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step0-get-expression-matrix.Rmd
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---
title: "step0-get-expression-matrix"
author: "[email protected]"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(warning = F)
knitr::opts_chunk$set(message = F)
```
## 引言
因为作者在GEO上面公布了自己处理好的表达矩阵
所以自己下载sra文件，转fq格式，走cellranger流程并不是必须
这里简要展示多个10x的cellranger结果如何整合，值得注意的是，因为时间关系，我们这里并没有去用心探索最佳参数。
因为文章使用的是Seurat2.0，所以这里就展示Seurat2.0的用法
## 读取数据
```{r}
library(Seurat)
sce.10x <- Read10X(data.dir = '~/Documents/10x/output/four-PBMC-mtx/SRR7722939/')
sce1 <- CreateSeuratObject(raw.data = sce.10x,
min.cells = 60,
min.genes = 200,
project = "SRR7722939")
sce1
sce.10x <- Read10X(data.dir = '~/Documents/10x/output/four-PBMC-mtx/SRR7722940/')
sce2 <- CreateSeuratObject(raw.data = sce.10x,
min.cells = 60,
min.genes = 200,
project = "SRR7722940")
sce2
sce.10x <- Read10X(data.dir = '~/Documents/10x/output/four-PBMC-mtx/SRR7722941/')
sce3 <- CreateSeuratObject(raw.data = sce.10x,
min.cells = 60,
min.genes = 200,
project = "SRR7722941")
sce3
sce.10x <- Read10X(data.dir = '~/Documents/10x/output/four-PBMC-mtx/SRR7722942/')
sce4 <- CreateSeuratObject(raw.data = sce.10x,
min.cells = 60,
min.genes = 200,
project = "SRR7722942")
sce4
sce1;sce2;sce3;sce4
```
这四个数据分别对应了四个时间点：治疗前(Pre)，治疗后早期day +27(Early)，治疗后反应期day+37(Resp)，治疗后复发+614 (AR)
timePoints
PBMC_ARD614 PBMC_EarlyD27 PBMC_Pre PBMC_RespD376
4516 1592 2082 4684
| GSM3330561 | PBMC Pre |
| GSM3330562 | PBMC Disc Early |
| GSM3330563 | PBMC Disc Resp |
| GSM3330564 | PBMC Disc AR |
```{r}
[email protected]$group <- "PBMC_Pre"
[email protected]$group <- "PBMC_EarlyD27"
[email protected]$group <- "PBMC_RespD376"
[email protected]$group <- "PBMC_ARD614"
head(colnames(sce1@data))
colnames(sce1@data) <- paste0("PBMC_Pre.",colnames(sce1@data))
head(colnames(sce1@data))
colnames(sce2@data) <- paste0("PBMC_EarlyD27.",colnames(sce2@data))
colnames(sce3@data) <- paste0("PBMC_RespD376.",colnames(sce3@data))
colnames(sce4@data) <- paste0("PBMC_ARD614.",colnames(sce4@data))
```
## Set up
```{r}
sce1 <- NormalizeData(sce1)
sce1 <- ScaleData(sce1, display.progress = F)
sce2 <- NormalizeData(sce2)
sce2 <- ScaleData(sce2, display.progress = F)
sce3 <- NormalizeData(sce3)
sce3 <- ScaleData(sce3, display.progress = F)
sce4 <- NormalizeData(sce4)
sce4 <- ScaleData(sce4, display.progress = F)
```
## Gene selection for input to CCA
```{r}
sce1 <- FindVariableGenes(sce1, do.plot = F)
sce2 <- FindVariableGenes(sce2, do.plot = F)
sce3 <- FindVariableGenes(sce3, do.plot = F)
sce4 <- FindVariableGenes(sce4, do.plot = F)
g.1 <- head(rownames([email protected]), 1000)
g.2 <- head(rownames([email protected]), 1000)
g.3 <- head(rownames([email protected]), 1000)
g.4 <- head(rownames([email protected]), 1000)
genes.use <- unique(c(g.1, g.2,g.3, g.4))
genes.use <- intersect(genes.use, rownames([email protected]))
genes.use <- intersect(genes.use, rownames([email protected]))
genes.use <- intersect(genes.use, rownames([email protected]))
genes.use <- intersect(genes.use, rownames([email protected]))
head(genes.use)
length(genes.use)
```
通过上面的操作，找到了4个数据集共同重要的基因，然后进行后续合并分析。
```{r}
head(colnames(sce1@data))
colnames(sce1@data) <- paste0("PBMC_Pre.",colnames(sce1@data))
colnames(sce2@data) <- paste0("PBMC_EarlyD27.",colnames(sce2@data))
colnames(sce3@data) <- paste0("PBMC_RespD376.",colnames(sce3@data))
colnames(sce4@data) <- paste0("PBMC_ARD614.",colnames(sce4@data))
head(colnames([email protected]))
colnames([email protected]) <- paste0("PBMC_Pre.",colnames(sce1@data))
colnames([email protected]) <- paste0("PBMC_EarlyD27.",colnames(sce2@data))
colnames([email protected]) <- paste0("PBMC_RespD376.",colnames(sce3@data))
colnames([email protected]) <- paste0("PBMC_ARD614.",colnames(sce4@data))
```
Duplicate cell names, please provide 'add.cell.id1' and/or 'add.cell.id2' for unique names
这一步耗时很夸张, 保守估计10分钟。
重点就是 RunMultiCCA 函数，做 10X 数据集的合并。
```{r}
start_time <- Sys.time()
sce.comb <- RunMultiCCA(list(sce1,sce2,sce3,sce4),
add.cell.ids=c("PBMC_Pre.","PBMC_EarlyD27.","PBMC_RespD376.","PBMC_ARD614."),
genes.use = genes.use,
num.cc = 30)
end_time <- Sys.time()
end_time - start_time
# save(sce.comb,file = 'patient1-PBMAC.sce.comb.Rdata')
```
visualize results of CCA plot CC1 versus CC2 and look at a violin plot
可以检查一下CCA, 类比PCA分析。
```{r}
p1 <- DimPlot(object = sce.comb,
reduction.use = "cca", group.by = "group",
pt.size = 0.5, do.return = TRUE)
p2 <- VlnPlot(object = sce.comb,
features.plot = "CC1",
group.by = "group",
do.return = TRUE)
plot_grid(p1, p2)
#ggsave('patient1-PBMAC.sce.comb_explore_CC.pdf')
PrintDim(object = sce.comb, reduction.type = "cca",
dims.print = 1:2,
genes.print = 10)
```
类比于PCA分析里面的JackStraw和PCElbowPlot查看挑选多个主成分合适。
耗时也很验证，五分钟左右。
```{r}
start_time <- Sys.time()
p3 <- MetageneBicorPlot(sce.comb, grouping.var = "group", dims.eval = 1:30,
display.progress = FALSE)
p3
end_time <- Sys.time()
end_time - start_time
# ggsave('patient1-PBMAC.sce.comb_MetageneBicorPlot.pdf')
DimHeatmap(object = sce.comb, reduction.type = "cca", cells.use = 500,
dim.use = 1:9, do.balanced = TRUE)
# ggsave('patient1-PBMAC.sce.comb_DimHeatmap.pdf')
```
有进度条报告测序运行情况，也需要五分钟。
```{r }
start_time <- Sys.time()
sce.comb <- AlignSubspace(sce.comb, reduction.type = "cca",
grouping.var = "group", verbose =F,
dims.align = 1:20)
end_time <- Sys.time()
end_time - start_time
p1 <- VlnPlot(object = sce.comb, features.plot = "ACC1", group.by = "group",
do.return = TRUE)
p2 <- VlnPlot(object = sce.comb, features.plot = "ACC2", group.by = "group",
do.return = TRUE)
plot_grid(p1, p2)
# ggsave('patient1-PBMAC.sce.comb_ACC.pdf')
```
## t-SNE and Clustering
```{r}
sce.comb <- RunTSNE(sce.comb, reduction.use = "cca.aligned", dims.use = 1:20,
do.fast = T)
sce.comb <- FindClusters(sce.comb, reduction.type = "cca.aligned",
resolution = 1, dims.use = 1:20,print.output = 0)
```
## Visualization
```{r}
p1 <- TSNEPlot(sce.comb, do.return = T, pt.size = 0.5, group.by = "group")
p2 <- TSNEPlot(sce.comb, do.label = T, do.return = T, pt.size = 0.5)
plot_grid(p1, p2)
# ggsave('patient1-PBMAC.sce.comb_tSNE.pdf')
table([email protected]$res.1,[email protected]$group)
# save(sce.comb,file = 'patient1-PBMAC.sce.comb.second.Rdata')
```