When it comes to make a heatmap, ComplexHeatmap by Zuguang Gu is my favorite. Check it out! You will be amazed on how flexible it is and the documentation is in top niche.
For Single-cell RNAseq, Seurat provides a DoHeatmap function using ggplot2. There are two limitations:
when your genes are not in the top variable gene list, the scale.data will not have that gene and DoHeatmap will drop those genes.
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  ## 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
I was using the tidyverse rocker image on HPC by singularity pull. see my previous post.
Everything was OK until I encountered problems installing jpeg and Cairo R packages. Later, I also had an error installing scRepertoire dependency gsl.
It turns out I have to install debian packages inside the container:
$ apt update && apt install -y –no-install-recommends libjpeg62-turbo-dev zlib1g-dev libpng-dev \ && apt install -y –no-install-recommends libx11-dev libcairo2-dev libxt-dev \ && apt install -y libgsl-dev However, singularity file system is read-only.
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.
In my last post, I tried to include transgenes to the cellranger reference and want to get the counts for the transgenes. However, even after I extended the Tdtomato and Cre with the potential 3’UTR, I still get very few cells express them. This is confusing to me.
My next thought is: maybe the STAR aligner is doing something weird that excluded those reads? At this point, I want to give kb-python, a python wrapper on kallisto and bustools a try.
The problem I am working on some 10x scRNAseq data from transgenic mouse. The cells express Tdtomato and Cre genes. I need to add those to the cellranger reference to get the counts for those two genes.
The journey to the solution Following https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/advanced/references#addgene
I created a fasta file for the two transgenes: tdTomato and Cre:
Using nested dataframe and list column has transformed my way of data wrangling in R. For more on this topic, I highly recommend purrr tutorial from Jenney Bryan.
In this post, I am going to show you how I use this to solve a problem for adding pct_in column from the differential scRNAseq result table.
I am going to use presto for differential gene expression test. presto performs a fast Wilcoxon rank sum test and auROC analysis.
It is the end of 2019. How time flies! It is a good time to reflect what I have achieved during the past year and what to look forward in 2020. I wrote a post for 2018 here. I am not the only one who has impostor syndrome :) It is important to celebrate your small successes/achievements by writing them down.
I taught a snakemake and scRNAseq workshop during the FAS informatics 2-week nanocourse.
In a typical “barnyard” experiment in which cells from different species are mixed before loading to the 10x controller, the identification of the species of origin after mapping/counting with the hybrid reference is a problem. People tend to use the ratio of reads mapped to each reference genome to determine which species a cell is from.
In this paper https://www.biorxiv.org/content/10.1101/630087v1.full
To deconvolute species, detect doublets and low quality cells, the mixed-species mapped data was used.