Getting started with Seurat

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Last updated: 2024-03-20

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/seurat.Rmd) and HTML (docs/seurat.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	de75931	Dave Tang	2024-03-20	Filtering on mitochondrial percent
html	03bb7a1	Dave Tang	2024-03-20	Build site.
Rmd	0887184	Dave Tang	2024-03-20	Complete official tutorial
html	23b21ad	Dave Tang	2024-03-20	Build site.
Rmd	64ac12f	Dave Tang	2024-03-20	Normalisation, variable genes, and scaling
html	3c41e26	Dave Tang	2024-03-20	Build site.
Rmd	d5ab5a8	Dave Tang	2024-03-20	Basic filtering
html	5de3e6c	Dave Tang	2024-03-19	Build site.
Rmd	0e1dfa9	Dave Tang	2024-03-19	Sparse matrix
html	944e5f2	Dave Tang	2024-03-19	Build site.
Rmd	ae0ea42	Dave Tang	2024-03-19	Getting started with Seurat

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This post follows the Peripheral Blood Mononuclear Cells (PBMCs) tutorial for 2,700 single cells. It was written while I was going through the tutorial and contains my notes. The dataset for this tutorial can be downloaded from the 10X Genomics dataset page but it is also hosted on Amazon (see below). The PBMCs, which are primary cells with relatively small amounts of RNA (around 1pg RNA/cell), come from a healthy donor. There were 2,700 cells detected and sequencing was performed on an Illumina NextSeq 500 with around 69,000 reads per cell. To get started install Seurat by using install.packages().

install.packages("Seurat")

If you get the warning:

‘SeuratObject’ was built under R 4.3.0 but the current version is 4.3.2; it is recomended that you reinstall ‘SeuratObject’ as the ABI for R may have changed

re-install the SeuratObject package using a repository that has an updated copy. The same goes for the htmltools package.

install.packages("SeuratObject", repos = "https://cran.ism.ac.jp/")
install.packages("htmltools", repos = "https://cran.ism.ac.jp/")
packageVersion("SeuratObject")
packageVersion("htmltools")

Load Seurat.

library("Seurat")

Loading required package: SeuratObject

Loading required package: sp

Attaching package: 'SeuratObject'

The following object is masked from 'package:base':

    intersect

packageVersion("Seurat")

[1] '5.0.3'

Data

To follow the tutorial, you need the 10X data.

mkdir -p data/pbmc3k && cd data/pbmc3k
wget -c https://s3-us-west-2.amazonaws.com/10x.files/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz
tar -xzf pbmc3k_filtered_gene_bc_matrices.tar.gz

The extracted files.

ls -1 data/pbmc3k/filtered_gene_bc_matrices/hg19

barcodes.tsv
genes.tsv
matrix.mtx

matrix.mtx is a MatrixMarket file. It has the following properties:

• Only non-zero entries are stored in the file
• Comments start with a %, like LaTeX
• The first line indicates the total number of rows, columns, and entries
• The following lines after the first provide a row and column number and the value at that coordinate

head data/pbmc3k/filtered_gene_bc_matrices/hg19/matrix.mtx

%%MatrixMarket matrix coordinate real general
%
32738 2700 2286884
32709 1 4
32707 1 1
32706 1 10
32704 1 1
32703 1 5
32702 1 6
32700 1 10

Seurat object

Load 10x data into a matrix.

pbmc.data <- Read10X(data.dir = "data/pbmc3k/filtered_gene_bc_matrices/hg19/")

dgTMatrix-class.

class(pbmc.data)

[1] "dgCMatrix"
attr(,"package")
[1] "Matrix"

32,738 genes and 2,700 cells.

dim(pbmc.data)

[1] 32738  2700

Check out the first six genes and cells

pbmc.data[1:6, 1:6]

6 x 6 sparse Matrix of class "dgCMatrix"
             AAACATACAACCAC-1 AAACATTGAGCTAC-1 AAACATTGATCAGC-1
MIR1302-10                  .                .                .
FAM138A                     .                .                .
OR4F5                       .                .                .
RP11-34P13.7                .                .                .
RP11-34P13.8                .                .                .
AL627309.1                  .                .                .
             AAACCGTGCTTCCG-1 AAACCGTGTATGCG-1 AAACGCACTGGTAC-1
MIR1302-10                  .                .                .
FAM138A                     .                .                .
OR4F5                       .                .                .
RP11-34P13.7                .                .                .
RP11-34P13.8                .                .                .
AL627309.1                  .                .                .

Summary of total expression per single cell.

summary(colSums(pbmc.data))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    548    1758    2197    2367    2763   15844 

Check how many genes have at least one transcript in each cell.

The median number of detected genes among the single cells is 817.

at_least_one <- apply(pbmc.data, 2, function(x) sum(x>0))
hist(
  at_least_one,
  breaks = 100,
  main = "Distribution of detected genes",
  xlab = "Genes with at least one tag"
)
abline(v = median(at_least_one), col = 2, lty = 3)

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
3c41e26	Dave Tang	2024-03-20
5de3e6c	Dave Tang	2024-03-19

Total expression per cell. The median sum of expression among the single cells is 2,197. This distribution is very similar to the distribution of detected genes shown above.

hist(
  colSums(pbmc.data),
  breaks = 100,
  main = "Expression sum per cell",
  xlab = "Sum expression"
)
abline(v = median(colSums(pbmc.data)), col = 2, lty = 3)

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
3c41e26	Dave Tang	2024-03-20
5de3e6c	Dave Tang	2024-03-19

We will filter out genes and single cells before we continue with the analysis. The tutorial has arbitrary values of keeping genes expressed in three or more cells and keeping cells with at least 200 detected genes.

Manually check the number of genes detected in three or more cells; a lot of genes are not detected in 3 or more cells.

tmp <- apply(pbmc.data, 1, function(x) sum(x>0))
table(tmp>=3)

FALSE  TRUE 
19024 13714 

All cells have at least 200 detected genes

keep <- tmp>=3
tmp <- pbmc.data[keep,]
at_least_one <- apply(tmp, 2, function(x) sum(x>0))
summary(at_least_one)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  212.0   690.0   816.0   845.5   952.0  3400.0 

dim(tmp)

[1] 13714  2700

See ?SeuratObject for more information on the class.

pbmc <- CreateSeuratObject(
  counts = pbmc.data,
  min.cells = 3,
  min.features = 200,
  project = "pbmc3k"
)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

class(pbmc)

[1] "Seurat"
attr(,"package")
[1] "SeuratObject"

Same numbers as above

pbmc

An object of class Seurat 
13714 features across 2700 samples within 1 assay 
Active assay: RNA (13714 features, 0 variable features)
 1 layer present: counts

Slots in Seurat object.

SeuratObject: Data Structures for Single Cell Data

Defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. Provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users

Read more about the S4 class in the Advanced R book.

slotNames(pbmc)

 [1] "assays"       "meta.data"    "active.assay" "active.ident" "graphs"      
 [6] "neighbors"    "reductions"   "images"       "project.name" "misc"        
[11] "version"      "commands"     "tools"       

Basic filtering

The tutorial states that “The number of genes and UMIs (nGene and nUMI) are automatically calculated for every object by Seurat.” The nUMI is calculated as num.mol <- colSums(object.raw.data), i.e. each transcript is a unique molecule. The number of genes is simply the tally of genes with at least 1 transcript; num.genes <- colSums(object.raw.data > is.expr) where is.expr is zero.

A common quality control metric is the percentage of transcripts from the mitochondrial genome. According to the paper Classification of low quality cells from single-cell RNA-seq data the reason this is a quality control metric is because if a single cell is lysed, cytoplasmic RNA will be lost apart from the RNA that is enclosed in the mitochondria, which will be retained and sequenced.

Human mitochondria genes conveniently start with MT.

mito.genes <- grep(pattern = "^MT-", x = rownames(x = pbmc@assays$RNA), value = TRUE)
length(mito.genes)

[1] 13

percent.mito <- Matrix::colSums(pbmc[['RNA']]$counts[mito.genes, ]) / Matrix::colSums(pbmc[['RNA']]$counts) * 100
head(percent.mito)

AAACATACAACCAC-1 AAACATTGAGCTAC-1 AAACATTGATCAGC-1 AAACCGTGCTTCCG-1 
       3.0177759        3.7935958        0.8897363        1.7430845 
AAACCGTGTATGCG-1 AAACGCACTGGTAC-1 
       1.2244898        1.6643551 

Check out the meta data.

head(pbmc@meta.data)

                 orig.ident nCount_RNA nFeature_RNA
AAACATACAACCAC-1     pbmc3k       2419          779
AAACATTGAGCTAC-1     pbmc3k       4903         1352
AAACATTGATCAGC-1     pbmc3k       3147         1129
AAACCGTGCTTCCG-1     pbmc3k       2639          960
AAACCGTGTATGCG-1     pbmc3k        980          521
AAACGCACTGGTAC-1     pbmc3k       2163          781

Add mitochondrial percent to the meta using AddMetaData.

pbmc <- AddMetaData(
  object = pbmc,
  metadata = percent.mito,
  col.name = "percent.mito"
)
head(pbmc@meta.data)

                 orig.ident nCount_RNA nFeature_RNA percent.mito
AAACATACAACCAC-1     pbmc3k       2419          779    3.0177759
AAACATTGAGCTAC-1     pbmc3k       4903         1352    3.7935958
AAACATTGATCAGC-1     pbmc3k       3147         1129    0.8897363
AAACCGTGCTTCCG-1     pbmc3k       2639          960    1.7430845
AAACCGTGTATGCG-1     pbmc3k        980          521    1.2244898
AAACGCACTGGTAC-1     pbmc3k       2163          781    1.6643551

Use PercentageFeatureSet to calculate the percentage of a set of features, which saves us from some typing. The [[ operator can add columns to object metadata, which is a great place to stash QC stats.

pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
head(pbmc@meta.data[, c('percent.mito', 'percent.mt')])

                 percent.mito percent.mt
AAACATACAACCAC-1    3.0177759  3.0177759
AAACATTGAGCTAC-1    3.7935958  3.7935958
AAACATTGATCAGC-1    0.8897363  0.8897363
AAACCGTGCTTCCG-1    1.7430845  1.7430845
AAACCGTGTATGCG-1    1.2244898  1.2244898
AAACGCACTGGTAC-1    1.6643551  1.6643551

Instead of setting a hard filtering threshold, one can use the 3 * Median Absolute Deviation to determine threshold limits for filtering out cells.

median(pbmc@meta.data$percent.mt) - 3 * mad(pbmc@meta.data$percent.mt)

[1] -0.3751742

median(pbmc@meta.data$percent.mt) + 3 * mad(pbmc@meta.data$percent.mt)

[1] 4.436775

Plot number of genes, UMIs, and % mitochondria

Visualize QC metrics as a violin plot

VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

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

Past versions of unnamed-chunk-21-1.png

Version	Author	Date
3c41e26	Dave Tang	2024-03-20

A couple of cells have high mitochondrial percentage which may indicate lost of cytoplasmic RNA.

The GenePlot() function can be used to visualise gene-gene relationships as well as any columns in the seurat object. Below we use the plotting function to spot cells that have a high percentage of mitochondrial RNA and to plot the relationship between the number of unique molecules and the number of genes captured.

FeatureScatter is typically used to visualize feature-feature relationships, but can be used for anything calculated by the object, i.e. columns in object metadata, PC scores etc.

plot1 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2

Past versions of unnamed-chunk-22-1.png

Version	Author	Date
3c41e26	Dave Tang	2024-03-20

Manual check; I already know all cells have >200 genes.

table(pbmc@meta.data$percent.mito < 5 & pbmc@meta.data$nFeature_RNA<2500)

FALSE  TRUE 
   62  2638 

pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
pbmc

An object of class Seurat 
13714 features across 2638 samples within 1 assay 
Active assay: RNA (13714 features, 0 variable features)
 1 layer present: counts

Normalisation

The next step is to normalise the data, so that each cell can be compared against each other. At the time of writing, the only normalisation method implemented in Seurat is by log normalisation. Gene expression measurements for each cell are normalised by its total expression, scaled by 10,000, and log-transformed.

hist(
  colSums(pbmc[['RNA']]$counts),
  breaks = 100,
  main = "Total expression before normalisation",
  xlab = "Sum of expression"
)

Past versions of unnamed-chunk-25-1.png

Version	Author	Date
23b21ad	Dave Tang	2024-03-20

After removing unwanted cells from the dataset, the next step is to normalise the data. By default, we employ a global-scaling normalization method “LogNormalize” that normalises the feature expression measurements for each cell by the total expression, multiplies this by a scale factor (10,000 by default), and log-transforms the result. In Seurat v5, Normalized values are stored in pbmc[[“RNA”]]$data.

pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)

Normalizing layer: counts

For clarity, in this previous line of code (and in future commands), we provide the default values for certain parameters in the function call. However, this isn’t required and the same behavior can be achieved with:

pbmc <- NormalizeData(pbmc)

Normalizing layer: counts

While this method of normalization is standard and widely used in scRNA-seq analysis, global-scaling relies on an assumption that each cell originally contains the same number of RNA molecules. We and others have developed alternative workflows for the single cell preprocessing that do not make these assumptions. For users who are interested, please check out our SCTransform() normalization workflow. The method is described in ourpaper, with a separate vignette using Seurat here. The use of SCTransform replaces the need to run NormalizeData, FindVariableFeatures, or ScaleData (described below.)

hist(
  colSums(pbmc[['RNA']]$data),
  breaks = 100,
  main = "Total expression after normalisation",
  xlab = "Sum of expression"
)

Past versions of unnamed-chunk-28-1.png

Version	Author	Date
23b21ad	Dave Tang	2024-03-20

Identification of highly variable features (feature selection)

Once the data is normalised, the next step is to find genes are vary between single cells; genes that are constant among all cells have no distinguishing power. The FindVariableFeatures() function calculates the average expression and dispersion for each gene, places these genes into bins, and then calculates a z-score for dispersion within each bin. I interpret that as take each gene, get the average expression and variance of the gene across the 2,638 cells, categorise genes into bins (default is 20) based on their expression and variance, and finally normalise the variance in each bin. This was the same approach in Macosko et al. and new methods for detecting genes with variable expression patterns will be implemented in Seurat soon (according to the tutorial). The parameters used below are typical settings for UMI data that is normalised to a total of 10,000 molecules and will identify around 2,000 variable genes. The tutorial recommends that users should explore the parameters themselves since each dataset is different.

We next calculate a subset of features that exhibit high cell-to-cell variation in the dataset (i.e, they are highly expressed in some cells, and lowly expressed in others). We and others have found that focusing on these genes in downstream analysis helps to highlight biological signal in single-cell datasets.

Our procedure in Seurat is described in detail here, and improves on previous versions by directly modeling the mean-variance relationship inherent in single-cell data, and is implemented in the FindVariableFeatures() function. By default, we return 2,000 features per dataset. These will be used in downstream analysis, like PCA.

pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)

Finding variable features for layer counts

length(VariableFeatures(pbmc))

[1] 2000

Identify the 10 most highly variable genes

top10 <- head(VariableFeatures(pbmc), 10)
top10

 [1] "PPBP"   "LYZ"    "S100A9" "IGLL5"  "GNLY"   "FTL"    "PF4"    "FTH1"  
 [9] "GNG11"  "S100A8"

Plot variable features with and without labels

plot1 <- VariableFeaturePlot(pbmc)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)

When using repel, set xnudge and ynudge to 0 for optimal results

plot1 + plot2

Warning in scale_x_log10(): log-10 transformation introduced infinite values.
log-10 transformation introduced infinite values.

Past versions of unnamed-chunk-32-1.png

Version	Author	Date
23b21ad	Dave Tang	2024-03-20

Scaling the data

Next, we apply a linear transformation (“scaling”) that is a standard pre-processing step prior to dimensional reduction techniques like PCA. The ScaleData() function:

• Shifts the expression of each gene, so that the mean expression across cells is 0
• Scales the expression of each gene, so that the variance across cells is 1
  • This step gives equal weight in downstream analyses, so that highly-expressed genes do not dominate
• The results of this are stored in pbmc[[“RNA”]]$scale.data
• By default, only variable features are scaled.
• You can specify the features argument to scale additional features

all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)

Centering and scaling data matrix

dim(pbmc[["RNA"]]$scale.data)

[1] 13714  2638

hist(
  colSums(pbmc[['RNA']]$scale.data),
  breaks = 100,
  main = "Total expression after scaling",
  xlab = "Sum of expression"
)

Past versions of unnamed-chunk-34-1.png

Version	Author	Date
23b21ad	Dave Tang	2024-03-20

How can I remove unwanted sources of variation?

In Seurat, we also use the ScaleData() function to remove unwanted sources of variation from a single-cell dataset. For example, we could “regress out” heterogeneity associated with (for example) cell cycle stage, or mitochondrial contamination i.e.:

pbmc <- ScaleData(pbmc, features = all.genes, vars.to.regress = "percent.mt")

However, particularly for advanced users who would like to use this functionality, we strongly recommend the use of our new normalization workflow, SCTransform(). The method is described in this paper, with a separate vignette using Seurat. As with ScaleData(), the function SCTransform() also includes a vars.to.regress parameter.

Perform linear dimensional reduction

Next we perform PCA on the scaled data. By default, only the previously determined variable features are used as input, but can be defined using features argument if you wish to choose a different subset (if you do want to use a custom subset of features, make sure you pass these to ScaleData first).

For the first principal components, Seurat outputs a list of genes with the most positive and negative loadings, representing modules of genes that exhibit either correlation (or anti-correlation) across single-cells in the dataset.

pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))

PC_ 1 
Positive:  CST3, TYROBP, LST1, AIF1, FTL, FTH1, LYZ, FCN1, S100A9, TYMP 
       FCER1G, CFD, LGALS1, S100A8, CTSS, LGALS2, SERPINA1, IFITM3, SPI1, CFP 
       PSAP, IFI30, SAT1, COTL1, S100A11, NPC2, GRN, LGALS3, GSTP1, PYCARD 
Negative:  MALAT1, LTB, IL32, IL7R, CD2, B2M, ACAP1, CD27, STK17A, CTSW 
       CD247, GIMAP5, AQP3, CCL5, SELL, TRAF3IP3, GZMA, MAL, CST7, ITM2A 
       MYC, GIMAP7, HOPX, BEX2, LDLRAP1, GZMK, ETS1, ZAP70, TNFAIP8, RIC3 
PC_ 2 
Positive:  CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DQB1, HLA-DRA, LINC00926, CD79B, HLA-DRB1, CD74 
       HLA-DMA, HLA-DPB1, HLA-DQA2, CD37, HLA-DRB5, HLA-DMB, HLA-DPA1, FCRLA, HVCN1, LTB 
       BLNK, P2RX5, IGLL5, IRF8, SWAP70, ARHGAP24, FCGR2B, SMIM14, PPP1R14A, C16orf74 
Negative:  NKG7, PRF1, CST7, GZMB, GZMA, FGFBP2, CTSW, GNLY, B2M, SPON2 
       CCL4, GZMH, FCGR3A, CCL5, CD247, XCL2, CLIC3, AKR1C3, SRGN, HOPX 
       TTC38, APMAP, CTSC, S100A4, IGFBP7, ANXA1, ID2, IL32, XCL1, RHOC 
PC_ 3 
Positive:  HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1, HLA-DPA1, CD74, MS4A1, HLA-DRB1, HLA-DRA 
       HLA-DRB5, HLA-DQA2, TCL1A, LINC00926, HLA-DMB, HLA-DMA, CD37, HVCN1, FCRLA, IRF8 
       PLAC8, BLNK, MALAT1, SMIM14, PLD4, LAT2, IGLL5, P2RX5, SWAP70, FCGR2B 
Negative:  PPBP, PF4, SDPR, SPARC, GNG11, NRGN, GP9, RGS18, TUBB1, CLU 
       HIST1H2AC, AP001189.4, ITGA2B, CD9, TMEM40, PTCRA, CA2, ACRBP, MMD, TREML1 
       NGFRAP1, F13A1, SEPT5, RUFY1, TSC22D1, MPP1, CMTM5, RP11-367G6.3, MYL9, GP1BA 
PC_ 4 
Positive:  HLA-DQA1, CD79B, CD79A, MS4A1, HLA-DQB1, CD74, HLA-DPB1, HIST1H2AC, PF4, TCL1A 
       SDPR, HLA-DPA1, HLA-DRB1, HLA-DQA2, HLA-DRA, PPBP, LINC00926, GNG11, HLA-DRB5, SPARC 
       GP9, AP001189.4, CA2, PTCRA, CD9, NRGN, RGS18, GZMB, CLU, TUBB1 
Negative:  VIM, IL7R, S100A6, IL32, S100A8, S100A4, GIMAP7, S100A10, S100A9, MAL 
       AQP3, CD2, CD14, FYB, LGALS2, GIMAP4, ANXA1, CD27, FCN1, RBP7 
       LYZ, S100A11, GIMAP5, MS4A6A, S100A12, FOLR3, TRABD2A, AIF1, IL8, IFI6 
PC_ 5 
Positive:  GZMB, NKG7, S100A8, FGFBP2, GNLY, CCL4, CST7, PRF1, GZMA, SPON2 
       GZMH, S100A9, LGALS2, CCL3, CTSW, XCL2, CD14, CLIC3, S100A12, CCL5 
       RBP7, MS4A6A, GSTP1, FOLR3, IGFBP7, TYROBP, TTC38, AKR1C3, XCL1, HOPX 
Negative:  LTB, IL7R, CKB, VIM, MS4A7, AQP3, CYTIP, RP11-290F20.3, SIGLEC10, HMOX1 
       PTGES3, LILRB2, MAL, CD27, HN1, CD2, GDI2, ANXA5, CORO1B, TUBA1B 
       FAM110A, ATP1A1, TRADD, PPA1, CCDC109B, ABRACL, CTD-2006K23.1, WARS, VMO1, FYB 

Seurat provides several useful ways of visualizing both cells and features that define the PCA, including VizDimReduction(), DimPlot(), and DimHeatmap().

Examine and visualize PCA results a few different ways

print(pbmc[["pca"]], dims = 1:5, nfeatures = 5)

PC_ 1 
Positive:  CST3, TYROBP, LST1, AIF1, FTL 
Negative:  MALAT1, LTB, IL32, IL7R, CD2 
PC_ 2 
Positive:  CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DQB1 
Negative:  NKG7, PRF1, CST7, GZMB, GZMA 
PC_ 3 
Positive:  HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1 
Negative:  PPBP, PF4, SDPR, SPARC, GNG11 
PC_ 4 
Positive:  HLA-DQA1, CD79B, CD79A, MS4A1, HLA-DQB1 
Negative:  VIM, IL7R, S100A6, IL32, S100A8 
PC_ 5 
Positive:  GZMB, NKG7, S100A8, FGFBP2, GNLY 
Negative:  LTB, IL7R, CKB, VIM, MS4A7 

VizDimLoadings(pbmc, dims = 1:2, reduction = "pca")

Past versions of unnamed-chunk-38-1.png

Version	Author	Date
03bb7a1	Dave Tang	2024-03-20

DimPlot(pbmc, reduction = "pca") + NoLegend()

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Version	Author	Date
03bb7a1	Dave Tang	2024-03-20

In particular DimHeatmap() allows for easy exploration of the primary sources of heterogeneity in a dataset, and can be useful when trying to decide which PCs to include for further downstream analyses. Both cells and features are ordered according to their PCA scores. Setting cells to a number plots the ‘extreme’ cells on both ends of the spectrum, which dramatically speeds plotting for large datasets. Though clearly a supervised analysis, we find this to be a valuable tool for exploring correlated feature sets.

DimHeatmap(pbmc, dims = 1, cells = 500, balanced = TRUE)

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Version	Author	Date
03bb7a1	Dave Tang	2024-03-20

DimHeatmap(pbmc, dims = 1:15, cells = 500, balanced = TRUE)

Past versions of unnamed-chunk-41-1.png

Version	Author	Date
03bb7a1	Dave Tang	2024-03-20

Determine the dimensionality of the dataset

To overcome the extensive technical noise in any single feature for scRNA-seq data, Seurat clusters cells based on their PCA scores, with each PC essentially representing a ‘metafeature’ that combines information across a correlated feature set. The top principal components therefore represent a robust compression of the dataset. However, how many components should we choose to include? 10? 20? 100?

In Macosko et al, we implemented a resampling test inspired by the JackStraw procedure. While still available in Seurat (see previous vignette), this is a slow and computationally expensive procedure, and we is no longer routinely used in single cell analysis.

An alternative heuristic method generates an ‘Elbow plot’: a ranking of principle components based on the percentage of variance explained by each one (ElbowPlot() function). In this example, we can observe an ‘elbow’ around PC9-10, suggesting that the majority of true signal is captured in the first 10 PCs.

ElbowPlot(pbmc)

Past versions of unnamed-chunk-42-1.png

Version	Author	Date
03bb7a1	Dave Tang	2024-03-20

Identifying the true dimensionality of a dataset – can be challenging/uncertain for the user. We therefore suggest these multiple approaches for users. The first is more supervised, exploring PCs to determine relevant sources of heterogeneity, and could be used in conjunction with GSEA for example. The second (ElbowPlot) The third is a heuristic that is commonly used, and can be calculated instantly. In this example, we might have been justified in choosing anything between PC 7-12 as a cutoff.

We chose 10 here, but encourage users to consider the following:

• Dendritic cell and NK aficionados may recognize that genes strongly associated with PCs 12 and 13 define rare immune subsets (i.e. MZB1 is a marker for plasmacytoid DCs). However, these groups are so rare, they are difficult to distinguish from background noise for a dataset of this size without prior knowledge.
• We encourage users to repeat downstream analyses with a different number of PCs (10, 15, or even 50!). As you will observe, the results often do not differ dramatically.
• We advise users to err on the higher side when choosing this parameter. For example, performing downstream analyses with only 5 PCs does significantly and adversely affect results.

Cluster the cells

Seurat applies a graph-based clustering approach, building upon initial strategies in (Macosko et al). Importantly, the distance metric which drives the clustering analysis (based on previously identified PCs) remains the same. However, our approach to partitioning the cellular distance matrix into clusters has dramatically improved. Our approach was heavily inspired by recent manuscripts which applied graph-based clustering approaches to scRNA-seq data [SNN-Cliq, Xu and Su, Bioinformatics, 2015] and CyTOF data [PhenoGraph, Levine et al., Cell, 2015]. Briefly, these methods embed cells in a graph structure - for example a K-nearest neighbor (KNN) graph, with edges drawn between cells with similar feature expression patterns, and then attempt to partition this graph into highly interconnected ‘quasi-cliques’ or ‘communities’.

As in PhenoGraph, we first construct a KNN graph based on the euclidean distance in PCA space, and refine the edge weights between any two cells based on the shared overlap in their local neighborhoods (Jaccard similarity). This step is performed using the FindNeighbors() function, and takes as input the previously defined dimensionality of the dataset (first 10 PCs).

To cluster the cells, we next apply modularity optimization techniques such as the Louvain algorithm (default) or SLM [SLM, Blondel et al., Journal of Statistical Mechanics], to iteratively group cells together, with the goal of optimizing the standard modularity function. The FindClusters() function implements this procedure, and contains a resolution parameter that sets the ‘granularity’ of the downstream clustering, with increased values leading to a greater number of clusters. We find that setting this parameter between 0.4-1.2 typically returns good results for single-cell datasets of around 3K cells. Optimal resolution often increases for larger datasets. The clusters can be found using the Idents() function.

pbmc <- FindNeighbors(pbmc, dims = 1:10)

Computing nearest neighbor graph

Computing SNN

pbmc <- FindClusters(pbmc, resolution = 0.5)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2638
Number of edges: 95965

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8723
Number of communities: 9
Elapsed time: 0 seconds

Look at cluster IDs of the first 5 cells

head(Idents(pbmc), 5)

AAACATACAACCAC-1 AAACATTGAGCTAC-1 AAACATTGATCAGC-1 AAACCGTGCTTCCG-1 
               2                3                2                1 
AAACCGTGTATGCG-1 
               6 
Levels: 0 1 2 3 4 5 6 7 8

Non-linear dimensional reduction

Seurat offers several non-linear dimensional reduction techniques, such as tSNE and UMAP, to visualize and explore these datasets. The goal of these algorithms is to learn underlying structure in the dataset, in order to place similar cells together in low-dimensional space. Therefore, cells that are grouped together within graph-based clusters determined above should co-localize on these dimension reduction plots.

While we and others have routinely found 2D visualization techniques like tSNE and UMAP to be valuable tools for exploring datasets, all visualization techniques have limitations, and cannot fully represent the complexity of the underlying data. In particular, these methods aim to preserve local distances in the dataset (i.e. ensuring that cells with very similar gene expression profiles co-localize), but often do not preserve more global relationships. We encourage users to leverage techniques like UMAP for visualization, but to avoid drawing biological conclusions solely on the basis of visualization techniques.

pbmc <- RunUMAP(pbmc, dims = 1:10)

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

13:03:21 UMAP embedding parameters a = 0.9922 b = 1.112

13:03:21 Read 2638 rows and found 10 numeric columns

13:03:21 Using Annoy for neighbor search, n_neighbors = 30

13:03:21 Building Annoy index with metric = cosine, n_trees = 50

0%   10   20   30   40   50   60   70   80   90   100%

[----|----|----|----|----|----|----|----|----|----|

**************************************************|
13:03:22 Writing NN index file to temp file /tmp/Rtmpo9PSkm/file29d918ccc007
13:03:22 Searching Annoy index using 1 thread, search_k = 3000
13:03:22 Annoy recall = 100%
13:03:23 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
13:03:23 Initializing from normalized Laplacian + noise (using RSpectra)
13:03:23 Commencing optimization for 500 epochs, with 105124 positive edges
13:03:26 Optimization finished

Note that you can set label = TRUE or use the LabelClusters function to help label individual clusters

DimPlot(pbmc, reduction = "umap")

Past versions of unnamed-chunk-46-1.png

Version	Author	Date
03bb7a1	Dave Tang	2024-03-20

Finding differentially expressed features

Seurat can help you find markers that define clusters via differential expression (DE). By default, it identifies positive and negative markers of a single cluster (specified in ident.1), compared to all other cells. FindAllMarkers() automates this process for all clusters, but you can also test groups of clusters vs. each other, or against all cells.

In Seurat v5, we use the presto package (as described here and available for installation here), to dramatically improve the speed of DE analysis, particularly for large datasets. For users who are not using presto, you can examine the documentation for this function (?FindMarkers) to explore the min.pct and logfc.threshold parameters, which can be increased in order to increase the speed of DE testing.

For a (much!) faster implementation of the Wilcoxon Rank Sum Test, (default method for FindMarkers) please install the {presto} package. After installation of {presto}, Seurat will automatically use the more efficient implementation (no further action necessary).

install.packages("remotes")
remotes::install_github('immunogenomics/presto')

Load {presto}.

library('presto')

Loading required package: Rcpp

Loading required package: data.table

packageVersion('presto')

[1] '1.0.0'

Find all markers of cluster 2.

cluster2.markers <- FindMarkers(pbmc, ident.1 = 2)
head(cluster2.markers, n = 5)

            p_val avg_log2FC pct.1 pct.2    p_val_adj
IL32 2.593535e-91  1.3221171 0.949 0.466 3.556774e-87
LTB  7.994465e-87  1.3450377 0.981 0.644 1.096361e-82
CD3D 3.922451e-70  1.0562099 0.922 0.433 5.379250e-66
IL7R 1.130870e-66  1.4256944 0.748 0.327 1.550876e-62
LDHB 4.082189e-65  0.9765875 0.953 0.614 5.598314e-61

Find all markers distinguishing cluster 5 from clusters 0 and 3

cluster5.markers <- FindMarkers(pbmc, ident.1 = 5, ident.2 = c(0, 3))
head(cluster5.markers, n = 5)

                      p_val avg_log2FC pct.1 pct.2     p_val_adj
FCGR3A        2.150929e-209   6.832372 0.975 0.039 2.949784e-205
IFITM3        6.103366e-199   6.181000 0.975 0.048 8.370156e-195
CFD           8.891428e-198   6.052575 0.938 0.037 1.219370e-193
CD68          2.374425e-194   5.493138 0.926 0.035 3.256286e-190
RP11-290F20.3 9.308287e-191   6.335402 0.840 0.016 1.276538e-186

Find markers for every cluster compared to all remaining cells, report only the positive ones.

pbmc.markers <- FindAllMarkers(pbmc, only.pos = TRUE)

Calculating cluster 0

Calculating cluster 1

Calculating cluster 2

Calculating cluster 3

Calculating cluster 4

Calculating cluster 5

Calculating cluster 6

Calculating cluster 7

Calculating cluster 8

pbmc.markers %>%
    dplyr::group_by(cluster) %>%
    dplyr::filter(avg_log2FC > 1)

# A tibble: 7,046 × 7
# Groups:   cluster [9]
       p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene     
       <dbl>      <dbl> <dbl> <dbl>     <dbl> <fct>   <chr>    
 1 1.74e-109       1.19 0.897 0.593 2.39e-105 0       LDHB     
 2 1.17e- 83       2.37 0.435 0.108 1.60e- 79 0       CCR7     
 3 8.94e- 79       1.09 0.838 0.403 1.23e- 74 0       CD3D     
 4 3.05e- 53       1.02 0.722 0.399 4.19e- 49 0       CD3E     
 5 3.28e- 49       2.10 0.333 0.103 4.50e- 45 0       LEF1     
 6 6.66e- 49       1.25 0.623 0.358 9.13e- 45 0       NOSIP    
 7 9.31e- 44       2.02 0.328 0.11  1.28e- 39 0       PRKCQ-AS1
 8 4.69e- 43       1.53 0.435 0.184 6.43e- 39 0       PIK3IP1  
 9 1.47e- 39       2.70 0.195 0.04  2.01e- 35 0       FHIT     
10 2.44e- 33       1.94 0.262 0.087 3.34e- 29 0       MAL      
# ℹ 7,036 more rows

Seurat has several tests for differential expression which can be set with the test.use parameter (see our DE vignette for details). For example, the ROC test returns the ‘classification power’ for any individual marker (ranging from 0 - random, to 1 - perfect).

cluster0.markers <- FindMarkers(pbmc, ident.1 = 0, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)

We include several tools for visualizing marker expression. VlnPlot() (shows expression probability distributions across clusters), and FeaturePlot() (visualizes feature expression on a tSNE or PCA plot) are our most commonly used visualizations. We also suggest exploring RidgePlot(), CellScatter(), and DotPlot() as additional methods to view your dataset.

VlnPlot(pbmc, features = c("MS4A1", "CD79A"))

Past versions of unnamed-chunk-53-1.png

Version	Author	Date
03bb7a1	Dave Tang	2024-03-20

You can plot raw counts as well.

VlnPlot(pbmc, features = c("NKG7", "PF4"), layer = "counts", log = TRUE)

Past versions of unnamed-chunk-54-1.png

Version	Author	Date
03bb7a1	Dave Tang	2024-03-20

FeaturePlot(pbmc, features = c("MS4A1", "GNLY", "CD3E", "CD14", "FCER1A", "FCGR3A", "LYZ", "PPBP",
    "CD8A"))

Past versions of unnamed-chunk-55-1.png

Version	Author	Date
03bb7a1	Dave Tang	2024-03-20

DoHeatmap() generates an expression heatmap for given cells and features. In this case, we are plotting the top 20 markers (or all markers if less than 20) for each cluster.

pbmc.markers %>%
    dplyr::group_by(cluster) %>%
    dplyr::filter(avg_log2FC > 1) %>%
    dplyr::slice_head(n = 10) %>%
    dplyr::ungroup() -> top10

DoHeatmap(pbmc, features = top10$gene) + NoLegend()

Past versions of unnamed-chunk-56-1.png

Version	Author	Date
03bb7a1	Dave Tang	2024-03-20

Assigning cell type identity to clusters

Fortunately in the case of this dataset, we can use canonical markers to easily match the unbiased clustering to known cell types:

Cluster ID	Markers	Cell Type
0	IL7R, CCR7	Naive CD4+ T
1	CD14, LYZ	CD14+ Mono
2	IL7R, S100A4	Memory CD4+
3	MS4A1	B
4	CD8A	CD8+ T
5	FCGR3A, MS4A7	FCGR3A+ Mono
6	GNLY, NKG7	NK
7	FCER1A, CST3	DC
8	PPBP	Platelet

new.cluster.ids <- c("Naive CD4 T", "CD14+ Mono", "Memory CD4 T", "B", "CD8 T", "FCGR3A+ Mono",
    "NK", "DC", "Platelet")
names(new.cluster.ids) <- levels(pbmc)
pbmc <- RenameIdents(pbmc, new.cluster.ids)
DimPlot(pbmc, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()

Past versions of unnamed-chunk-57-1.png

Version	Author	Date
03bb7a1	Dave Tang	2024-03-20

library(ggplot2)
DimPlot(pbmc, reduction = "umap", label = TRUE, label.size = 4.5) + xlab("UMAP 1") +
  ylab("UMAP 2") +
  theme(axis.title = element_text(size = 18), legend.text = element_text(size = 18)) +
  guides(colour = guide_legend(override.aes = list(size = 10)))

Past versions of unnamed-chunk-58-1.png

Version	Author	Date
03bb7a1	Dave Tang	2024-03-20

Session information

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.5.0      presto_1.0.0       data.table_1.15.2  Rcpp_1.0.12       
[5] Seurat_5.0.3       SeuratObject_5.0.1 sp_2.1-3           workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3     rstudioapi_0.15.0      jsonlite_1.8.8        
  [4] magrittr_2.0.3         spatstat.utils_3.0-4   farver_2.1.1          
  [7] rmarkdown_2.26         fs_1.6.3               vctrs_0.6.5           
 [10] ROCR_1.0-11            spatstat.explore_3.2-6 htmltools_0.5.7       
 [13] sass_0.4.9             sctransform_0.4.1      parallelly_1.37.1     
 [16] KernSmooth_2.23-22     bslib_0.6.1            htmlwidgets_1.6.4     
 [19] ica_1.0-3              plyr_1.8.9             plotly_4.10.4         
 [22] zoo_1.8-12             cachem_1.0.8           whisker_0.4.1         
 [25] igraph_2.0.3           mime_0.12              lifecycle_1.0.4       
 [28] pkgconfig_2.0.3        Matrix_1.6-5           R6_2.5.1              
 [31] fastmap_1.1.1          fitdistrplus_1.1-11    future_1.33.1         
 [34] shiny_1.8.0            digest_0.6.35          colorspace_2.1-0      
 [37] patchwork_1.2.0        ps_1.7.6               rprojroot_2.0.4       
 [40] tensor_1.5             RSpectra_0.16-1        irlba_2.3.5.1         
 [43] labeling_0.4.3         progressr_0.14.0       fansi_1.0.6           
 [46] spatstat.sparse_3.0-3  httr_1.4.7             polyclip_1.10-6       
 [49] abind_1.4-5            compiler_4.3.2         withr_3.0.0           
 [52] fastDummies_1.7.3      highr_0.10             MASS_7.3-60           
 [55] tools_4.3.2            lmtest_0.9-40          httpuv_1.6.14         
 [58] future.apply_1.11.1    goftest_1.2-3          glue_1.7.0            
 [61] callr_3.7.5            nlme_3.1-163           promises_1.2.1        
 [64] grid_4.3.2             Rtsne_0.17             getPass_0.2-4         
 [67] cluster_2.1.4          reshape2_1.4.4         generics_0.1.3        
 [70] gtable_0.3.4           spatstat.data_3.0-4    tidyr_1.3.1           
 [73] utf8_1.2.4             spatstat.geom_3.2-9    RcppAnnoy_0.0.22      
 [76] ggrepel_0.9.5          RANN_2.6.1             pillar_1.9.0          
 [79] stringr_1.5.1          spam_2.10-0            RcppHNSW_0.6.0        
 [82] later_1.3.2            splines_4.3.2          dplyr_1.1.4           
 [85] lattice_0.21-9         survival_3.5-7         deldir_2.0-4          
 [88] tidyselect_1.2.1       miniUI_0.1.1.1         pbapply_1.7-2         
 [91] knitr_1.45             git2r_0.33.0           gridExtra_2.3         
 [94] scattermore_1.2        xfun_0.42              matrixStats_1.2.0     
 [97] stringi_1.8.3          lazyeval_0.2.2         yaml_2.3.8            
[100] evaluate_0.23          codetools_0.2-19       tibble_3.2.1          
[103] cli_3.6.2              uwot_0.1.16            xtable_1.8-4          
[106] reticulate_1.35.0      munsell_0.5.0          processx_3.8.4        
[109] jquerylib_0.1.4        globals_0.16.3         spatstat.random_3.2-3 
[112] png_0.1-8              parallel_4.3.2         ellipsis_0.3.2        
[115] dotCall64_1.1-1        listenv_0.9.1          viridisLite_0.4.2     
[118] scales_1.3.0           ggridges_0.5.6         leiden_0.4.3.1        
[121] purrr_1.0.2            rlang_1.1.3            cowplot_1.1.3