test-1.Rmd

author: "Xiaoyu"
date: "2/12/2020"
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) 
```
```{r}
library(Seurat)
library(tidyverse)
library(dplyr)
library(stringr)
aggr.data <- Read10X(data.dir = "aggr")
aggr <- CreateSeuratObject(counts = aggr.data, project = "aggr", min.cells = 3, min.features = 200)
aggr

timePoints <- sapply(colnames(aggr), function(x) unlist(strsplit(x, "\\-"))[2]) 
timePoints <-ifelse(timePoints == '1', 'Day_2', 
                    ifelse(timePoints == '2', 'Day_4',
                           ifelse(timePoints == '3', 'Day_3', 'Day_0')))
table(timePoints)
aggr <- AddMetaData(object = aggr, metadata = timePoints, col.name = 'TimePoints')
table(aggr@meta.data$TimePoints)

aggr[["percent.mt"]] <- PercentageFeatureSet(aggr, pattern = "^mt-")
VlnPlot(aggr, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, group.by = 'TimePoints')
aggr <- subset(aggr, subset = nFeature_RNA > 1000 & nFeature_RNA < 9000 & percent.mt < 25)
dim(aggr)
aggr <- NormalizeData(aggr, normalization.method = "LogNormalize", scale.factor = 10000)
all.genes <- rownames(aggr)
aggr <- ScaleData(aggr,features = all.genes)
aggr <- FindVariableFeatures(aggr, selection.method = "vst", nfeatures = 5000)
aggr <- RunPCA(aggr, features = VariableFeatures(object = aggr), verbose = FALSE)
aggr<- RunTSNE(aggr, dims = 1:20)
DimPlot(aggr, reduction = "tsne", label = TRUE, group.by = 'TimePoints')
FeaturePlot(aggr, features = c("Ddx4", "Gfra1", "Stra8", "Spo11", "Dmc1", "Spata22", "Meiob"), reduction = "tsne")
```