single_cell_RNA/inferCNV.Rmd at master · the8thday/single_cell_RNA · GitHub

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---
title: "inferCNV"
author: "liuc"
date: '2022-04-02'
output: pdf_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## inferCNV

运行inferCNV需要根据单细胞数据制备三个input文件，包括

scRNA-seq表达量的count数据；
表型注释文件，记录肿瘤和正常细胞；
基因或染色体位置文件。

CNV的分析一般还是在肿瘤研究中。


```{r}
library(inferCNV)

```


展现下自带数据集的用法
```{r}
##读取示例数据目录
exprMatrix = system.file("extdata", "oligodendroglioma_expression_downsampled.counts.matrix.gz", package="infercnv")
cellAnnota = system.file("extdata", "oligodendroglioma_annotations_downsampled.txt", package="infercnv")
geneLocate = system.file("extdata", "gencode_downsampled.EXAMPLE_ONLY_DONT_REUSE.txt", package="infercnv")
#创建inferCNV对象，直接给相应的文件路径即可
infercnv_obj = CreateInfercnvObject(delim = '\t',
                  raw_counts_matrix = exprMatrix,
                  annotations_file = cellAnnota,
                  gene_order_file = geneLocate,
                  ref_group_names = c("Microglia/Macrophage","Oligodendrocytes (non-malignant)"))

##分析细胞CNV
#cutoff阈值，Smart-seq2数据选“1”, 10x Genomics数据选“0.1”
infercnv_obj = infercnv::run(infercnv_obj,
                             cutoff=1,
                             out_dir='inferCNV/test',
                             cluster_by_groups=TRUE,
                             denoise=TRUE,
                             HMM=TRUE)
```


Read 10X
```{r}
cellAnnota <- subset(scRNAsclc@meta.data, select='celltype')
exprMatrix <- as.matrix(GetAssayData(scRNAsclc, slot='counts'))


#创建inferCNV对象
#all_celltype: B_cell Monocyte  Neurons  NK_cell  T_cells
infercnv_obj = CreateInfercnvObject(delim = '\t',
                  raw_counts_matrix = 'inferCNV/exprMatrix.txt',
                  annotations_file = 'inferCNV/cellAnnota.txt',
                  gene_order_file = 'inferCNV/geneLocate.txt',
                  ref_group_names = c("B_cell","Monocyte","NK_cell","T_cells"))
dir.create("inferCNV/gse149180")
#10x数据cutoff推荐使用0.1
infercnv_obj = infercnv::run(infercnv_obj,
                             cutoff=0.1,
                             out_dir='inferCNV/gse149180/',
                             cluster_by_groups=TRUE,
                             denoise=TRUE,
                             HMM=TRUE)
```