Bioinformatics

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.

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.

There was a paper on this topic: A New Online Computational Biology Curriculum. I am going to provide a biased list below (I have read most of the books if not all). I say it is biased because you will see many books of R are from Hadely Wickham. I now use tidyverse most of the time. Unix I suggest people who want to learn bioinformatics starting to learn unix commands first.

Readings links to read: https://www.bioconductor.org/developers/package-guidelines/#rcode https://github.com/Bioconductor/Contributions use container https://github.com/Bioconductor/bioconductor_full I am following the last link. pull the container docker pull bioconductor/bioconductor_full:devel docker images REPOSITORY TAG IMAGE ID CREATED SIZE bioconductor/bioconductor_full devel ae3ec2be7376 3 hours ago 5.7GB seuratv3 latest 9b358ab1fd63 2 days ago 2.76GB It is 5.7G in size. start the Rstuido from the image. I have another Rstudio instance using port 8787, let me use a different one (e.

TSS enrichment score serves as an important quality control metric for ATACseq data. I want to write a script for single cell ATACseq data. From the Encode page: Transcription Start Site (TSS) Enrichment Score - The TSS enrichment calculation is a signal to noise calculation. The reads around a reference set of TSSs are collected to form an aggregate distribution of reads centered on the TSSs and extending to 1000 bp in either direction (for a total of 2000bp).

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.

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.