| The Guidance Paper |
Ballouz S, Verleyen W, Gillis J. Guidance for RNA-seq co-expression network construction and analysis: safety in numbers. Bioinformatics. 2015. doi: 10.1093/bioinformatics/btv118. PubMed PMID: 25717192. https://academic.oup.com/bioinformatics/article/31/13/2123/196230 |
- It’s important to have lots of data - Microarray coexpression and RNA-seq coexpression are similar except that low expressing genes form strong modules in microarray but not RNA-seq networks |
RNA-seq, microarray, coexpression, human, replicability, network analysis |
| AuPairWise |
Ballouz S, Gillis J. AuPairWise: A Method to Estimate RNA-Seq Replicability through Co-expression. PLoS computational biology. 2016;12(4):e1004868. doi: 10.1371/journal.pcbi.1004868. PubMed PMID: 27082953; PubMed Central PMCID: PMC4833304. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004868 |
- Higher coexpression of selected gene-pairs over random gene-pairs can be used for RNA-seq quality control |
Software, coexpression |
| EGAD |
Ballouz S, Weber M, Pavlidis P, Gillis J. EGAD: ultra-fast functional analysis of gene networks. Bioinformatics. 2017 Feb 15; 33(4):612-614. PubMed PMID: 27993773. https://academic.oup.com/bioinformatics/article/33/4/612/2664343 |
- Bioconductor package for neighbor voting and other assorted functions |
Software, network analysis |
| ErmineJ |
Ballouz S, Pavlidis P, Gillis J. Using predictive specificity to determine when gene set analysis is biologically meaningful. Nucleic Acids Research. 2016. doi: 10.1093/nar/gkw957 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389513/ |
- Specificity and robustness are useful heuristics to identify reliable enrichment results. - We can use multifunctionality as a way of targeting specificity and robustness. |
Enrichment analysis, GO |
| The Single Cell Coexpression Paper (The Genome Biology Paper) |
Crow M, Paul A, Ballouz S, Huang ZJ, Gillis J (2016) Exploiting single-cell expression to characterize co-expression replicability. Genome Biology 17, 101. PubMed PMID: 27165153; PubMed Central PMCID: PMC4862082. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0964-6 |
- Single cell RNA-seq coexpression aggregation ~ bulk - Coexpression within cell types ~ across cell types - Expression level can predict coexpression, so should test for this |
Single cell, meta-analysis, coexpression, Brainspan, control experiments, novel data |
| The Effect Size Paper (The Genome Medicine Paper) |
Ballouz S, Gillis J. Strength of functional signature correlates with effect size in autism. Genome Med. 2017 Jul 7; 9(1):64. PubMed PMID: 28687074; PubMed Central PMCID: PMC5501949. https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-017-0455-8 |
- The more strongly a gene is associated with a disease, the more likely it is to show functional convergence. |
Expression, functional enrichment, disease, genetics, autism, Brainspan |
| MetaNeighbor |
Crow M, Paul A, Ballouz S, Huang ZJ, Gillis J. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nature communications. 2018; 9(1):884. PMID: 29491377, PMCID: PMC5830442 https://www.nature.com/articles/s41467-018-03282-0 |
- Cell type transcriptional profiles are replicable across studies - When predicting cell identity, almost any set of genes can be used to improve performance above chance - Highly variable genes are generally useful, even when cell types are rare or only subtly different from the outgroup |
Single cell, meta-analysis, brain, software |
| Aligner |
Ballouz S, Dobin A, Gingeras TR, Gillis J. The fractured landscape of RNA-seq alignment: The default in our STARs. Nucleic Acids Research. https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gky325/4990636 |
- Exact expression is hard to get right, statistical differences are easy - Most parameter choices are fine, but our ways of telling what is fine are overly technical. |
RNA-seq, STAR, software, meta-analysis, collaboration |
| Consensus (null) opinion |
Ballouz S, Dobin A, Gillis J. (2019) Is it time to change the reference genome? Genome Biology. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1774-4 |
- The reference genome is idiosyncratic and shouldn't be used as a baseline. - Incorporating the most frequent/common allele into the reference (i.e., converting it into a 'consensus' genome) is a good-enough fix |
Consensus genome, Reference genome, mapping, variant-calling, collaboration |