About

Last updated: 2018-11-09

workflowr checks: (Click a bullet for more information)

✔ R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

✔ Repository version: f98a31e

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    .vscode/
    Ignored:    code/.DS_Store
    Ignored:    data/raw/
    Ignored:    src/.DS_Store
    Ignored:    src/Rmd/.Rhistory

Untracked files:
    Untracked:  Snakefile_clonality
    Untracked:  Snakefile_somatic_calling
    Untracked:  code/analysis_for_garx.Rmd
    Untracked:  code/selection/
    Untracked:  code/yuanhua/
    Untracked:  data/canopy/
    Untracked:  data/cell_assignment/
    Untracked:  data/de_analysis_FTv62/
    Untracked:  data/donor_info_070818.txt
    Untracked:  data/donor_info_core.csv
    Untracked:  data/donor_neutrality.tsv
    Untracked:  data/exome-point-mutations/
    Untracked:  data/fdr10.annot.txt.gz
    Untracked:  data/human_H_v5p2.rdata
    Untracked:  data/human_c2_v5p2.rdata
    Untracked:  data/human_c6_v5p2.rdata
    Untracked:  data/neg-bin-rsquared-petr.csv
    Untracked:  data/neutralitytestr-petr.tsv
    Untracked:  data/sce_merged_donors_cardelino_donorid_all_qc_filt.rds
    Untracked:  data/sce_merged_donors_cardelino_donorid_all_with_qc_labels.rds
    Untracked:  data/sce_merged_donors_cardelino_donorid_unstim_qc_filt.rds
    Untracked:  data/sces/
    Untracked:  data/selection/
    Untracked:  data/simulations/
    Untracked:  data/variance_components/
    Untracked:  figures/
    Untracked:  output/differential_expression/
    Untracked:  output/donor_specific/
    Untracked:  output/line_info.tsv
    Untracked:  output/nvars_by_category_by_donor.tsv
    Untracked:  output/nvars_by_category_by_line.tsv
    Untracked:  output/variance_components/
    Untracked:  references/
    Untracked:  tree.txt

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.

Expand here to see past versions:

File	Version	Author	Date	Message
html	0540cdb	davismcc	2018-09-02	Build site.
html	f0ed980	davismcc	2018-08-31	Build site.
html	ca3438f	davismcc	2018-08-29	Build site.
Rmd	dc78a95	davismcc	2018-08-29	Minor updates to analyses.
html	e573f2f	davismcc	2018-08-27	Build site.
html	9ec2a59	davismcc	2018-08-26	Build site.
Rmd	cae617f	davismcc	2018-08-26	Updating simulation analyses
html	36acf15	davismcc	2018-08-25	Build site.
html	090c1b9	davismcc	2018-08-24	Build site.
html	02a8343	davismcc	2018-08-24	Build site.
Rmd	43f15d6	davismcc	2018-08-24	Adding data pre-processing workflow and updating analyses.
html	d2e8b31	davismcc	2018-08-19	Build site.
html	1489d32	davismcc	2018-08-17	Add html files
Rmd	6b5f8c7	davismcc	2018-08-17	Updating organisational pages.
html	9856275	davismcc	2018-08-07	Build site.
Rmd	5fc189d	davismcc	2018-08-07	Start workflowr project.

Cardelino : Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants

Key findings:

A new approach for integrating DNA-seq and single-cell RNA-seq data to reconstruct clonal substructure and single-cell transcriptomes.
A new computational method to map single-cell RNA-seq profiles to clones.
Evidence for non-neutral evolution of clonal populations in human fibroblasts.
Proliferation and cell cycle pathways are commonly distorted in mutated clonal populations, with implications for cancer and ageing.

Abstract

Decoding the clonal substructures of somatic tissues sheds light on cell growth, development and differentiation in health, ageing and disease. DNA-sequencing, either using bulk or using single-cell assays, has enabled the reconstruction of clonal trees from somatic variants. However, approaches to characterize phenotypic and functional variations between clones are not established.

Here we present cardelino (https://github.com/PMBio/cardelino), a computational method to assign single-cell transcriptome profiles to somatic clones using variant information contained in single-cell RNA-seq (scRNA-seq) data. After validating our model using simulations, we apply cardelino to matched scRNA-seq and exome sequencing data from 32 human dermal fibroblast lines

We identify hundreds of differentially expressed genes between cells assigned to different clones. These genes were frequently enriched for the cell cycle and pathways related to cell proliferation, and our data point to clone gene expression phenotypes that support previous work showing non-neutral somatic evolution in nominally healthy human skin cells.

Authors

The full author list is as follows:

Davis J. McCarthy^1,4,*, Raghd Rostom^1,2,*, Yuanhua Huang^1,*, Daniel J. Kunz^2,5,6, Petr Danecek², Marc Jan Bonder¹, Tzachi Hagai^1,2, HipSci Consortium, Wenyi Wang⁸, Daniel J. Gaffney², Benjamin D. Simons^5,6,7, Oliver Stegle^1,3,9,#, Sarah A. Teichmann^1,2,5,#

¹European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridge, UK; ²Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK; ³European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany; ⁴St Vincent’s Institute of Medical Research, Fitzroy, Victoria 3065, Australia. ⁵Cavendish Laboratory, Department of Physics, JJ Thomson Avenue, Cambridge, CB3 0HE, UK. ⁶The Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, CB2 1QN, UK. ⁷The Wellcome Trust/Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, UK. ⁸Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. ⁹Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.

^* These authors contributed equally to this work.

^# Corresponding authors.

This reproducible R Markdown analysis was created with workflowr 1.1.1