Identify alternative methods

[ozgunbabur]

Identify at least 2, ideally 3 alternative methods that can use prior information (known targets of transcription factors) to predict TF activities on single-cell transcriptomic profiles.

These methods need to be free to use so that we can run them on simulated and real datasets.

Here is a good starting point:
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1949-z

[KisanThapa]

Papers:

1. Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data. https://link.springer.com/article/10.1186/s13059-020-1949-z
2. Benchmark and integration of resources for the estimation of human transcription factor activities. https://genome.cshlp.org/content/29/8/1363.full
3. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. https://www.nature.com/articles/ng.3593
4. Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm
   . https://www.nature.com/articles/s41467-018-03843-3
5. SCENIC: single-cell regulatory network inference and clustering. https://www.nature.com/articles/nmeth.4463
6. Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects. https://doi.org/10.1101/630087
7. Prediction of single-cell gene expression for transcription factor analysis https://doi.org/10.1093/gigascience/giaa113
8. Gene regulatory network inference in the era of single-cell multi-omics https://www.nature.com/articles/s41576-023-00618-5
9. A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes https://pubmed.ncbi.nlm.nih.gov/34193535/
10. Predicting transcription factor binding in single cells through deep learning https://www.science.org/doi/abs/10.1126/sciadv.aba9031
11. Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data. https://doi.org/10.1093/nar/gkq149
12. Splatter: simulation of single-cell RNA sequencing data https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1305-0
13. Massive mining of publicly available RNA-seq data from human and mouse https://www.nature.com/articles/s41467-018-03751-6
14. Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES https://doi.org/10.1093/bioinformatics/btac775

Methods:

• A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes https://pubmed.ncbi.nlm.nih.gov/34193535/
• Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data. https://doi.org/10.1093/nar/gkq149
• Predicting transcription factor binding in single cells through deep learning https://www.science.org/doi/abs/10.1126/sciadv.aba9031
• Prediction of single-cell gene expression for transcription factor analysis https://doi.org/10.1093/gigascience/giaa113

Benchmarking:

• Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data. https://link.springer.com/article/10.1186/s13059-020-1949-z
• Benchmark and integration of resources for the estimation of human transcription factor activities. https://genome.cshlp.org/content/29/8/1363.full
• Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm
  . https://www.nature.com/articles/s41467-018-03843-3
• Functional characterization of somatic mutations in cancer using network-based inference of protein activity. https://www.nature.com/articles/ng.3593

[KisanThapa]

1. PROGENy: Schubert M, Klinger B, Klünemann M, Sieber A, Uhlitz F, Sauer S, et al. Perturbation-response genes reveal signaling footprints in cancer gene expression [Internet]. Nature Communications. 2018; Available from: https://doi.org/10.1038/s41467-017-02391-6.
2. DoRothEA: Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res. 2019;29:1363–75 Available from: https://doi.org/10.1101/gr.240663.118.
3. GO Enrichment: Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Michael Cherry J, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000:25–9 Available from: https://doi.org/10.1038/75556.
4. SCENIC: Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14:1083–6 Available from: https://doi.org/10.1038/nmeth.4463.
5. metaVIPER: Ding H, Douglass EF Jr, Sonabend AM, Mela A, Bose S, Gonzalez C, et al. Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm. Nat Commun. 2018;9:1471 Available from: https://doi.org/10.1038/s41467-018-03843-3.
6. AUCell: http://bioconductor.org/packages/release/bioc/html/AUCell.html