Aim

Analyse 10x Genomics* single cell RNA-Seq data.

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Aim

Analyse 10x Genomics* single cell RNA-Seq data.

This analysis workflow is split into 2 main sections: 1) ‘Upstream’ analysis on QUT’s HPC (high performance compute cluster) using aNextflow workflow, nfcore/scrnaseq, 2) ‘Downstream’ analysis in R, primarily using the package seurat.

*Note: this workflow can be adapted to work with scRNA-Seq datasets generated by other sequencing technologies than 10x Genomics.

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1. Open RStudio (you can type it in the Windows search bar)

2. Create a new R script: ‘File’ → “New File” → “R script”

3. Save this script where your samples folders are (‘File’ → ‘Save’). These should be on your H or W drive. Save the script file as ‘scrnaseq.R’

In the following sections you will be copying and running the R code into your scrnaseq.R script.

Cell Ranger (and nfcore/scrnaseq) generates a default folder and file output structure. There will be a main folder that contains all the sample subfolders (NOTE: this is where you must save your R script). Each sample folder will have an ‘outs’ subfolder. This ‘outs’ folder contains a ‘filtered_feature_bc_matrix’ folder, which contains the files that Seurat uses in its analysis.

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You can manually set your working directory in RStudio by selecting ‘Session' -> 'Set working directory' -> 'Choose directory'. Choose the same directory as you saved your scrnaseq.R script, previous section. This will output the setwd(...) command with your working directory into the console window (bottom left panel). Copy this command to replace the default setwd(...) line in your R script.

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In this section 3 we’ll be using a variety of Seurat functions to analyse our scRNA-Seq data.

3a. Select a sample to work with and import the data into Rto work with and import the data into R

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#TO DO: Make sure the folder structure provided follows the Cell ranger output format.

├── filtered_feature_bc_matrix
    │   ├── barcodes.tsv.gz
    │   ├── features.tsv.gz
    │   └── matrix.mtx.gz

Clarify how the files will be output from the upstream Nextflow pipeline and update text accordingly to guide to the output folder.

mat <- Read10X(data.dir = "H:/nfcore-scrnaseq_R_analysis/test_data/Choroid/filtered_feature_bc_matrix"

################################################################

In this section we’ll choose one of our samples to analyse, then import that sample’s scRNA-Seq dataset into R. You can re-run this, and following sections, for each sample dataset that you have. Just replace the sample name in sample <- "xxxxxx" below.

#### 3a. Choose a sample to work with and import the data for that sample into R ####

# Give the sample name here that you want to work with.
# To see the available samples (choose a sample name from this list):
list.dirs(full.names = F, recursive = F)
## **USER INPUT**
sample <- "Cerebellum"


# Use Seurat's 'Read10X()' function to read in the full sample database. Cell Ranger creates 3 main database files that need to be combined into a single Seurat object.
# Note: these datasets can be very large and take several minutes to import into R. 
mat <- Read10X(data.dir = paste0(sample, "/outs/filtered_feature_bc_matrix"))

# Have a look at the top 10 rows and columns to see if the data has been imported correctly. You should see gene IDs as rows and barcodes (i.e. cells) as columns
as.matrix(mat[1:10, 1:10])

# Now convert this to a Seurat object. Again, this may take several minutes to load and use a lot of memory
mat2 <- CreateSeuratObject(counts = mat, project = sample)

# You can see a summary of the data by simply running the Seurat object name
mat2

# Set a colour palette that can contrast multiple clusters when you plot them. 
# You can change these colours as you like. 
# You can see what R colours are available here: http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
c25 <- c("dodgerblue2", "#E31A1C", "green4",  "#6A3D9A", "#FF7F00", "black", "gold1", "skyblue2", "#FB9A99", "palegreen2", "#CAB2D6", "#FDBF6F", "gray70", "khaki2", "maroon", "orchid1", "deeppink1", "blue1", "steelblue4", "darkturquoise", "green1", "yellow4", "yellow3", "darkorange4", "brown")

3b. Identify markers in cells

Now we're going to identify individual markers that were present (i.e. were expressed) in our dataset.

IMPORTANT: the gene symbols you provide in the following section have to exactly match the gene symbols in your dataset (including capitalisation).
Gene symbols are more like 'common names' and can vary between databases.
Your main gene identifiers are Ensembl IDs and we need to find the gene symbols that match these Ensembl IDs.
For example, the gene P2ry12 is also called ADPG-R, BDPLT8, HORK3 and various other IDs, depending on the database it's listed in.
In the Ensemble database it's listed as P2ry12 (not P2RY12, remember, case matters) and matches Ensembl ID ENSMUSG00000036353.
For this reason it's advisable to first search the Ensembl website for your markers of interest and for your organism, to ensure you are providing gene symbols that match the Ensembl IDs.
https://asia.ensembl.org/Mus_musculus/Info/Index

#### 3b. Identify markers in cells ####

# Create a vector called 'markers' that contains each of the markers you want to examine. 
# These should be gene symbols. Replace the gene symbols below with your target markers.
## **USER INPUT**
markers <- c("P2ry12", "Tmem119", "Itgam")

# You can see if the markers you provided are present:
sum(e. cells) as columns
as.matrix(mat[1:10, 1:10])

# Now convert this to a Seurat object. Again, this may take several minutes to load and use a lot of memory
mat2 <- CreateSeuratObject(counts = mat, project = sample)

# You can see a summary of the data by simply running the Seurat object name
mat2

# Set a colour palette that can contrast multiple clusters when you plot them. 
# You can change these colours as you like. 
# You can see what R colours are available here: http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
c25 <- c("dodgerblue2", "#E31A1C", "green4",  "#6A3D9A", "#FF7F00", "black", "gold1", "skyblue2", "#FB9A99", "palegreen2", "#CAB2D6", "#FDBF6F", "gray70", "khaki2", "maroon", "orchid1", "deeppink1", "blue1", "steelblue4", "darkturquoise", "green1", "yellow4", "yellow3", "darkorange4", "brown")

3b. Identify markers in cells

Now we're going to identify individual markers that were present (i.e. were expressed) in our dataset.

IMPORTANT: the gene symbols you provide in the following section have to exactly match the gene symbols in your dataset (including capitalisation).
Gene symbols are more like 'common names' and can vary between databases.
Your main gene identifiers are Ensembl IDs and we need to find the gene symbols that match these Ensembl IDs.
For example, the gene P2ry12 is also called ADPG-R, BDPLT8, HORK3 and various other IDs, depending on the database it's listed in.
In the Ensemble database it's listed as P2ry12 (not P2RY12, remember, case matters) and matches Ensembl ID ENSMUSG00000036353.
For this reason it's advisable to first search the Ensembl website for your markers of interest and for your organism, to ensure you are providing gene symbols that match the Ensembl IDs.
https://asia.ensembl.org/Mus_musculus/Info/Index

#### 3b. Identify markers in cells ####

# Create a vector called 'markers' that contains each of the markers you want to examine. 
# These should be gene symbols. Replace the gene symbols below with your target markers.
## **USER INPUT**
markers <- c("P2ry12", "Tmem119", "Itgam")

# You can see if the markers you provided are present:
sum(row.names(mat) %in% markers)
# If you input 3 markers and the output from the above code = 3, then all are present. 
# If the result is 2 then 2 of the 3 markers you provided are found in your data, etc.

## USER INPUT
# You can see if an individual marker is present like so (substitute for a marker of choice):
sum(row.names(mat) == "P2ry12")
# Outputs 1 if the marker is present, 0 if it isn't

# Pull out just the read counts for your defined markers
y <- mat[row.names(mat) %in% markers), #]
If
you# inputNow 3we markerscan andcount the number outputof fromcells thecontaining abovezero codetranscripts =for 3,each thenof allthe areexamined presentmarkers. 
# IfThis theenables resultan isexamination 2of thenthe 2number of the 3 markerscells youthat providedhave arezero foundexpression infor yourthese datamarkers,
etc.# and therefore ##the USERnumber INPUTof #cells Youthat can seebe ifconsidered an individual marker is present like so (substitute for a marker of choice):
sum(row.names(mat) == "P2ry12")
# Outputs 1 if the marker is present, 0 if it isn't

# Pull out just the read counts for your defined markers
y <- mat[row.names(mat) %in% markers, ]

# Now we can count the number of cells containing zero transcripts for each of the examined markers. 
# This enables an examination of the number of cells that have zero expression for these markers,
# and therefore the number of cells that can be considered non-target cells.

# First count all cells
# Then make a loop to cycle through all markers (defined in previously created 'markers' vector)
a <- length(colnames(y))
for (i in 1:length(markers)) {
  
  a <- c(a, sum(y[i,] == 0))
  
}
# Do a sum of the columns
y2 <- colSums(y)
# See if any zeros. If so, these cells are not target cells (as determined by absence of any target cell markers)
count <- c(a, sum(y2 == 0))
# Name the vector elements
names(count) <- c("Total_cells", markers, "All_zero")
# Generate the table
as.data.frame(count)

# The generated table shows:
# 1) the total number of cells for your sample ("Total_cells" row), 
# 2) the number of cells which had 0 expression for each marker, and,
# 3) the number of cell that had zero expression for all of the markers you provided ("All_zero" row).

3c. Processing expression data (dimensionality reduction)

There are a variety of methods to visualise expression in single cell data. The most commonly used methods - PCA, t-SNE and UMAP - involve 'dimensionality', i.e. converting expression to x-n dimensions (which can then be plotted) based on gene expression per cell.

Seurat can generate and store PCA, t-SNE and UMAP data in the Seurat object we created previously ('mat2'), but first the raw data needs to be processed in a variety of ways:

1. Normalise the data by log transformation

2. Identify genes that exhibit high cell-to-cell variation

3. Scale the data so that highly expressed genes don't dominate the visual representation of expression

4. Perform the linear dimensional reduction that converts expression to dimensions

5. Plot the x-y dimension data (i.e. first 2 dimensions)

The first 4 steps are completed in the code below (this may take a few minutes to run)

#### 3c. Processing expression data (dimensionality reduction) ####

# Normalise data
mat3 <- NormalizeData(mat2)

# Identification of variable features
mat3 <- FindVariableFeatures(mat3, selection.method = "vst", nfeatures = nrow(mat3))

# Scaling the data
all.genes <- rownames(mat3)
mat3 <- ScaleData(mat3, features = all.genes)

# Perform linear dimensional reduction (PCA. This may take several minutes to run)
mat3 <- RunPCA(mat3, features = VariableFeatures(object = mat3))

...

non-target cells.

# First count all cells
# Then make a loop to cycle through all markers (defined in previously created 'markers' vector)
a <- length(colnames(y))
for (i in 1:length(markers)) {
  
  a <- c(a, sum(y[i,] == 0))
  
}
# Do a sum of the columns
y2 <- colSums(y)
# See if any zeros. If so, these cells are not target cells (as determined by absence of any target cell markers)
count <- c(a, sum(y2 == 0))
# Name the vector elements
names(count) <- c("Total_cells", markers, "All_zero")
# Generate the table
as.data.frame(count)

# The generated table shows:
# 1) the total number of cells for your sample ("Total_cells" row), 
# 2) the number of cells which had 0 expression for each marker, and,
# 3) the number of cell that had zero expression for all of the markers you provided ("All_zero" row).

3c. Processing expression data (dimensionality reduction)

There are a variety of methods to visualise expression in single cell data. The most commonly used methods - PCA, t-SNE and UMAP - involve 'dimensionality', i.e. converting expression to x-n dimensions (which can then be plotted) based on gene expression per cell.

Seurat can generate and store PCA, t-SNE and UMAP data in the Seurat object we created previously ('mat2'), but first the raw data needs to be processed in a variety of ways:

1. Normalise the data by log transformation

2. Identify genes that exhibit high cell-to-cell variation

3. Scale the data so that highly expressed genes don't dominate the visual representation of expression

4. Perform the linear dimensional reduction that converts expression to dimensions

5. Plot the x-y dimension data (i.e. first 2 dimensions)

The first 4 steps are completed in the code below (this may take a few minutes to run)

#### 3c. Processing expression data (dimensionality reduction) ####

# Normalise data
mat3 <- NormalizeData(mat2)

# Identification of variable features
mat3 <- FindVariableFeatures(mat3, selection.method = "vst", nfeatures = nrow(mat3))

# Scaling the data
all.genes <- rownames(mat3)
mat3 <- ScaleData(mat3, features = all.genes)

# Perform linear dimensional reduction (PCA. This may take several minutes to run)
mat3 <- RunPCA(mat3, features = VariableFeatures(object = mat3))

3d. Plot of highly variable genes

################################################################

Question for Paul:

The following command returns a warning.

p <- LabelPoints(plot = p, points = top_genes, repel = TRUE) +
  theme_bw() +
  theme(text = element_text(size = 17))
p

Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session

################################################################

Using the FindVariableFeatures results, we can visualise the most highly variable genes, including a count of variable and non variable genes in your dataset. The below code outputs the top 10 genes, but you can adjust this number as desired (i.e. in top_genes <- head(VariableFeatures(mat3), 10) change 10 to another number).

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#### 3d. Plot of highly variable genes ####

# Identify the 10 most highly variable genes
top_genes <- head(VariableFeatures(mat3), 10)

# plot variable features with labels
p <- VariableFeaturePlot(mat3, pt.size = 2, cols = c("black", "firebrick"))
p <- LabelPoints(plot = p, points = top_genes, repel = TRUE) +
theme_bw() +
theme(text = element_text(size = 17))
p

# You can save your plot as a 300dpi (i.e. publication quality) tiff or pdf file. 
# These files can be found in your working directory.
# You can adjust the width and height of the saved images by changing width = and height = in the code below.

# Export as a 300dpi tiff
tiff_exp <- paste0(sample, "_top_genes.tiff")
ggsave(file = tiff_exp, dpi = 300, compression = "lzw", device = "tiff", plot = p, width = 20, height = 20, units = "cm")

# Export as a pdf
pdf_exp <- paste0(sample, "_top_genes.pdf")
ggsave(file = pdf_exp, device = "pdf", plot = p, width = 20, height = 20, units = "cm")

...

 = "cm")

# Export as a pdf
pdf_exp <- paste0(sample, "_top_genes.pdf")
ggsave(file = pdf_exp, device = "pdf", plot = p, width = 20, height = 20, units = "cm")

3e. PCA, UMAP and t-SNE plots (plotting dimensionality reduction data)

################################################################

Question for Paul:

one warning comes up when running RunUMAP:

Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session

################################################################

In the section 3c we ran dimensionality reduction based on Principal Component Analysis (PCA).

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3f. Remove low quality or outlier cells

################################################################

Question for Paul:

Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.

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From the Seurat website:

Seurat allows you to easily explore QC metrics and filter cells based on any user-defined criteria. A few QC metrics commonly used by the community include

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