Last updated: 2021-06-02

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Knit directory: KEJP_2020_splatPop/

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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (public/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd bd0c8b0 cazodi 2021-05-17 updates to 10x neuroseq and ss2 ipsc examples
html bd0c8b0 cazodi 2021-05-17 updates to 10x neuroseq and ss2 ipsc examples
html 0286c19 cazodi 2021-02-17 re-knit after gitlab ci
Rmd d24c101 cazodi 2021-02-17 ipsc ss2 preprocessing
html d24c101 cazodi 2021-02-17 ipsc ss2 preprocessing
Rmd b200a1f cazodi 2020-03-18 removing accidental commits by christina…
Rmd 9057b09 cazodi 2020-03-18 Revert “initial repo change”
Rmd 0c6d3c6 cazodi 2020-03-18 initial repo change
Rmd c57ac08 Christina Azodi 2019-11-26 First init
Rmd b1f309c Davis McCarthy 2019-02-06 Updating template with improvements.
Rmd d73fd37 Davis McCarthy 2019-01-10 Initial commit

Overview

This project demonstrates use cases for splatPop, an extension of the splat model implemented in Splatter, that allows for the simulation of population-scale single-cell RNA-sequencing data.

The splatPop functions are available in the splatter package (v1.14.1+), available in Bioconductor.

Key features:

Data pre-processing

The empirical data used as reference data in this study were downloaded already processed by the original authors.

Analyses

The results presented in the paper were produced with the following reproducible analyses. They were generated by rendering the R Markdown documents into web pages available at the links below. Analyses are organized by the empirical dataset was used for reference. Details about where to download the empirical data are in the preprocessing page for each dataset.

  1. 10x Differentiating iPSCs (floor plate progenitors and dopaminergic neurons)
  2. SmartSeq2 iPSCs from HipSci (multiple batches)
  3. 10x Fibroblasts (with and without bleomycin to induce fibrosis)
  4. Example use cases

eQTL mapping was done using the best practices as described in Cuomo, Alveri, Azodi, et. al. Snakemake was used to automate that workflow.

Reference Data

Reference VCF and GFF files for chromosome 22 were downloaded and processed as follows.

Reference VCF

# Download
wget http://hgdownload.cse.ucsc.edu/gbdb/hg19/1000Genomes/phase3/ALL.chr22.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz

mv ALL.chr22.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz chr22.vcf.gz

# Filter
conda activate plink
plink --vcf chr22.vcf.gz --biallelic-only --snps-only --geno 0 --maf 0.05 --hwe 0.00001 --indep-pairwise 1600 5 0.75 --keep keep_samples.txt --out chr22.filt --recode

plink --vcf chr22.vcf.gz --extract chr22.filt.prune.in --keep keep_samples.txt --recode vcf --out chr22.filtered

# Output: chr22.filtered.vcf**

Reference GFF

## Download
wget http://ftp.ensembl.org/pub/release-99/gff3/homo_sapiens/Homo_sapiens.GRCh38.99.chromosome.22.gff3.gz

## Filter
gunzip Homo_sapiens.GRCh38.99.chromosome.22.gff3.gz
awk '$3 == "gene"' Homo_sapiens.GRCh38.99.chromosome.22.gff3 > Homo_sapiens.GRCh38.99.chromosome.22.genes.gff3
mv Homo_sapiens.GRCh38.99.chromosome.22.genes.gff3 chr22.genes.gff3

# Output: chr22.genes.gff3

With the data downloaded and organised as above, you will be able to reproduce the analyses presented in the RMarkdown files linked to above and, if desired, even run the whole analysis pipeline from raw reads to results following these instructions.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


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