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I recently was interviewed by the SEQanswers forum on single-cell RNAseq analysis.
In your opinion, what is the most challenging aspect of single-cell analysis? Every single-cell dataset is unique in terms of data quality and QC has to be carried out in a dataset specific manner. Cell annotation is still one of the most challenging steps.
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The problem df<- data.frame(id = c(1,2,3), value = c('x,y', 'z,w', 'a')) df #> id value #> 1 1 x,y #> 2 2 z,w #> 3 3 a we want to put x,y in the first row into two rows:
1, x
1, y
and put z,w into two rows too.
solution with R There is a neat function separate_rows that does exactly this in tidyr package:
I was asked this question very often: “Tommy, what’s the p-value cutoff should I use to determine the differentially expressed genes; what log2 Fold change cutoff should I use too?”
For single-cell RNAseq quality control, what’s the cutoff for mitochondrial content?
My answer is always: it depends. I was joking: determining a cutoff is 90% of the work a bioinformatician does.
Why is that?
Biology is more than just statistics.
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.
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In the last blog post, I showed you how to use salmon to get counts from fastq files downloaded from GEO. In this post, I am going to show you how to read in the .sf salmon quantification file into R; how to get the tx2gene.txt file and do DESeq2 for differential gene expression analysis. Let’s dive in!
library(tximport) library(dplyr) library(ggplot2) files<- list.
Install fastq-dl To easily download fastq from GEO or ENA, use fastq-dl
Assume you already have conda installed, do the following:
conda config –add channels conda-forge conda config –add channels bioconda conda create -n fastq_download -c conda-forge -c bioconda fastq-dl conda activate fastq_download Tip: use mamba if conda is too slow for you. They are all big snakes!!
We will use bulk RNAseq data from this GEO accession ID: https://www.
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.
There are some hidden gems beyond the typical programming skills that have been instrumental in my journey. These are the often-overlooked yet crucial practices that have empowered me to tackle challenges and make sense of data in meaningful ways.
Firstly, let’s talk about patience. It’s not as glamorous as diving straight into analysis, but taking the time for thorough quality control is invaluable. Before you get carried away, understand the experimental design.
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.
Everyone is unique. Only you can talk about the story about yourself, and I realized that no matter how many times I have told my story, I have to tell it again, again, again, and again. Because no matter how many times I tell it, there is always someone who hear my story the first time. I hope it can inspire more people every time I tell it.
Fast backward 37 years ago, 1986.