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Open RStudio (you can type it in the Windows search bar)
Create a new R script: ‘File’ → “New File” → “R script”
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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2f. Idenitify markers in cells
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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
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#### 2f. Idenitify 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) |
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<- 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 above table shows the total number of cells for your sample,
# the number of cells which had 0 expression for each marker,
# and the number of cell that had zero expression for all of the markers you provided.
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2g. 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.
Suerat 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:
Normalise the data by log transformation
Identify genes that exhibit high cell-to-cell variation
Scale the data so that highly expressed genes don't dominate the visual representation of expression
Perform the linear dimensional reduction that converts expression to dimensions
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)
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#### 2g. 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)
mat3 <- RunPCA(mat3, features = VariableFeatures(object = mat3))
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