Last updated: 2021-02-28
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Knit directory: liver-disease-atlas/
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The results and the analysis scripts presented on this website ensures the reproducibility of all bioinformatics related findings presented in “Transcriptomic cross-species analysis of chronic liver disease reveals a consistent regulation pattern between humans and mice”. Please see below to get more information about the individual analysis and some tips on how to reproduce the results.
Mouse models are frequently used to study chronic liver diseases (CLD). To allow a better assessment of translational relevance, we quantified the similarity of prevalent mouse models to human CLD based on transcriptome data.
RNA-sequencing and gene array data from 372 patients with CLD (NAFLD, NASH, HCV, PBC, and PSC) were compared to acute and chronic mouse models with 227 mice and additionally 9 published gene sets of chronic mouse models. Genes consistently altered in humans and mice were mapped to specific liver cell types based on single-cell RNA-sequencing and validated by immunostaining.
Similarity of the top differentially expressed genes between humans and mice varied among the individual mouse models and depended on the period of damage induction. Some models, e.g. 12 months induction of damage by CCl4, reached high similarity to humans with 0.4 recall and 0.33 precision, respectively. Consistently upregulated genes between the chronic CCl4 mouse model and human data were enriched in inflammatory and developmental processes, and mapped to cholangiocytes, macrophages, endothelial and mesenchymal cells, while downregulated genes were enriched in metabolic functions and mapped to hepatocytes. Immunostaining confirmed selected consistent genes and their cell-type specificity. Upregulated genes in both acute and chronic mouse models showed a higher recall and precision with respect to human CLD than exclusively acute or chronic genes.
Our analyses led to the identification of similarly regulated genes in human and mouse liver disease. Although major species differences exist, mouse models may recall 30% of the genes significantly altered in human CLD. The relevance of individual genes an be assessed at https://saezlab.shinyapps.io/liverdiseaseatlas/.
The tab Mouse models
contains Rmarkdown scripts to analyze and characterize the transcriptomic profiles of acute and chronic liver disease mouse models. These analyses comprised
The tab Patient cohorts
contains Rmarkdown scripts to analyze and characterize the transcriptomic profiles of patient cohorts suffering from various chronic liver disease etiologies. These analyses comprised:
The tab Meta analysis
contains Rmarkdown scripts to integrate acute and chronic mouse models with patient cohorts.
The tab Figures
contains Rmarkdown scripts to generate the figures used in the manuscript.
We have used the workflowr package to organize the analysis scripts within this project so please familiarize yourself with its concept.
install.packages("renv")
renv::restore()
The GitHub repository contains only the analysis code and same small objects. All raw data is deposited at Zenodo . Download the zipped data
folder unzipp it and replace the existing data
folder at the root level of the R-project.
Run all analyses by running
install.packages("workflowr")
workflowr::wflow_build(republish = TRUE)
All intermediate and final results will be saved in the output
folder.
Holland CH, Ramirez Flores RO, Myllys M, Hassan R, Edlund K, Hofmann U, Marchan R, Cadenas C, Reinders J, Hoehme S, Seddek A, Dooley S, Keitel V, Godoy P, Begher-Tibbe B, Trautwein C, Rupp C, Mueller S, Longerich T, Hengstler JG#, Saez-Rodriguez J#, Ghallab A#. “Transcriptomic cross-species analysis of chronic liver disease reveals a consistent regulation pattern between humans and mice.” In preparation. 2021.
#Shared senior authorship
sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 rstudioapi_0.13 whisker_0.4 knitr_1.30
[5] magrittr_2.0.1 R6_2.5.0 rlang_0.4.9 stringr_1.4.0
[9] tools_4.0.2 xfun_0.19 git2r_0.27.1 htmltools_0.5.0
[13] ellipsis_0.3.1 rprojroot_2.0.2 yaml_2.2.1 digest_0.6.27
[17] tibble_3.0.4 lifecycle_0.2.0 crayon_1.3.4 later_1.1.0.1
[21] vctrs_0.3.6 promises_1.1.1 fs_1.5.0 glue_1.4.2
[25] evaluate_0.14 rmarkdown_2.6 stringi_1.5.3 compiler_4.0.2
[29] pillar_1.4.7 httpuv_1.5.4 renv_0.12.3 pkgconfig_2.0.3