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

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.

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.

why this blog post? I saw a biorxiv paper titled A comparison of marker gene selection methods for single-cell RNA sequencing data Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Student’s t-test and logistic regression I am interested in using logistic regression to find marker genes and want to try fitting the model in the tidymodel ecosystem and using different regularization methods.

In scanpy, there is a function to create a stacked violin plot. There is no such function in Seurat, and many people were asking for this feature. e.g. https://github.com/satijalab/seurat/issues/300 https://github.com/satijalab/seurat/issues/463 The developers have not implemented this feature yet. In this post, I am trying to make a stacked violin plot in Seurat. The idea is to create a violin plot per gene using the VlnPlot in Seurat, then customize the axis text/tick and reduce the margin for each plot and finally concatenate by cowplot::plot_grid or patchwork::wrap_plots.

TSS enrichment score serves as an important quality control metric for ATACseq data. I want to write a script for single cell ATACseq data. From the Encode page: Transcription Start Site (TSS) Enrichment Score - The TSS enrichment calculation is a signal to noise calculation. The reads around a reference set of TSSs are collected to form an aggregate distribution of reads centered on the TSSs and extending to 1000 bp in either direction (for a total of 2000bp).

scATACseq data are very sparse. It is sparser than scRNAseq. To do clustering of scATACseq data, there are some preprocessing steps need to be done. I want to reproduce what has been done after reading the method section of these two recent scATACseq paper: A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility Darren et.al Cell 2018 Latent Semantic Indexing Cluster Analysis In order to get an initial sense of the relationship between individual cells, we first broke the genome into 5kb windows and then scored each cell for any insertions in these windows, generating a large, sparse, binary matrix of 5kb windows by cells for each tissue.

This post was inspired by Andrew Hill’s recent blog post. Inspired by some nice posts by @timoast and @tangming2005 and work from @10xGenomics. Would still definitely have to split BAM files for other tasks, so easy to use tools for that are super useful too! — Andrew J Hill (@ahill_tweets) April 13, 2019 Andrew wrote that blog post in light of my other recent blog post and Tim’s (developer of the almighty Seurat package) blog post.