Overview

This workflow template is for analyzing single cell RNA-seq (scRNA-seq) data. It is provided by systemPipeRdata, a companion package to systemPipeR (H Backman and Girke 2016). The content of this workflow steps are provided by this Seurat Tutorial. Similar to other systemPipeR workflow templates, a single command generates the necessary working environment. This includes the expected directory structure for executing systemPipeR workflows and parameter files for running command-line (CL) software utilized in specific analysis steps. For learning and testing purposes, a small sample (toy) data set is also included (mainly FASTQ and reference genome files). This enables users to seamlessly run the numerous analysis steps of this workflow from start to finish without the requirement of providing custom data. After testing the workflow, users have the flexibility to employ the template as is with their own data or modify it to suit their specific needs. For more comprehensive information on designing and executing workflows, users want to refer to the main vignettes of systemPipeR and systemPipeRdata.

The Rmd file (SPscrna.Rmd) associated with this vignette serves a dual purpose. It acts both as a template for executing the workflow and as a template for generating a reproducible scientific analysis report. Thus, users want to customize the text (and/or code) of this vignette to describe their experimental design and analysis results. This typically involves deleting the instructions how to work with this workflow, and customizing the text describing experimental designs, other metadata and analysis results.

Experimental design

Typically, the user wants to describe here the sources and versions of the reference genome sequence along with the corresponding annotations. The standard directory structure of systemPipeR (see here), expects the input data in a subdirectory named data and all results will be written to a separate results directory. The Rmd source file for executing the workflow and rendering its report (here SPscrna.Rmd) is expected to be located in the parent directory.

In this template, a toy single cell dataset is preprocessed/filtered by 10X. Samples taken from peripheral blood mononuclear cells (PBMCs), about 3000 cells.

This dataset will be downloaded on-the-fly as one of the workflow steps in this template. Users can also manually download the dataset and unzip into the data directory.

To use their own scRNA-Seq and reference genome data, users want to move or link the data to the designated data directory and execute the workflow from the parent directory using their customized Rmd file. Beginning with this template, users should delete the provided test data and move or link their custom data to the designated locations. Alternatively, users can create an environment skeleton (named new here) or build one from scratch.

Workflow steps

The default analysis steps included in this scRNA-Seq workflow template are listed below. Users can modify the existing steps, add new ones or remove steps as needed.

Default analysis steps

1. Read in single cell count data.
2. Basic stats on input data.
3. Create some basic QC on cell counting.
4. Normalization.
5. Find high variable genes.
6. Scaling.
7. Dim reduction, PCA.
8. Clustering with tSNE, uMAP.
9. Find clustering markers (marker gene).
10. Find cell types.
11. Visualize cell types and clustering together.

Load workflow environment

The environment for this scRNA-Seq workflow is auto-generated below with the genWorkenvir function (selected under workflow="scrnaseq"). The name of the resulting workflow directory can be specified under the mydirname argument. The default NULL uses the name of the chosen workflow. An error is issued if a directory of the same name and path exists already. After this, the user’s R session needs to be directed into the resulting SPscrna directory (here with setwd).

library(systemPipeRdata)
genWorkenvir(workflow = "SPscrna", mydirname = "SPscrna")
setwd("SPscrna")

Load packages

The first step loads the required packages.

cat(crayon::blue$bold("To use this workflow, following R packages are expected:\n"))
cat(c("'Seurat", "ggplot2", "ggpubr", "patchwork", "dplyr", "tibble",
    "readr'\n"), sep = "', '")
### pre-end
appendStep(sal) <- LineWise(code = {
    library(systemPipeR)
    library(Seurat)
    library(dplyr)
    library(ggplot2)
    library(ggpubr)
    library(patchwork)
}, step_name = "load_packages")

Load data

In this example, the single cell data is preprocessed/filtered 10x data from a healthy donor. Samples taken from peripheral blood mononuclear cells (PBMCs), about 3000 cells.

Dataset can be downloaded with this link: https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz

If the link is not working, visit 10x website for updated links.

For your real data, please preprocess and put the dataset inside data directory

appendStep(sal) <- LineWise(code = {
    # unzip the data
    untar("data/pbmc3k_filtered_gene_bc_matrices.tar.gz", exdir = "data")
    # load data
    pbmc.data <- Read10X(data.dir = "data/filtered_gene_bc_matrices/hg19/")
    # Use dim to see the size of dataset, example data has
    # 2700 cells x 32738 genes
    dim(pbmc.data)
}, step_name = "load_data", dependency = "load_packages")

Simple count visulization

We can plot to see how many cells have good expressions.

appendStep(sal) <- LineWise(code = {
    at_least_one <- apply(pbmc.data, 2, function(x) sum(x > 0))
    count_p1 <- tibble::as_tibble(at_least_one) %>%
        ggplot() + geom_histogram(aes(x = value), binwidth = floor(nrow(pbmc.data)/400),
        fill = "#6b97c2", color = "white") + theme_pubr(16) +
        scale_y_continuous(expand = c(0, 0)) + scale_x_continuous(expand = c(0,
        0)) + labs(title = "Distribution of detected genes",
        x = "Genes with at least one tag")

    count_p2 <- tibble::as_tibble(MatrixGenerics::colSums(pbmc.data)) %>%
        ggplot() + geom_histogram(aes(x = value), bins = floor(ncol(pbmc.data)/50),
        fill = "#6b97c2", color = "white") + theme_pubr(16) +
        scale_y_continuous(expand = c(0, 0)) + scale_x_continuous(expand = c(0,
        0)) + labs(title = "Expression sum per cell", x = "Sum expression")

    png("results/count_plots.png", 1000, 700)
    count_p1 + count_p2 + patchwork::plot_annotation(tag_levels = "A")
    dev.off()
}, step_name = "count_plot", dependency = "load_data")

Create Seurat object

appendStep(sal) <- LineWise(code = {
    sce <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k",
        min.cells = 3, min.features = 200)
    # calculate mitochondria gene ratio
    sce[["percent.mt"]] <- PercentageFeatureSet(sce, pattern = "^MT-")
}, step_name = "create_seurat", dependency = "load_data")

Filters applied here are:

• min.cells = 3: for a single gene, it must be presented in at least 3 cells.
• min.features = 200: for any cell, it must at least have 200 genes with readable gene counts.

In your real data, you may want to adjust the filters for different projects.

QC of cells

Some indicators are important to show the quality of the cell batch, for example

• N genes (features) per cell
• N counts per cell
• Mitochondria (mt) gene ratio.
• …

appendStep(sal) <- LineWise(code = {

    png("results/qc1.png", 700, 700)
    VlnPlot(sce, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
        ncol = 3)
    dev.off()

    qc_p1 <- FeatureScatter(sce, feature1 = "nCount_RNA", feature2 = "percent.mt")
    qc_p2 <- FeatureScatter(sce, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
    png("results/qc2.png", 700, 450)
    qc_p1 + qc_p2 + patchwork::plot_annotation(tag_levels = "A")
    dev.off()
}, step_name = "qc_seurat", dependency = "create_seurat")

Extreme numbers, outlines are likely low quality sequencing/bad cells.

As shown on the figure above, A is the ratio of percent mt vs. N count. Normally, no matter how many counts you can find from a cell, the percent mt gene ratio should always be stable. For cells with extremely high mt ratios, they should be removed. Looking at x-axis, for a single cell, the N counts should also be inside a reasonable range. Too low of counts indicates the cell gel bead is empty during barcoding. Too high of counts indicates a single gel bead had more than one cell during barcoding. Either these cases are bad, and cells of these should be removed.

B is N genes per cell vs. N counts per cell. Normally, if as the N counts increases, the N genes also increases. All cells should be connected to approximately one line. If you see more than one line, there may be problem with the sequencing, or cells are not from the same batch.

Filter cells

appendStep(sal) <- LineWise(code = {
    # Based on the QC plots, please change the settings
    # accordingly
    sce <- subset(sce, subset = nFeature_RNA > 200 & nFeature_RNA <
        2500 & nCount_RNA < 10000 & percent.mt <= 5)
}, step_name = "filter_cells", dependency = "create_seurat")

Normalization

For RNAseq data, usually log transformation and TPM is performed. For scRNA, the normalization is

\[ Normalized\ count\ for\ a\ gene\ in\ a\ cell\ =log1p(\frac{Feature\ counts}{Total\ counts\ in\ a\ cell})\\ where\ log1p\ is\ ln(n+1) \]

appendStep(sal) <- LineWise(code = {
    # scale.factor = 10000 is a convenient number for
    # plotting, so the normalized counts is ranged between
    # 0.xx to 10.
    sce <- NormalizeData(sce, normalization.method = "LogNormalize",
        scale.factor = 10000)

    # compare counts before and after
    count_p_norm <- tibble::as_tibble(MatrixGenerics::colSums(sce$RNA@layers$data)) %>%
        ggplot() + geom_histogram(aes(x = value), bins = floor(ncol(pbmc.data)/50),
        fill = "#6b97c2", color = "white") + theme_pubr(16) +
        scale_y_continuous(expand = c(0, 0)) + scale_x_continuous(expand = c(0,
        0)) + labs(title = "Total expression after normalization",
        x = "Sum expression")
    png("results/normalize_count_compare.png", 1000, 700)
    count_p2 + count_p_norm + patchwork::plot_annotation(tag_levels = "A")
    dev.off()
}, step_name = "normalize", dependency = "filter_cells")

After the normalization, expression is close to a normal distribution.

Find highly variable genes

Some genes are highly variable among cells. These genes are the key for the research. Here, some top-ranked variable genes are calculated and will be used for downstream analysis.

appendStep(sal) <- LineWise(code = {
    # 2000 is default
    sce <- FindVariableFeatures(sce, selection.method = "vst",
        nfeatures = 2000)
    # top 10 variable genes
    top10_var <- head(VariableFeatures(sce), 10)
    # plot the top 2000 variable genes and mark top 10
    png("results/variable_genes.png", 700, 600)
    VariableFeaturePlot(sce) %>%
        LabelPoints(points = top10_var, repel = TRUE) + theme_pubr(16)
    dev.off()
}, step_name = "find_var_genes", dependency = "normalize")

Scaling

To make the visualization within a reasonable range, scaling is performed. This changes the expression data points to be centered at 0 with standard deviation of 1.

appendStep(sal) <- LineWise(code = {
    sce <- ScaleData(sce, features = rownames(sce))
}, step_name = "scaling", dependency = "find_var_genes")

PCA

appendStep(sal) <- LineWise(code = {
    # only use the top 2000 genes, first 50 PCs (default)
    sce <- RunPCA(sce, features = VariableFeatures(object = sce),
        npcs = 50)
    # we can use following command to see first 5 genes in
    # each PC
    print(sce$pca, dims = 1:5, nfeatures = 5)
}, step_name = "pca", dependency = "scaling")

Plot PCA results

appendStep(sal) <- LineWise(code = {
    # plot PCA overview
    png("results/pca_overview.png", 500, 500)
    DimPlot(sce, reduction = "pca")
    dev.off()
    # plot top contributed genes in PC 1 and 2
    png("results/pca_loadings.png", 700, 550)
    VizDimLoadings(sce, dims = 1:2, reduction = "pca")
    dev.off()
    # we can also use heatmap to show top genes in
    # different PCs
    png("results/pca_heatmap.png", 700, 700)
    DimHeatmap(sce, dims = 1:6, cells = ncol(sce)/5, balanced = TRUE,
        slot = "scale.data")
    dev.off()
}, step_name = "pca_plots", dependency = "pca")

Find optimal PCs

After PCA, the dimension has reduced from tens of thousands (features) to 50. Before, we do the clustering, we can further reduced the PCs to only a few important ones, so the computation work can be easier. How to choose the best number of PCs is hard to decide. Here, the JackStraw plot and Elbow plot can be good ways to help us make the decision.

appendStep(sal) <- LineWise(code = {
    # for demo purposes, only a few replicates are used to
    # speed up the calculation in your real data, use
    # larger number like 100, etc.
    sce <- JackStraw(sce, num.replicate = 30)
    sce <- ScoreJackStraw(sce, dims = 1:20)
    png("results/jackstraw.png", 660, 750)
    JackStrawPlot(sce, dims = 1:20)
    dev.off()

    png("results/elbow.png", 500, 500)
    ElbowPlot(sce)
    dev.off()
}, step_name = "choose_pcs", dependency = "pca")

There is a gap of values between PCs 1-6 and other PCs. Therefore PCs 1-6 are the most important. One could also include PCs 7-13 since they have \(p\leq0.05\). One would need to adjust the PC choice based on different datasets.

The Elbow plot also shows there is turning point around PC 7-8. All other following PCs did not change too much.

It seems we should choose PC 6 or 7 as the cutoff point based on plots above. However, Seurat documents recommend to combine methods above with GSEA. Some cell populations may relate to genes in later PCs, e.g. dendritic cell and NK aficionados are linked to PCs 12, 13. Therefore, if your computational resources allow, it would be good to include a few more PCs.

The Seurat documents recommend to repeat with 10, 15 or PCs. However, in our experiences, the results did not change significantly.

In the downstream analysis, this template choose 10 PCs.

Clustering

Seurat uses graph-based methods like KNN to find the clusters.

appendStep(sal) <- LineWise(code = {
    sce <- FindNeighbors(sce, dims = 1:10)
    # resolution 0.4-1.2 good for 3000 cells, if you have
    # more cells, increase the number will give you more
    # clusters
    sce <- FindClusters(sce, resolution = 0.5)
}, step_name = "find_clusters", dependency = "pca")

Visualize clusters with UMAP/tSNE

appendStep(sal) <- LineWise(code = {
    sce <- RunUMAP(sce, dims = 1:20)
    p_umap <- DimPlot(sce, reduction = "umap", label = TRUE)
    sce <- RunTSNE(sce, dims = 1:20)
    p_tsne <- DimPlot(sce, reduction = "tsne", label = TRUE)
    png("results/plot_clusters.png", 1000, 570)
    p_umap + p_tsne
    dev.off()
}, step_name = "plot_cluster", dependency = "find_clusters")

Find cluster biomarkers

Find genes that represent different clusters.

appendStep(sal) <- LineWise(code = {
    # find markers for every cluster compared to all
    # remaining cells, report only the positive ones
    sce.markers <- FindAllMarkers(sce, only.pos = TRUE, min.pct = 0.25,
        logfc.threshold = 0.25)
    sce.markers %>%
        group_by(cluster) %>%
        top_n(n = 2, wt = avg_log2FC)
}, step_name = "find_markers", dependency = "find_clusters")

Visualize markers

appendStep(sal) <- LineWise(code = {
    png("results/vlnplot.png", 600, 600)
    VlnPlot(sce, features = c("MS4A1", "CD79A"))
    dev.off()

    png("results/marker_features.png", 700, 500)
    FeaturePlot(sce, features = c("MS4A1", "GNLY", "CD3E", "CD14"))
    dev.off()

    # plot top 10 DEG genes in each cluster as a heatmap
    top10_markers <- sce.markers %>%
        group_by(cluster) %>%
        top_n(n = 10, wt = avg_log2FC)
    png("results/marker_heatmap.png", 1100, 700)
    DoHeatmap(sce, features = top10_markers$gene) + NoLegend()
    dev.off()
}, step_name = "plot_markers", dependency = "find_markers")

Classify cell types

There are a few ways one can classify different clusters into different cell types. The best way is you know what are the markers for targeting cell types. CellMarker(http://biocc.hrbmu.edu.cn/CellMarker/) is a great source to find markers of different cell types. If you do not know the markers, use singleR package may be helpful. This package automatically classify clusters into cell types. However, the accuracy is not promised.

For example, if we know the markers of different cell types as following:

appendStep(sal) <- LineWise(code = {
    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(sce)
    sce <- RenameIdents(sce, new.cluster.ids)
    png("results/marker_labels.png", 700, 700)
    DimPlot(sce, reduction = "umap", label = TRUE, pt.size = 0.5) +
        NoLegend()
    dev.off()

}, step_name = "label_cell_type", dependency = c("plot_cluster",
    "find_markers"))

Workflow session

appendStep(sal) <- LineWise(code = {
    sessionInfo()
}, step_name = "wf_session", dependency = "label_cell_type")

Tools used

To check command-line tools used in this workflow, use listCmdTools, and use listCmdModules to check if you have a modular system.

The following code will print out tools required in your custom SPR project in the report. In case you are running the workflow for the first time and do not have a project yet, or you just want to browser this workflow, following code displays the tools required by default.

if (file.exists(file.path(".SPRproject", "SYSargsList.yml"))) {
    local({
        sal <- systemPipeR::SPRproject(resume = TRUE)
        systemPipeR::listCmdTools(sal)
        systemPipeR::listCmdModules(sal)
    })
} else {
    cat(crayon::blue$bold("Tools and modules required by this workflow are:\n"))
    cat(c("NA"), sep = "\n")
}

## Tools and modules required by this workflow are:
## NA

Session Info

This is the session information for rendering this report. To access the session information of workflow running, check HTML report of renderLogs.

sessionInfo()

## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 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-r0.3.32.so;  LAPACK version 3.12.0
## 
## 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 
## [6] methods   base     
## 
## other attached packages:
## [1] BiocStyle_2.41.0
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.39       R6_2.6.1           
##  [3] codetools_0.2-20    fastmap_1.2.0      
##  [5] xfun_0.61           maketools_1.3.2    
##  [7] cachem_1.1.0        knitr_1.52         
##  [9] htmltools_0.5.9     rmarkdown_2.32     
## [11] buildtools_1.0.0    lifecycle_1.0.5    
## [13] cli_3.6.6           sass_0.4.10        
## [15] jquerylib_0.1.4     compiler_4.6.1     
## [17] sys_3.4.3           tools_4.6.1        
## [19] bslib_0.12.0        evaluate_1.0.5     
## [21] yaml_2.3.12         formatR_1.14       
## [23] otel_0.2.0          BiocManager_1.30.27
## [25] crayon_1.5.3        jsonlite_2.0.0     
## [27] rlang_1.3.0