Seurat - Guided Clustering Tutorial

Seurat_PBMC3k_TENxPBMCData_tutorial.Rmd

---
title: "Seurat PBMC 3k tutorial using TENxPBMCData"
author: "Kevin Rue-Albrecht"
date: "27/11/2018"
output: html_document
editor_options: 
  chunk_output_type: console
---

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

Fetch the SingleCellExperiment object using the `TENxPBMCData`.
The first time download from the web and cache locally; subsequently from the local cache.

```{r}
library(TENxPBMCData)
tenx_pbmc3k <- TENxPBMCData(dataset = "pbmc3k")
tenx_pbmc3k
```

Prepare a sparse matrix that emulates the first section of the tutorial.

```{r}
library(Matrix)
pbmc.data <- as(counts(tenx_pbmc3k), "Matrix")
pbmc.data <- as(pbmc.data, "dgTMatrix")
colnames(pbmc.data) <- paste0("Cell", seq_len(ncol(pbmc.data)))
rownames(pbmc.data) <- make.unique(rowData(tenx_pbmc3k)[, "Symbol_TENx", drop=TRUE])
```

From here on, follow the Seurat tutorial to the letter.

```{r}
library(Seurat)
library(dplyr)
# Initialize the Seurat object with the raw (non-normalized data).  Keep all
# genes expressed in >= 3 cells (~0.1% of the data). Keep all cells with at
# least 200 detected genes
pbmc <- CreateSeuratObject(raw.data = pbmc.data, min.cells = 3, min.genes = 200, project = "10X_PBMC")
```

```{r}
# The number of genes and UMIs (nGene and nUMI) are automatically calculated
# for every object by Seurat.  For non-UMI data, nUMI represents the sum of
# the non-normalized values within a cell We calculate the percentage of
# mitochondrial genes here and store it in percent.mito using AddMetaData.
# We use [email protected] since this represents non-transformed and
# non-log-normalized counts The % of UMI mapping to MT-genes is a common
# scRNA-seq QC metric.
mito.genes <- grep(pattern = "^MT-", x = rownames(x = pbmc@data), value = TRUE)
percent.mito <- Matrix::colSums([email protected][mito.genes, ])/Matrix::colSums([email protected])

# AddMetaData adds columns to [email protected], and is a great place to
# stash QC stats
pbmc <- AddMetaData(object = pbmc, metadata = percent.mito, col.name = "percent.mito")
VlnPlot(object = pbmc, features.plot = c("nGene", "nUMI", "percent.mito"), nCol = 3)
```

```{r}
# GenePlot is typically used to visualize gene-gene relationships, but can
# be used for anything calculated by the object, i.e. columns in
# [email protected], PC scores etc.  Since there is a rare subset of cells
# with an outlier level of high mitochondrial percentage and also low UMI
# content, we filter these as well
par(mfrow = c(1, 2))
GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "percent.mito")
GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "nGene")
```

```{r}
# We filter out cells that have unique gene counts over 2,500 or less than
# 200 Note that low.thresholds and high.thresholds are used to define a
# 'gate'.  -Inf and Inf should be used if you don't want a lower or upper
# threshold.
pbmc <- FilterCells(object = pbmc, subset.names = c("nGene", "percent.mito"), 
    low.thresholds = c(200, -Inf), high.thresholds = c(2500, 0.05))
```

```{r}
pbmc <- NormalizeData(object = pbmc, normalization.method = "LogNormalize", 
    scale.factor = 10000)
```

```{r}
pbmc <- FindVariableGenes(object = pbmc, mean.function = ExpMean, dispersion.function = LogVMR, 
    x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5)
```

```{r}
length(x = [email protected])
```

```{r}
pbmc <- ScaleData(object = pbmc, vars.to.regress = c("nUMI", "percent.mito"))
```

```{r}
pbmc <- RunPCA(object = pbmc, pc.genes = [email protected], do.print = TRUE, pcs.print = 1:5, 
    genes.print = 5)
```

```{r}
# Examine and visualize PCA results a few different ways
PrintPCA(object = pbmc, pcs.print = 1:5, genes.print = 5, use.full = FALSE)
```

```{r}
VizPCA(object = pbmc, pcs.use = 1:2)
```

```{r}
PCAPlot(object = pbmc, dim.1 = 1, dim.2 = 2)
```

```{r}
# ProjectPCA scores each gene in the dataset (including genes not included
# in the PCA) based on their correlation with the calculated components.
# Though we don't use this further here, it can be used to identify markers
# that are strongly correlated with cellular heterogeneity, but may not have
# passed through variable gene selection.  The results of the projected PCA
# can be explored by setting use.full=T in the functions above
pbmc <- ProjectPCA(object = pbmc, do.print = FALSE)
```

```{r}
PCHeatmap(object = pbmc, pc.use = 1, cells.use = 500, do.balanced = TRUE, label.columns = FALSE)
```

```{r}
PCHeatmap(object = pbmc, pc.use = 1:12, cells.use = 500, do.balanced = TRUE, 
    label.columns = FALSE, use.full = FALSE)
```

Small deviation from the tutorial. Skip the lengthy JackStraw computation.

```{r}
# NOTE: This process can take a long time for big datasets, comment out for
# expediency.  More approximate techniques such as those implemented in
# PCElbowPlot() can be used to reduce computation time
if (FALSE) {
    pbmc <- JackStraw(object = pbmc, num.replicate = 100, display.progress = FALSE)
    JackStrawPlot(object = pbmc, PCs = 1:12)
}
```

```{r}
PCElbowPlot(object = pbmc)
```

```{r}
# save.SNN = T saves the SNN so that the clustering algorithm can be rerun
# using the same graph but with a different resolution value (see docs for
# full details)
pbmc <- FindClusters(object = pbmc, reduction.type = "pca", dims.use = 1:10, 
    resolution = 0.6, print.output = 0, save.SNN = TRUE)
```

```{r}
PrintFindClustersParams(object = pbmc)
```

```{r}
pbmc <- RunTSNE(object = pbmc, dims.use = 1:10, do.fast = TRUE)
```

```{r}
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = pbmc)
```

Save the `seurat` object

```{r}
saveRDS(pbmc, file = "pbmc3k_tutorial.rds")
```

Save the equivalent `SingleCellExperiment` object

```{r}
sce_pbmc <- as.SingleCellExperiment(pbmc)
saveRDS(sce_pbmc, file = "pbmc3k_tutorial.sce.rds")
```

Note that the marker genes used to later assign cell identities to the various clusters are listed below

```{r}
## Markers and cell type annotations by cluster
# Cluster ID	Markers	        Cell Type
# 0             IL7R	        CD4 T cells
# 1             CD14, LYZ	    CD14+ Monocytes
# 2	            MS4A1           B cells
# 3	            CD8A	        CD8 T cells
# 4	            FCGR3A, MS4A7	FCGR3A+ Monocytes
# 5	            GNLY, NKG7	    NK cells
# 6	            FCER1A, CST3	Dendritic Cells
# 7	            PPBP	        Megakaryocytes
```