dplyr::count misses factor levels: a case in comparing scRNAseq cell type abundance

It is very common to see in the scRNAseq papers that the authors compare cell type abundance across groups (e.g., treatment vs control, responder vs non-responder). Let’s create some dummy data. library(tidyverse) set.seed(23) # we have 6 treatment samples and 6 control samples, 3 clusters A,B,C # but in the treatment samples, cluster C is absent (0 cells) in sample7 sample_id<- c(paste0("sample", 1:6, "_control", rep(c("_A","_B","_C"),each = 6)), paste0("sample", 8:12, "_treatment", rep(c("_A","_B", "_C"), each = 5))) sample_id<- c(sample_id, "sample7_treatment_A", "sample7_treatment_B") cell_id<- paste0("cell", 1:20000) cell_df<- tibble::tibble(sample_id = sample(sample_id, size = length(cell_id), replace = TRUE), cell_id = cell_id) %>% tidyr::separate(sample_id, into = c("sample_id", "group", "clusterid"), sep= "") cell_num<- cell_df %>% group_by(group, cluster_id, sample_id)%>% summarize(n=n()) cell_num ## # A tibble: 35 x 4 ## # Groups: group, cluster_id [6] ## group cluster_id sample_id n ## <chr> <chr> <chr> <int> ## 1 control A sample1 551 ## 2 control A sample2 546 ## 3 control A sample3 544 ## 4 control A sample4 585 ## 5 control A sample5 588 ## 6 control A sample6 542 ## 7 control B sample1 550 ## 8 control B sample2 562 ## 9 control B sample3 574 ## 10 control B sample4 563 ## # … with 25 more rows total_cells<- cell_df %>% group_by(sample_id) %>% summarise(total = n()) total_cells ## # A tibble: 12 x 2 ## sample_id total ## <chr> <int> ## 1 sample1 1673 ## 2 sample10 1713 ## 3 sample11 1691 ## 4 sample12 1696 ## 5 sample2 1633 ## 6 sample3 1700 ## 7 sample4 1711 ## 8 sample5 1768 ## 9 sample6 1727 ## 10 sample7 1225 ## 11 sample8 1720 ## 12 sample9 1743 join the two dataframe to get percentage of cells per cluster per sample

customize FeaturePlot in Seurat for multi-condition comparisons using patchwork

Seurat is great for scRNAseq analysis and it provides many easy-to-use ggplot2 wrappers for visualization. However, this brings the cost of flexibility. For example, In FeaturePlot, one can specify multiple genes and also split.by to further split to multiple the conditions in the meta.data. If split.by is not NULL, the ncol is ignored so you can not arrange the grid. This is best to understand with an example. library(dplyr) library(Seurat) library(patchwork) library(ggplot2) # Load the PBMC dataset pbmc.

Monty Hall problem- a peek through simulation

I am taking this STATE-80 course from Harvard Extension School. This course teaches commonly used distributions and probability theory. The instructor Hatch is a really good teacher and he uses simulation for all the demonstrations along with the formulas. In week 6, we revisited the Monty Hall problem which we played on the first day of class. If you have not heard about it, I quoted from the wiki: Suppose you’re on a game show, and you’re given the choice of three doors: Behind one door is a car; behind the others, goats.

Modeling single cell RNAseq data with multinomial distribution

I was reading Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model. In the paper, the authors model the scRNAseq counts using a multinomial distribution. I was using negative binomial distribution for modeling in my last post, so I asked the question on twitter: for modeling RNAseq counts, what’s the difference/advantages using negative binomial and multinomial distribution? — Ming Tang (@tangming2005) November 26, 2019 some quotes from the answers I get from Matthew

negative bionomial distribution in (single-cell) RNAseq

This post is inspired by two posts written by Valentine Svensson: http://www.nxn.se/valent/2017/11/16/droplet-scrna-seq-is-not-zero-inflated http://www.nxn.se/valent/2018/1/30/count-depth-variation-makes-Poisson-scrna-seq-data-negative-binomial The original ipython notebook can be found at https://github.com/vals/Blog/blob/master/171116-zero-inflation/Negative%20control%20analysis.ipynb Thanks for writing those and put both the data and code in public. After I read Droplet scRNA-seq is not zero-inflated by Valentine Svensson, I want to gain some understanding of it. This post is an effort to replicate some of the analysis in the preprint using R. The original analysis was carried out in python.

clustering scATACseq data: the TF-IDF way

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.

plot 10x scATAC coverage by cluster/group

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.

Use docopt to write command line R utilities

I was writing an R script to plot the ATACseq fragment length distribution and wanted to turn the R script to a command line utility. I then (re)discovered this awesome docopt.R. One just needs to write the help message the you want to display and docopt() will parse the options, arguments and return a named list which can be accessed inside the R script. check http://docopt.org/ for more information as well.

Evaluating single cell RNAseq cluster stability

An R package for evaluating and visualizing scRNAseq cluster stability

Three gotchas when using R for Genomic data analysis

During my daily work with R for genomic data analysis, I encountered several instances that R gives me some (bad) surprises. 1. The devil 1 and 0 coordinate system read detail here https://github.com/crazyhottommy/DNA-seq-analysis#tips-and-lessons-learned-during-my-dna-seq-data-analysis-journey some files such as bed file is 0 based. Two genomic regions: chr1 0 1000 chr1 1001 2000 when you import that bed file into R using rtracklayer::import(), it will become chr1 1 1000 chr1 1002 2000 The function convert it to 1 based internally (R is 1 based unlike python).