In saeyslab/muscatWrapper: Wrapper functions around muscat for differential expression analysis of multi-sample multi-condition scRNAseq datasets

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In this vignette, you can learn how to use muscatWrapper only for visualization and not for DE analysis itself. muscatWrapper visualization can be performed if you have multi-sample, multi-group single-cell data. The absolute minimum of meta data you need to have, are following columns indicating for each cell: the group, sample and cell type. This vignette is for users who want to perform the DE analysis in their own way, but still want to use the muscatWrapper visualizations easily.

As example expression data, we will use data from Puram et al. of the tumor microenvironment in head and neck squamous cell carcinoma (HNSCC) [See @puram_single-cell_2017] . The groups we have here are tumors scoring high for a partial epithelial-mesenschymal transition (p-EMT) program vs low-scoring tumors.

The different steps of the MultiNicheNet analysis are the following:

• 1. Preparation of the analysis: load packages, read in the single-cell expression data, and read in geneset of interest
• 1. Prepare muscatWrapper-specific objects needed for visualization
• 1. Gene expression visualization

In this vignette, we will demonstrate all these steps in detail.

Step 0: Preparation of the analysis: load packages, read in the single-cell expression data, and read in geneset of interest

IMPORTANT: The current implementation of the muscat wrapper starts from a SingleCellExperiment object, therefore we will need to load the SingleCellExperiment library. If you start from a Seurat object, you can convert it easily to a SingleCellExperiment via sce = Seurat::as.SingleCellExperiment(seurat_obj, assay = "RNA").

library(SingleCellExperiment)
library(dplyr)
library(ggplot2)
library(muscatWrapper)

In this case study, we want to study differences in expression between pEMT-high and pEMT-low tumors. The meta data columns that indicate the pEMT status of tumors are 'pEMT' and 'pEMT_fine', cell type is indicated in the 'celltype' column, and the sample is indicated by the 'tumor' column.

User adaptation required

sce = readRDS(url("https://zenodo.org/record/5196144/files/sce_hnscc.rds"))
scater::plotReducedDim(sce, dimred = "UMAP", colour_by = "celltype")
scater::plotReducedDim(sce, dimred = "UMAP", colour_by = "tumor")
scater::plotReducedDim(sce, dimred = "UMAP", colour_by = "pEMT")
scater::plotReducedDim(sce, dimred = "UMAP", colour_by = "pEMT_fine")

Now we will define in which metadata columns we can find the group, sample and cell type IDs

For the group_id in this vignette, we choose the 'pEMT' column instead of 'pEMT_fine'.

Because DE analysis is here done with another tool than muscatWrapper, the list of genes to visualize should be given here as well

User adaptation required

sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
geneset = c("IL20","IL24","TGFBI") ## user input required!!! eg from DE analysis with another tool

Step 1: Prepare muscatWrapper-specific objects needed for visualization

We will now calculate the cell type abundance table and make the visualizations:

abundance_output = get_abundance_info(sce, sample_id, group_id, celltype_id, min_cells = 10, covariates = NA)

celltype_info = get_expression_info(sce = sce, sample_id = sample_id, group_id = group_id, celltype_id = celltype_id, covariates = NA)

Step 2: Gene expression visualization

Visualize the expression of some DE genes: here as example: genes higher in the pEMT high tumors in the Malignant cell type

celltype_oi = "Malignant"
group_oi = "High"

First, make a violin plot

gene_oi = geneset[1]

violin_plot = make_DEgene_violin_plot(sce = sce, gene_oi = gene_oi, celltype_oi = celltype_oi, group_id = group_id, sample_id = sample_id, celltype_id = celltype_id)
violin_plot

Then a Dotplot

dotplots = make_DEgene_dotplot_pseudobulk(genes_oi = geneset, celltype_info = celltype_info, abundance_data = abundance_output$abundance_data, celltype_oi = celltype_oi)
dotplots$pseudobulk_plot 
dotplots$singlecell_plot

References

Puram, Sidharth V., Itay Tirosh, Anuraag S. Parikh, Anoop P. Patel, Keren Yizhak, Shawn Gillespie, Christopher Rodman, et al. 2017. “Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer.” Cell 171 (7): 1611–1624.e24. https://doi.org/10.1016/j.cell.2017.10.044.

saeyslab/muscatWrapper documentation built on March 11, 2023, 6:14 p.m.