I am a postdoc with applied mathematics and bioinformatics background. I recently proceed to work on dynamic network inference from single-cell RNA-seq (scRNA-seq) data. We already got paired-ended scRNA-seq data generated from Illumina HiSeq but haven't started analysis yet. I have machine learning skills but have no idea how to analyze scRNA-seq. I've explored network inference methods from papers and started to take online courses on molecular biology.
Could anyone recommend up-to-date gene regulatory network inference methods or scRNA-seq analysis methods?
Here are the sources that I have found so far:
Bayesian Approach to Single-Cell RNA-Seq Differential Expression Analysis (June 12, 2014) http://nextgenseek.com/2014/06/bayesian-approach-to-single-cell-rna-seq-differential-expression-analysis/
MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data. (2017) https://www.biorxiv.org/content/early/2017/02/25/111591
SCENIC: single-cell regulatory network inference and clustering. (2017) https://www.nature.com/articles/nmeth.4463
- How to analyze RNA-Seq data? Find differentially expressed genes in your research. (Oct 6, 2016) https://www.nature.com/articles/nmeth.4463