ScRNAseq

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: tdtomato_cre.fa: >tdtomato dna:chromosome chromosome:GRCm38:tdtomato:1:1431:1 REF ATGGTGAGCAAGGGCGAGGAGGTCATCAAAGAGTTCATGCGCTTCAAGGTGCGCATGGAGGGCTCCATGAACGGCCACGAGTTCGAGATCGAGGGCGAGGGCGAGGGCCGCCCCTACGAGGGCACCCAGACCGCCAAGCTGAAGGTGACCAAGGGCGGCCCCCTGCCCTTCGCCTGGGACATCCTGTCCCCCCAGTTCATGTACGGCTCCAAGGCGTACGTGAAGCACCCCGCCGACATCCCCGATTACAAGAAGCTGTCCTTCCCCGAGGGCTTCAAGTGGGAGCGCGTGATGAACTTCGAGGACGGCGGTCTGGTGACCGTGACCCAGGACTCCTCCCTGCAGGACGGCACGCTGATCTACAAGGTGAAGATGCGCGGCACCAACTTCCCCCCCGACGGCCCCGTAATGCAGAAGAAGACCATGGGCTGGGAGGCCTCCACCGAGCGCCTGTACCCCCGCGACGGCGTGCTGAAGGGCGAGATCCACCAGGCCCTGAAGCTGAAGGACGGCGGCCACTACCTGGTGGAGTTCAAGACCATCTACATGGCCAAGAAGCCCGTGCAACTGCCCGGCTACTACTACGTGGACACCAAGCTGGACATCACCTCCCACAACGAGGACTACACCATCGTGGAACAGTACGAGCGCTCCGAGGGCCGCCACCACCTGTTCCTGGGGCATGGCACCGGCAGCACCGGCAGCGGCAGCTCCGGCACCGCCTCCTCCGAGGACAACAACATGGCCGTCATCAAAGAGTTCATGCGCTTCAAGGTGCGCATGGAGGGCTCCATGAACGGCCACGAGTTCGAGATCGAGGGCGAGGGCGAGGGCCGCCCCTACGAGGGCACCCAGACCGCCAAGCTGAAGGTGACCAAGGGCGGCCCCCTGCCCTTCGCCTGGGACATCCTGTCCCCCCAGTTCATGTACGGCTCCAAGGCGTACGTGAAGCACCCCGCCGACATCCCCGATTACAAGAAGCTGTCCTTCCCCGAGGGCTTCAAGTGGGAGCGCGTGATGAACTTCGAGGACGGCGGTCTGGTGACCGTGACCCAGGACTCCTCCCTGCAGGACGGCACGCTGATCTACAAGGTGAAGATGCGCGGCACCAACTTCCCCCCCGACGGCCCCGTAATGCAGAAGAAGACCATGGGCTGGGAGGCCTCCACCGAGCGCCTGTACCCCCGCGACGGCGTGCTGAAGGGCGAGATCCACCAGGCCCTGAAGCTGAAGGACGGCGGCCACTACCTGGTGGAGTTCAAGACCATCTACATGGCCAAGAAGCCCGTGCAACTGCCCGGCTACTACTACGTGGACACCAAGCTGGACATCACCTCCCACAACGAGGACTACACCATCGTGGAACAGTACGAGCGCTCCGAGGGCCGCCACCACCTGTTCCTGTACGGCATGGACGAGCTGTACAAGTAA >cre dna:chromosome chromosome:GRCm38:cre:1:1032:1 REF ATGGCCAATTTACTGACCGTACACCAAAATTTGCCTGCATTACCGGTCGATGCAACGAGTGATGAGGTTCGCAAGAACCTGATGGACATGTTCAGGGATCGCCAGGCGTTTTCTGAGCATACCTGGAAAATGCTTCTGTCCGTTTGCCGGTCGTGGGCGGCATGGTGCAAGTTGAATAACCGGAAATGGTTTCCCGCAGAACCTGAAGATGTTCGCGATTATCTTCTATATCTTCAGGCGCGCGGTCTGGCAGTAAAAACTATCCAGCAACATTTGGGCCAGCTAAACATGCTTCATCGTCGGTCCGGGCTGCCACGACCAAGTGACAGCAATGCTGTTTCACTGGTTATGCGGCGGATCCGAAAAGAAAACGTTGATGCCGGTGAACGTGCAAAACAGGCTCTAGCGTTCGAACGCACTGATTTCGACCAGGTTCGTTCACTCATGGAAAATAGCGATCGCTGCCAGGATATACGTAATCTGGCATTTCTGGGGATTGCTTATAACACCCTGTTACGTATAGCCGAAATTGCCAGGATCAGGGTTAAAGATATCTCACGTACTGACGGTGGGAGAATGTTAATCCATATTGGCAGAACGAAAACGCTGGTTAGCACCGCAGGTGTAGAGAAGGCACTTAGCCTGGGGGTAACTAAACTGGTCGAGCGATGGATTTCCGTCTCTGGTGTAGCTGATGATCCGAATAACTACCTGTTTTGCCGGGTCAGAAAAAATGGTGTTGCCGCGCCATCTGCCACCAGCCAGCTATCAACTCGCGCCCTGGAAGGGATTTTTGAAGCAACTCATCGATTGATTTACGGCGCTAAGGATGACTCTGGTCAGAGATACCTGGCCTGGTCTGGACACAGTGCCCGTGTCGGAGCCGCGCGAGATATGGCCCGCGCTGGAGTTTCAATACCGGAGATCATGCAAGCTGGTGGCTGGACCAATGTAAATATTGTCATGAACTATATCCGTAACCTGGATAGTGAAACAGGGGCAATGGTGCGCCTGCTGGAAGATGGCGATTAG edit the genome.

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.

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.

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.

Background Please read the following before go ahead: what is docker? what is Rocker? what is singularity? from Harvard Research computing website: Odyssey has singularity installed. Why Singularity? There are some important differences between Docker and Singularity: Docker and Singularity have their own container formats. Docker containers may be imported to run via Singularity. Docker containers need root privileges for full functionality which is not suitable for a shared HPC environment.

single cell RNAseq Please read the following posts by Dave Tang. When I google, I always find his posts on top of the pages. Thanks for sharing your knowledge. https://davetang.org/muse/2018/06/06/10x-single-cell-bam-files/ https://davetang.org/muse/2018/08/09/getting-started-with-cell-ranger/ From the 10x manual: The final Single Cell 3’ Libraries contain the P5 and P7 primers used in Illumina bridge amplification PCR. The 10x Barcode and Read 1 (primer site for sequencing read 1) is added to the molecules during the GEMRT incubation.

An R package for evaluating and visualizing scRNAseq cluster stability