Combining Multiple Single cell RNA-seq Datasets: Part II
In this post, I will try to combine Nowakowski et al., 2017 , BrainSpan and Zhong et al., 2017 datasets.
All of these datasets are publicly available. For more information, please visit the Resources page
First let's combine the Nowakowski and Brainsan datasets.
df0.all <- MergeSeurat(df0.BS, df0.nowa,
min.cells = 10,
do.normalize = T,
scale.factor = 100000)
Normalize both datasets:
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|p1 <- plotGeneDistr("PAX6",object1 = df0.all)
p2 <- plotGeneDistr("BCL11B",object1 = df0.all)
p3 <- plotGeneDistr("GAD1",object1 = df0.all)
p4 <- plotGeneDistr("OLIG1",object1 = df0.all)
plot_grid(p1, p2, p3, p4, nrow = 2)
Find highly variable genes to conduct PCA analysis:
Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Run PCA:
df0.all <- RunPCA(object = df0.all,
pc.genes=df0.all@var.genes,
pcs.compute = 30,
pcs.print = 1:10,
do.print = F,
genes.print = 10)
Pick significant PCs for tSNE and clustering analysis based on elbow plot:
I chose 20 PCs for down stream analysis.
Run tSNE analysis:
df0.all <- RunTSNE(df0.all,
dims.use = 1:20,
perplexity = 30,
seed.use = 1)
df0.all <- FindClusters(object = df0.all,
reduction.type = "pca",
dims.use = 1:20,
resolution = 0.8,
print.output = 0,
save.SNN = F,
force.recalc = T) p1 <- TSNEPlot(object = df0.all,
pt.size = 3,
do.label = T,
label.size = 10,
do.return = T)
p2 <- TSNEPlot(object = df0.all,
group.by = "orig.ident",
do.return = T,
pt.size = 3)
plot_grid(p1, p2, ncol = 2)
source("~/zhen/scripts/R/functions/markers.R")
markers <- unlist(sapply(markers,function(x){grep(x, rownames(df0.all@data), value = T)}))## Plot marker genes
temp <- df0.all@data[markers,]
ord <- order(df0.all@meta.data$res.0.8)
ColSideColors <- c(gg_color_hue(length(unique(df0.all@meta.data$res.0.8)))[as.numeric(as.factor(df0.all@meta.data$res.0.8))],
rainbow(2)[as.numeric(as.factor(df0.all@meta.data$orig.ident))])
ColSideColors <- matrix(data = ColSideColors, nrow = ncol(temp));
ColSideColors <- ColSideColors[ord, ];
colsep <- lapply(unique(df0.all@meta.data$res.0.8[ord]),
function(xx){length(which(df0.all@meta.data$res.0.8 == xx))});
colsep <- cumsum(unlist(colsep));
pairs.breaks <- seq(-2, 2, length.out=101);heatmap.3(temp[, ord],
breaks = pairs.breaks,
#symbreaks = T,
keysize = 0.8,
main="Marker gene expression",
col = gplots::bluered(100),
symkey = F,
cexRow=1, cexCol = 0.6,
Rowv = F,
Colv = F,#as.dendrogram(hca),
ColSideColors = as.matrix(ColSideColors),
ColSideColorsSize = 1,
#RowSideColors = RowSideColors,
dendrogram = "none",
scale = "row",
colsep = colsep,
sepcolor = "black",
labRow = rownames(temp),
labCol = "",
na.rm = F)Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.
Add another dataset: Zhong et al. 2017
Merge Zhong et al dataset to the other two:
df0.all <- MergeSeurat(df0.all, df0.zhong,
min.cells = 10,
do.normalize = T,
scale.factor = 100000)df0.all@meta.data$Time_point2[df0.all@meta.data$Time_point %in%
c("5 pcw", "6 pcw", "8 pcw", "GW08", "GW09","GW10")] <- "W1"
df0.all@meta.data$Time_point2[df0.all@meta.data$Time_point %in%
c("11", "13", "13.3","14","14.2","14.5","14.7","GW12","GW13", "GW16")] <- "W2"
df0.all@meta.data$Time_point2[df0.all@meta.data$Time_point %in%
c("15", "16 pcw", "16.3","17.3","17.5","18.5","GW19")] <- "W3"
df0.all@meta.data$Time_point2[df0.all@meta.data$Time_point %in%
c("19", "19 pcw", "20 pcw","20","21.5","22","GW23","GW26")] <- "W4"
df0.all@meta.data$Time_point2[df0.all@meta.data$Time_point %in% c("32")] <- "W5"Regress out nUMI and different studies.
Look at expression level distribution in normalized data:
Take a look at expression level distribution in scaled data:
Find highly variable genes to conduct PCA analysis:
Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Run PCA:
df0.all <- RunPCA(object = df0.all,
pc.genes=df0.all@var.genes,
pcs.compute = 30,
pcs.print = 1:10,
do.print = F,
genes.print = 10)
Error in irlba(A = t(x = data.use), nv = pcs.compute, ...) :
max(nu, nv) must be positivePick significant PCs for tSNE and clustering analysis based on elbow plot:
I chose 20 PCs for down stream analysis.
Run tSNE analysis:
df0.all <- RunTSNE(df0.all,
dims.use = 1:20,
perplexity = 30,
seed.use = 1)
df0.all <- FindClusters(object = df0.all,
reduction.type = "pca",
dims.use = 1:20,
resolution = 0.8,
print.output = 0,
save.SNN = F,
force.recalc = T) 
source("~/zhen/scripts/R/functions/markers.R")
markers <- unlist(sapply(markers,function(x){grep(x, rownames(df0.all@data), value = T)}))## Plot marker genes
temp <- df0.all@data[markers,]
ord <- order(df0.all@meta.data$res.0.8)
ColSideColors <- c(gg_color_hue(length(unique(df0.all@meta.data$res.0.8)))[as.numeric(as.factor(df0.all@meta.data$res.0.8))],
rainbow(3)[as.numeric(as.factor(df0.all@meta.data$orig.ident))])
ColSideColors <- matrix(data = ColSideColors, nrow = ncol(temp));
ColSideColors <- ColSideColors[ord, ];
colsep <- lapply(unique(df0.all@meta.data$res.0.8[ord]),
function(xx){length(which(df0.all@meta.data$res.0.8 == xx))});
colsep <- cumsum(unlist(colsep));
pairs.breaks <- seq(-2, 2, length.out=101);heatmap.3(temp[, ord],
breaks = pairs.breaks,
key = F,
#symbreaks = T,
keysize = 0.8,
main="Marker gene expression",
col = gplots::bluered(100),
symkey = F,
cexRow=1, cexCol = 0.6,
Rowv = F,
Colv = F,#as.dendrogram(hca),
ColSideColors = as.matrix(ColSideColors),
ColSideColorsSize = 1,
#RowSideColors = RowSideColors,
dendrogram = "none",
scale = "row",
colsep = colsep,
sepcolor = "black",
labRow = rownames(temp),
labCol = "",
na.rm = F)Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.
---
title: "2018-10-24"
output: html_notebook
---
> Combine Nowakowski and BrainSpan dataset for greater cell number to do classification

```{r include=F}
setwd("~/zhen/data/hOrg/")
load("~/zhen/data/hOrg/nowa_BS.RData")
library(Seurat)
source("~/zhen/scripts/R/functions/plotGeneDistr.R")
# df0.mic1707.n <- readRDS("~/zhen/data/hOrg/df0.mic1707.n.rds")
# df0.mic1707.d <- readRDS("~/zhen/data/hOrg/df0.mic1707.d.rds")
```

Normalize both datasets:

```{r}
df0.all <- MergeSeurat(df0.BS, df0.nowa,
                       min.cells = 10,
                       do.normalize = T,
                       scale.factor = 100000)

df0.all <- ScaleData(df0.all, vars.to.regress = "nUMI")
```

```{r warning=FALSE, message=FALSE, fig.width=16, fig.height=16}
p1 <- plotGeneDistr("PAX6",object1 = df0.all)
p2 <- plotGeneDistr("BCL11B",object1 = df0.all)
p3 <- plotGeneDistr("GAD1",object1 = df0.all)
p4 <- plotGeneDistr("OLIG1",object1 = df0.all)
plot_grid(p1, p2, p3, p4, nrow = 2)
```
Find highly variable genes to conduct PCA analysis:
```{r echo=F, fig.height=7, fig.width=7}
df0.all <- FindVariableGenes(df0.all, x.low.cutoff = 0.5)
```

Run PCA:
```{r}
df0.all <- RunPCA(object = df0.all,
                  pc.genes=df0.all@var.genes, 
                  pcs.compute = 30, 
                  pcs.print = 1:10,
                  do.print = F, 
                  genes.print = 10)
```

```{r echo=F, fig.height=7, fig.width=7}
p1 <- PCAPlot(df0.all, group.by = "orig.ident", do.return = T)
p1 <- p1 + theme(legend.position = c(0.1, 0.9))
p1
```

Pick significant PCs for tSNE and clustering analysis based on elbow plot:

```{r echo=F}
PCElbowPlot(df0.all,num.pc = 30)
```

I chose 20 PCs for down stream analysis.

Run tSNE analysis:
```{r results='hide', message=F}
df0.all <- RunTSNE(df0.all, 
               dims.use = 1:20, 
               perplexity = 30, 
               seed.use = 1)

df0.all <- FindClusters(object = df0.all, 
                    reduction.type = "pca", 
                    dims.use = 1:20, 
                    resolution = 0.8, 
                    print.output = 0, 
                    save.SNN = F, 
                    force.recalc = T) 
```

```{r fig.height=7, fig.width=14}
p1 <- TSNEPlot(object = df0.all, 
               pt.size = 3,
               do.label = T,
               label.size = 10, 
               do.return = T)
p2 <- TSNEPlot(object = df0.all, 
               group.by = "orig.ident", 
               do.return = T, 
               pt.size = 3)

plot_grid(p1, p2, ncol = 2)

```

```{r}
source("~/zhen/scripts/R/functions/markers.R")
markers <- unlist(sapply(markers,function(x){grep(x, rownames(df0.all@data), value = T)}))
```

```{r}
## Plot marker genes
temp <- df0.all@data[markers,]

ord <- order(df0.all@meta.data$res.0.8)
ColSideColors <- c(gg_color_hue(length(unique(df0.all@meta.data$res.0.8)))[as.numeric(as.factor(df0.all@meta.data$res.0.8))],
rainbow(2)[as.numeric(as.factor(df0.all@meta.data$orig.ident))])

ColSideColors <- matrix(data = ColSideColors, nrow = ncol(temp));
ColSideColors <- ColSideColors[ord, ];

colsep <- lapply(unique(df0.all@meta.data$res.0.8[ord]), 
function(xx){length(which(df0.all@meta.data$res.0.8 == xx))});
colsep <- cumsum(unlist(colsep));  

pairs.breaks <- seq(-2, 2, length.out=101);
```

```{r fig.height=15, fig.width=7}
heatmap.3(temp[, ord],
          breaks = pairs.breaks,
          #symbreaks = T,
          keysize = 0.8, 
          main="Marker gene expression",
          col = gplots::bluered(100),
          symkey = F,
          cexRow=1, cexCol = 0.6, 
          Rowv = F, 
          Colv = F,#as.dendrogram(hca), 
          ColSideColors = as.matrix(ColSideColors),
          ColSideColorsSize = 1,
          #RowSideColors = RowSideColors,
          dendrogram = "none",
          scale = "row",
          colsep = colsep,
          sepcolor = "black",
          labRow = rownames(temp), 
          labCol = "",
          na.rm = F)
```

Add another dataset? Zhong et al. 2017
```{r}
df.zhong <- read.table("~/zhen/data/hOrg/raw_data/GSE104276_all_pfc_2394_UMI_count_NOERCC.xls")


MT <- grep("^MT-", rownames(df.zhong))
RP <- grep("^RP", rownames(df.zhong))

rownames(df.zhong) <- gsub("\\-", "\\.",rownames(df.zhong))

df0.zhong <- CreateSeuratObject(as.matrix(df.zhong[ -c(MT,RP), ]), 
                          project = "Zhong",
                          display.progress = F)
temp <- strsplit(df0.zhong@cell.names, split = "_")
df0.zhong@meta.data$Time_point <- sapply(temp,  function(xx) xx[1])
df0.zhong@meta.data$Area_ <- sapply(temp,  function(xx) xx[2])
df0.zhong@meta.data$orig.ident <- "zhong"
```

Merge Zhong et al dataset to the other two:
```{r}
df0.all <- MergeSeurat(df0.all, df0.zhong,
                       min.cells = 10,
                       do.normalize = T,
                       scale.factor = 100000)
```

```{r}
df0.all@meta.data$Time_point2[df0.all@meta.data$Time_point %in% 
                                    c("5 pcw", "6 pcw", "8 pcw", "GW08", "GW09","GW10")] <- "W1"
df0.all@meta.data$Time_point2[df0.all@meta.data$Time_point %in% 
                                    c("11", "13", "13.3","14","14.2","14.5","14.7","GW12","GW13", "GW16")] <- "W2"
df0.all@meta.data$Time_point2[df0.all@meta.data$Time_point %in% 
                                    c("15", "16 pcw", "16.3","17.3","17.5","18.5","GW19")] <- "W3"
df0.all@meta.data$Time_point2[df0.all@meta.data$Time_point %in% 
                                    c("19", "19 pcw", "20 pcw","20","21.5","22","GW23","GW26")] <- "W4"
df0.all@meta.data$Time_point2[df0.all@meta.data$Time_point %in% c("32")] <- "W5"

```


Regress out nUMI and different studies.
```{r}
df0.all <- ScaleData(df0.all, 
                     vars.to.regress = c("nUMI", "orig.ident"),
                     display.progress = F)
```

Look at expression level distribution in normalized data:
```{r echo=F, fig.width=16, fig.height=16}
p1 <- plotGeneDistr("PAX6",object1 = df0.all)
p2 <- plotGeneDistr("BCL11B",object1 = df0.all)
p3 <- plotGeneDistr("GAD1",object1 = df0.all)
p4 <- plotGeneDistr("OLIG1",object1 = df0.all)
plot_grid(p1, p2, p3, p4, nrow = 2)
```

Take a look at expression level distribution in scaled data:
```{r echo=F, fig.width=16, fig.height=16}
p1 <- plotGeneDistr("PAX6",object1 = df0.all, data.type = "scaled")
p2 <- plotGeneDistr("BCL11B",object1 = df0.all, data.type = "scaled")
p3 <- plotGeneDistr("GAD1",object1 = df0.all, data.type = "scaled")
p4 <- plotGeneDistr("OLIG1",object1 = df0.all, data.type = "scaled")
plot_grid(p1, p2, p3, p4, nrow = 2)
```

Find highly variable genes to conduct PCA analysis:
```{r echo=F, fig.height=7, fig.width=7}
df0.all <- FindVariableGenes(df0.all, x.low.cutoff = 0.5, y.cutoff = 0)
```

Run PCA:
```{r}
df0.all <- RunPCA(object = df0.all,
                  pc.genes=df0.all@var.genes, 
                  pcs.compute = 30, 
                  pcs.print = 1:10,
                  do.print = F, 
                  genes.print = 10)
```

```{r echo=F, fig.height=7, fig.width=7}
p1 <- PCAPlot(df0.all, group.by = "orig.ident", do.return = T)
p1 <- p1 + theme(legend.position = c(0.1, 0.9))
p1
```

Pick significant PCs for tSNE and clustering analysis based on elbow plot:

```{r echo=F}
PCElbowPlot(df0.all,num.pc = 30)
```

I chose 20 PCs for down stream analysis.

Run tSNE analysis:
```{r results='hide', message=F}
df0.all <- RunTSNE(df0.all, 
               dims.use = 1:20, 
               perplexity = 30, 
               seed.use = 1)

df0.all <- FindClusters(object = df0.all, 
                    reduction.type = "pca", 
                    dims.use = 1:20, 
                    resolution = 0.8, 
                    print.output = 0, 
                    save.SNN = F, 
                    force.recalc = T) 
```

```{r fig.height=7, fig.width=21}
p1 <- TSNEPlot(object = df0.all, 
               pt.size = 3,
               do.label = T,
               label.size = 10, 
               do.return = T)
p2 <- TSNEPlot(object = df0.all, 
               group.by = "orig.ident", 
               do.return = T, 
               pt.size = 3)
p3 <- TSNEPlot(object = df0.all, 
               group.by = "Time_point2", 
               do.return = T, 
               pt.size = 3)

plot_grid(p1, p2, p3, ncol = 3)
```

```{r}
source("~/zhen/scripts/R/functions/markers.R")
markers <- unlist(sapply(markers,function(x){grep(x, rownames(df0.all@data), value = T)}))
```

```{r}
## Plot marker genes
temp <- df0.all@data[markers,]

ord <- order(df0.all@meta.data$res.0.8)
ColSideColors <- c(gg_color_hue(length(unique(df0.all@meta.data$res.0.8)))[as.numeric(as.factor(df0.all@meta.data$res.0.8))],
rainbow(3)[as.numeric(as.factor(df0.all@meta.data$orig.ident))])

ColSideColors <- matrix(data = ColSideColors, nrow = ncol(temp));
ColSideColors <- ColSideColors[ord, ];

colsep <- lapply(unique(df0.all@meta.data$res.0.8[ord]), 
function(xx){length(which(df0.all@meta.data$res.0.8 == xx))});
colsep <- cumsum(unlist(colsep));  

pairs.breaks <- seq(-2, 2, length.out=101);
```

```{r fig.height=15, fig.width=14}
heatmap.3(temp[, ord],
          breaks = pairs.breaks,
          key = F,
          #symbreaks = T,
          keysize = 0.8, 
          main="Marker gene expression",
          col = gplots::bluered(100),
          symkey = F,
          cexRow=1, cexCol = 0.6, 
          Rowv = F, 
          Colv = F,#as.dendrogram(hca), 
          ColSideColors = as.matrix(ColSideColors),
          ColSideColorsSize = 1,
          #RowSideColors = RowSideColors,
          dendrogram = "none",
          scale = "row",
          colsep = colsep,
          sepcolor = "black",
          labRow = rownames(temp), 
          labCol = "",
          na.rm = F)
```

> This looks pretty good. Can I add more datasets? 
