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<h1 class="title toc-ignore">Human dermal fibroblast clonality project</h1>
<h4 class="author"><em>Davis J. McCarthy</em></h4>
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<p><strong>Last updated:</strong> 2018-08-27</p>
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<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>Repository version:</strong> <a href="https://github.com/davismcc/fibroblast-clonality/tree/678546d87f50bae3c1430f12e82c511a6eecdf6f" target="_blank">678546d</a> </summary></p>
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. <br><br> 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 <code>wflow_publish</code> or <code>wflow_git_commit</code>). 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:
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<p></details></p>
<hr />
<div id="project-overview" class="section level2">
<h2>Project overview</h2>
<p>This project investigates clonality in human dermal fibroblast cell populations in 32 cell lines from distinct donors, using bulk whole-exome sequencing and single-cell RNA-sequencing data.</p>
<p><strong>Key findings:</strong></p>
<ul>
<li>A novel approach for integrating DNA-seq and single-cell RNA-seq data to reconstruct clonal substructure and single-cell transcriptomes.</li>
<li>A new computational method, <a href="github.com/PMBio/cardelino">cardelino</a>, to map single-cell RNA-seq profiles to clones.</li>
<li>Evidence for non-neutral evolution of clonal populations in human fibroblasts.</li>
<li>Proliferation and cell cycle pathways are commonly distorted in mutated clonal populations, with implications for cancer and ageing.</li>
</ul>
<p>For a richer overview, see the <a href="about.html">About</a> page.</p>
</div>
<div id="data-pre-processing" class="section level2">
<h2>Data pre-processing</h2>
<p>The data pre-processing for this project from the raw data described above is complicated and computationally expensive, so this repository does not reproduce the data pre-processing in an automated way. However, we provide the source code for the <a href="https://snakemake.readthedocs.io/en/stable/">Snakemake</a> workflow for data pre-processing in this repository. Docker images providing the computing environment and software used are publicly available, split into an image for command line <a href="hub.docker.com/r/davismcc/fibroblast-clonality/">bioinformatics tools</a> and an <a href="hub.docker.com/r/davismcc/r-singlecell-img/">R installation</a> with necessary packages installed.</p>
<p>If you would like to pre-process the data from raw reads to results as we have, please consult our description of <a href="data_preprocessing.html">how to run</a> the workflow.</p>
</div>
<div id="analyses" class="section level2">
<h2>Analyses</h2>
<p>Here we present the reproducible the results of our analyses. They were generated by rendering the <a href="https://github.com/davismcc/fibroblast-clonality/tree/master/analysis">R Markdown documents</a> into webpages available at the links below.</p>
<p>The results presented in the paper were produced with these analyses.</p>
<ol style="list-style-type: decimal">
<li><p><a href="simulations.html">Simulation results.</a></p></li>
<li><p><a href="overview_lines.html">Overview of lines.</a></p></li>
<li><p><a href="selection_models.html">Selection models.</a></p></li>
<li><p><a href="clone_prevalences.html">Analysis of clonal prevalences.</a></p></li>
<li><p><a href="analysis_for_joxm.html">Analysis for the example cell line <em>joxm</em>.</a></p></li>
<li><p><a href="variance_components.html">Variance components analysis.</a></p></li>
<li><p><a href="differential_expression.html">Differential expression analysis.</a></p></li>
<li><p><a href="mutated_genes.html">Analysis of effects of somatic variants on cis gene expression.</a></p></li>
</ol>
</div>
<div id="data-availability" class="section level2">
<h2>Data availability</h2>
<p>This is a complicated project, and reproducing all of the results presented, especially from raw data is highly non-trivial. Nevertheless, we have made all data available so that everything is entirely reproducible.</p>
<p>Single-cell RNA-seq data have been deposited in the <a href="www.ebi.ac.uk/arrayexpress">ArrayExpress</a> database at EMBL-EBI under accession number <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-7167">E-MTAB-7167</a>. Whole-exome sequencing data is available through the <a href="www.hipsci.org">HipSci portal</a>. Processed data and large results files are available from <a href="http://doi.org/10.5281/zenodo.1403510">Zenodo</a> with DOI 10.5281/zenodo.1403510.</p>
<p>To set up the project to reproduce our analyses, first clone the <a href="https://github.com/davismcc/fibroblast-clonality">source code repository</a> from GitHub. Next, download all of the reference, metadata and results files and add them to the (cloned) project folder with the following structure:</p>
<pre><code>.
├── data
│ ├── canopy
│ │ ├── canopy_results.*.rds
│ ├── cell_assignment
│ │ ├── cardelino_results.*.rds
│ ├── de_analysis_FTv62
│ │ ├── cellcycle_analyses
│ │ │ ├── filt_lenient.all_filt_sites.de_results_unstimulated_cells.cc.rds
│ │ │ ├── filt_lenient.cell_coverage_sites.de_results_unstimulated_cells.cc.rds
│ │ │ ├── filt_strict.all_filt_sites.de_results_unstimulated_cells.cc.rds
│ │ │ └── filt_strict.cell_coverage_sites.de_results_unstimulated_cells.cc.rds
│ │ ├── filt_lenient.all_filt_sites.de_results_unstimulated_cells.rds
│ │ ├── filt_lenient.cell_coverage_sites.de_results_unstimulated_cells.rds
│ │ ├── filt_strict.all_filt_sites.de_results_unstimulated_cells.rds
│ │ └── filt_strict.cell_coverage_sites.de_results_unstimulated_cells.rds
│ ├── donor_info_070818.txt
│ ├── donor_info_core.csv
│ ├── donor_neutrality.tsv
│ ├── exome-point-mutations
│ │ ├── high-vs-low-exomes.v62.ft.alldonors-filt_lenient.all_filt_sites.vep_most_severe_csq.txt
│ │ └── high-vs-low-exomes.v62.ft.filt_lenient-alldonors.txt.gz
│ ├── human_H_v5p2.rdata
│ ├── human_c2_v5p2.rdata
│ ├── human_c6_v5p2.rdata
│ ├── neg-bin-rsquared-petr.csv
│ ├── neutralitytestr-petr.tsv
| ├── sces
│ │ ├── sce_*.rds
│ ├── selection
│ │ ├── neg-bin-params-fit.csv
│ │ ├── neg-bin-rsquared-fit.csv
│ ├── simulations
│ │ ├── *.filt_lenient.cell_coverage_sites.mult.rds
│ │ ├── *.simulate.rds
│ └── variance_components
│ ├── covar_all.csv
│ ├── donorVar
│ │ ├── *.var_part.var1.csv
│ ├── fit_all_gene_highVar.csv
│ ├── fit_per_gene_highVar.csv
│ ├── gene_info_all.csv
│ └── logcnt_all.csv
├── metadata
│ ├── cell_metadata.csv
│ └── data_processing_metadata.tsv
├── references
│ ├── 1000G_phase1.indels.hg19.sites.vcf.gz
│ ├── GRCh37.p13.genome.ERCC92.fa
│ ├── Homo_sapiens.GRCh37.rel75.cdna.all.ERCC92.fa.gz
│ ├── Mills_and_1000G_gold_standard.indels.hg19.sites.vcf.gz
│ ├── dbsnp_138.hg19.biallelicSNPs.HumanCoreExome12.Top1000ExpressedIpsGenes.Maf0.01.HWE0.0001.HipSci.vcf.gz
│ ├── dbsnp_138.hg19.vcf.gz
│ ├── gencode.v19.annotation_ERCC.gtf
│ ├── hipsci.wec.gtarray.HumanCoreExome.imputed_phased.20170327.genotypes.allchr.fibro_samples_v2_filt_vars_sorted_oa.vcf.gz
│ ├── hipsci.wec.gtarray.HumanCoreExome.imputed_phased.20170327.genotypes.allchr.fibro_samples_v2_filt_vars_sorted_oa.vcf.gz.csi
│ └── knownIndels.intervals</code></pre>
<p>For simplicity, we ignore all the directories and files present in the source code repository (that you should have clones) to focus just on where you should <em>add</em> the files downloaded from Zenodo. Yes, it’s still complicated, but such is life.</p>
<p>There is a large number of <code>canopy_results.*.rds</code> files: these should be stored in the <code>data/canopy</code> directory. Similarly, all of the <code>cardelino_results.*.rds</code> files should be stored in <code>data/cell_assignment</code>. All of the SingleCellExperiment object files (<code>sce_*.rds</code>) should be stored in <code>data/sces</code>. Simulation results files (<code>*.mult.rds</code>; <code>*.simulate.rds</code>) should be stored in <code>data/simulations</code>. Variance components results should be stored in <code>data/variance_components</code> as shown above.</p>
<p>Differential expression results belong in <code>data/de_analysis_FTv62</code>.</p>
<p>Metadata files belong in <code>metadata</code>. Reference files belong in <code>references</code>.</p>
<p>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 <a href="data_preprocessing.html">these instructions</a>.</p>
<hr />
<p><a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.</p>
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