| The Proteomics Paper |
Gillis J, Ballouz S, Pavlidis P. Bias tradeoffs in the creation and analysis of protein-protein interaction networks. Journal of proteomics. 2014;100:44-54. Epub 2014/02/01. doi: 10.1016/j.jprot.2014.01.020. PubMed PMID: 24480284; PubMed Central PMCID: PMC3972268. https://www.sciencedirect.com/science/article/pii/S1874391914000384?via%3Dihub |
- Biases in PPI data due to prey/bait selection |
Protein–protein interaction, Co-expression, Bias, Gene Ontology, Networks, Multifunctionality |
| Wim’s First Paper |
Verleyen W, Ballouz S, Gillis J. Measuring the wisdom of the crowds in network-based gene function inference. Bioinformatics. 2015;31(5):745-52. doi: 10.1093/bioinformatics/btu715. PubMed PMID: 25359890. https://academic.oup.com/bioinformatics/article/31/5/745/317877 |
- Data is more important than methods |
Machine learning |
| The Guidance Paper (Sara’s First Paper, RNA-seq Co-expression 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 |
| The Goodhart Paper |
Verleyen W, Ballouz S, Gillis J. Positive and negative forms of replicability in gene network analysis. Bioinformatics. 2015. doi: 10.1093/bioinformatics/btv734. PubMed PMID: 26668004. PMC Journal - In Process. https://academic.oup.com/bioinformatics/article/32/7/1065/1744280 |
- Replicability can occur for uninteresting reasons (e.g. data re-use) |
Machine learning, replicability, network analysis, generalization |
| 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 Shoichet Paper (The Ligand Paper) |
O'Meara MJ, Ballouz S, Shoichet BK, Gillis J. Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction. PLoS One. 2016; 11(7):e0160098. PMID: 27467773. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0160098 |
- Ligand similarity contains different information than other networks. |
Collaboration, coexpression, gene function |
| 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 |
| Anirban’s Paper |
Paul A, Crow M, Raudales R, He M, Gillis J, Huang ZJ. Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity. Cell. 2017; 171(3):522-539.e20. NIHMSID: NIHMS927502, PMID: 28942923, PMCID: PMC5772785 http://www.cell.com/cell/abstract/S0092-8674(17)30990-X |
- Gene sets related to synaptic function show characteristic expression patterns within interneuron subtypes |
Single cell, collaboration, brain, novel data |
| 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 |
| Maggie's Single-cell Coexpression Opinion |
Crow M, Gillis J. Co-expression in single cell analysis: Saving grace or original sin? Trends in Genetics. 2018; 34(11):823-831. PMID: 30146183, PMCID: PMC6195469 https://doi.org/10.1016/j.tig.2018.07.007 |
- Single-cell RNA-seq only works because of coexpression. - At some point this will fail. |
Single cell, coexpression, marker genes, causality, opinion |
| The Current Opinion Piece |
Crow M, Gillis J. Single cell RNA-sequencing: Replicability of cell types. Current Opinion in Neurobiology. 2019; 56, 69-77. https://doi.org/10.1016/j.conb.2018.12.002 |
- What is a cell type? Transcription alone is not sufficient to establish whether a cluster has a unique function, but replicability of profiles is a good first step. |
Single cell, replicability, causality |
| The DE Prior paper (the PNAS paper) |
Crow M, Lim N, Ballouz S, Pavlidis P, Gillis J. (2019) Predictability of human differential gene expression. PNAS. 2019. https://doi.org/10.1073/pnas.1802973116 |
- Some genes are more likely to be DE than others. - Knowing this can help you interpret the plausibility and specificity of your DE hit list. |
Expression, meta-analysis, collaboration, Gemma, functional enrichment |
| 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 |