Context and Problem In scRNA-seq, each cell is sequenced individually, allowing for the analysis of gene expression at the single-cell level. This provides a wealth of information about the cellular identities and states. However, the high dimensionality of the data (thousands of genes) and the technical noise in the data can lead to challenges in accurately clustering the cells. Over-clustering is one such challenge, where cells that are biologically similar are clustered into distinct clusters.

In Single-cell RNAseq analysis, there is a step to find the marker genes for each cluster. The output from Seurat FindAllMarkers has a column called avg_log2FC. It is the gene expression log2 fold change between cluster x and all other clusters. How is that calculated? In this tweet thread by Lior Pachter, he said that there was a discrepancy for the logFC changes between Seurat and Scanpy: Actually, both Scanpy and Seurat calculate it wrong.

Dotplots are very popular for visualizing single-cell RNAseq data. In essence, the dot size represents the percentage of cells that are positive for that gene; the color intensity represents the average gene expression of that gene in a cell type. It is easy to plot one using Seurat::dotplot or Sccustomize::clustered_dotplot. However, when you have multiple groups/conditions in your data and you want to visualize it by groups, it is not that straightforward.

What is pseduobulk? Many of you have heard about bulk-RNAseq data. What is pseduobulk? Single-cell RNAseq can profile the gene expression at single-cell resolution. For differential expression, psedobulk seems to perform really well(see paper muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data). To create a pseudobulk, one can artificially add up the counts for cells from the same cell type of the same sample. In this blog post, I’ll guide you through the art of creating pseudobulk data from scRNA-seq experiments.

Download the data https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE116256 cd data/GSE116256 wget https://ftp.ncbi.nlm.nih.gov/geo/series/GSE116nnn/GSE116256/suppl/GSE116256_RAW.tar tar xvf GSE116256_RAW.tar rm GSE116256_RAW.tar Depending on how the authors upload their data. Some authors may just upload the merged count matrix file. This is the easiest situation. In this dataset, each sample has a separate set of matrix (*dem.txt.gz), features and barcodes. Total, there are 43 samples. For each sample, it has an associated metadata file (*anno.txt.gz) too. You can inspect the files in command line:

I tweeted it at https://twitter.com/tangming2005/status/1679120948140572672 I got asked to put all my posts in a central place and I think it is a good idea. And here it is! Benchmarking integration of single-cell differential expression Benchmarking atlas-level data integration in single-cell genomics A review of computational strategies for denoising and imputation of single-cell transcriptomic data Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution

introduce ggside using single cell data The ggside R package provides a new way to visualize data by combining the flexibility of ggplot2 with the power of side-by-side plots. We will use a single cell dataset to demonstrate its usage. ggside allows users to create side-by-side plots of multiple variables, such as gene expression, cell type, and experimental conditions. This can be helpful for identifying patterns and trends in scRNA-seq data that would be difficult to see in individual plots.

Sign up for my newsletter to not miss a post like this https://divingintogeneticsandgenomics.ck.page/newsletter Single-cell spatial transcriptome data is a new and advanced technology that combines the study of individual cells’ genes and their location in a tissue to understand the complex cellular and molecular differences within it. This allows scientists to investigate how genes are expressed and how cells interact with each other with much greater detail than before.

In my last blog post, I showed that pearson gene correlation for single-cell data has flaws because of the sparsity of the count matrix. One way to get around it is to use the so called meta-cell. One can use KNN to find the K nearest neighbors and collapse them into a meta-cell. You can implement it from scratch, but one should not re-invent the wheel. For example, you can use metacells.

This is an extension of my last blog post marker gene selection using logistic regression and regularization for scRNAseq. Let’s use the same PBMC single-cell RNAseq data as an example. Load libraries library(Seurat) library(tidyverse) library(tidymodels) library(scCustomize) # for plotting library(patchwork) Preprocess the data

Load the PBMC dataset pbmc.data <- Read10X(data.dir = "~/blog_data/filtered_gene_bc_matrices/hg19/") # Initialize the Seurat object with the raw (non-normalized data). pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.