：

单细胞分析实录(12): 如何推断肿瘤细胞

什么是CNV？

copy number variation (CNV)

A copy number variation (CNV) is when the number of copies of a particular gene varies from one individual to the next. Following the completion of the Human Genome Project, it became apparent that the genome experiences gains and losses of genetic material. The extent to which copy number variation contributes to human disease is not yet known. It has long been recognized that some cancers are associated with elevated copy numbers of particular genes.

https://github.com/broadinstitute/inferCNV

想了解算法，可以看作者的PhD thesis：Bioinformatic tool developments with applications to RNA-seq data analysis and clinical cancer research - BRIAN JOHN HAAS - 2021

教程：InferCNV: Inferring copy number alterations from tumor single cell RNA-Seq data

安装

conda install -c conda-forge jags
 
if (!requireNamespace("BiocManager", quietly = TRUE))
     install.packages("BiocManager")
BiocManager::install("infercnv")
 
library(infercnv)

下载github仓库测试数据

git clone https://github.com/broadinstitute/infercnv.git
cd inferCNV/example  
Rscript ./run.R

进入服务器jupyter，查看和准备输入文件：

# create the infercnv object
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=system.file("extdata", "oligodendroglioma_expression_downsampled.counts.matrix.gz", package = "infercnv"),
                                    annotations_file=system.file("extdata", "oligodendroglioma_annotations_downsampled.txt", package = "infercnv"),
                                    delim="\t",
                                    gene_order_file=system.file("extdata", "gencode_downsampled.EXAMPLE_ONLY_DONT_REUSE.txt", package = "infercnv"),
                                    ref_group_names=c("Microglia/Macrophage","Oligodendrocytes (non-malignant)"))

system.file("extdata", "oligodendroglioma_expression_downsampled.counts.matrix.gz", package = "infercnv")
system.file("extdata", "oligodendroglioma_annotations_downsampled.txt", package = "infercnv")
system.file("extdata", "gencode_downsampled.EXAMPLE_ONLY_DONT_REUSE.txt", package = "infercnv")

因为R版本不够，不能在本地装上最新的版本，可以用容器代替。

module load singularity
 
singularity build infercnv.latest.simg docker://trinityctat/infercnv:latest
 
singularity exec -e -B `pwd` infercnv.latest.simg Rscript run.R

需要运行的R脚本

#!/usr/bin/env Rscript
 
options(error = function() traceback(2))
 
packageVersion("infercnv")
library("infercnv")
 
# create the infercnv object
infercnv_obj = CreateInfercnvObject(raw_counts_matrix="~/project/scPipeline/infercnv/cell.count.matrix.txt.gz",
                                    annotations_file="~/project/scPipeline/infercnv/cell.anno.txt",
                                    delim="\t",
                                    gene_order_file="~/project/scPipeline/infercnv/gene.anno.txt",
                                    ref_group_names=c("IMR_ENCC","UE_ENCC"))
 
out_dir="HSCR_CNV"
# perform infercnv operations to reveal cnv signal
infercnv_obj = infercnv::run(infercnv_obj,
                             cutoff=1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
                             out_dir=out_dir,
                             cluster_by_groups=TRUE,
                             plot_steps=FALSE,
                             denoise=TRUE,
                             HMM=TRUE,
                             num_threads=10
                             )

可能不是癌症样本，结果没有那么突出。

结果解读

把基因按染色体的坐标排列，如果某个染色体片段的表达显著提高或减少，则说明其CNV发生了变化。

参考文章：Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma

结果描述：

Normalization of CNV profiles using signal from the ‘normal’ cluster revealed coherent chromosomal aberrations in each tumor (Fig. 1C). Gain of chromosome 7 and loss of chromosome 10, the two most common genetic alterations in glioblastoma (20), were consistently inferred in every tumor cell. Chromosomal aberrations were relatively consistent within tumors, with the exception that MGH31 appears to contain two genetic clones with discordant copy number changes on chromosomes 5, 13 and 14. While this data suggests largescale intratumoral genetic homogeneity, we recognize that heterogeneity generated by focal alterations and point mutations will be grossly underappreciated using this method. Nevertheless, such panoramic analysis of chromosomal landscape effectively separated normal from malignant cells.

待续~

参考：

单细胞转录组数据分析CNV

使用broad出品的inferCNV来对单细胞转录组数据推断CNV信息

The Trinity Cancer Transcriptome Analysis Toolkit (CTAT)