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.

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.

I am interested in learning more on matrix factorization and its application in scRNAseq data. I want to shout out to this paper: Enter the Matrix: Factorization Uncovers Knowledge from Omics by Elana J. Fertig group. A matrix is decomposed to two matrices: the amplitude matrix and the pattern matrix. You can then do all sorts of things with the decomposed matrices. Single cell matrix is no special, one can use the matrix factorization techniques to derive interesting biological insights.

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.

I want to split the PBMC scATAC bam from 10x by cluster id. So, I can then make a bigwig for each cluster to visualize in IGV. The first thing I did was googling to see if anyone has written such a tool (Do not reinvent the wheels!). People have done that because I saw figures from the scATAC papers. I just could not find it. Maybe I need to refine my googling skills.