https://github.com/saezlab/footprintmethods_on_scrnaseq
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
https://github.com/saezlab/footprintmethods_on_scrnaseq
Last synced: 11 months ago
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Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
- Host: GitHub
- URL: https://github.com/saezlab/footprintmethods_on_scrnaseq
- Owner: saezlab
- License: gpl-3.0
- Created: 2019-04-29T12:26:20.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2020-03-02T18:54:45.000Z (over 6 years ago)
- Last Synced: 2025-04-14T17:11:24.207Z (over 1 year ago)
- Language: R
- Homepage: https://doi.org/10.1186/s13059-020-1949-z
- Size: 12.4 MB
- Stars: 26
- Watchers: 2
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
### Abstract
**Background**
Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.
**Results**
To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.
**Conclusions**
Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.
***
### Availabilty of data
The datasets supporting the conclusions of this publication are available at Zenodo:
[](https://doi.org/10.5281/zenodo.3564179)
From Zenodo you can download these (zipped) folders:
* `data` - Contains raw data for the analyses
* `output` - Contains intermediate and final results
Please deposit the unzipped folders in the root directory of this R-project.
**Exceptions:**
* The raw Human Cell Atlas data ([`"data/hca_data/expression_data/sce.all.technologies.RData"`](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/hca_data_analysis.Rmd#L56)) are not available on Zenodo. Instead the users can work with the normalized data stored in [`"output/hca_data/expression/norm.rds"`](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/hca_data_analysis.Rmd#L205). The raw data are accessible on GEO: [GSE133549](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133549).
* Only a small fraction of the raw and normalized expression data of the simulated single cells are avaiable. All data together exceed the limitation of Zenodo's maximal upload size.
***
### How to cite
> Holland CH, Tanevski J, Perales-Patón J, Gleixner J, Kumar MP, Mereu E, Joughin BA, Stegle O, Lauffenburger DA, Heyn H, Szalai B, Saez-Rodriguez, J. "Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data." _Genome Biology._ 2020. DOI: [10.1186/s13059-020-1949-z](https://doi.org/10.1186/s13059-020-1949-z).
***
### Analyses & Scripts
#### Testing robustness with respect to low gene coverage (Fig. 1)
Analysis script available [here](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/general_robustness.Rmd).
#### In-silico benchmark (Fig. 2)
Analysis script available [here](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/in_silico_benchmark.Rmd).
#### In-vitro benchmark (Fig. 3)
Analysis script available [here](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/in_vitro_benchmark.Rmd).
#### Analysis of HCA-data (Fig. 4)
Analysis script available [here](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/hca_data_analysis.Rmd).
#### Plotting
We provide also scripts for plotting [individual](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/plot_figures.Rmd) figures and [collages](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/figure_arrangement.Rmd).