Sidney Brenner famously declared that “Progress in science depends on new techniques, new discoveries and new ideas, probably in that order.” The proliferation of single-cell genomic technologies in the last decade provides another case study with which to test Brenner’s thesis. As we barrel headlong into the single-cell genomics era, there are many exciting opportunities for the mathematically inclined to contribute to progress in (biomedical) science.
Using single-cell genomic techniques to make new discoveries demands new ideas from computational scientists on how to make best use of single-cell data. Or at least new ideas about how to make use of old ideas in the appropriate ways on new data. The scale and complexity of single-cell data, increasingly featuring “multi-omic” information from individual cells, raises many challenges for the data analyst. Statistics, machine learning and algorithmic skills can all be brought to bear in single-cell data science to answer scientific questions from the data.
In this talk, I will survey a set of “grand challenges” in single-cell data science that I hope will pique your interest and perhaps provide some points of entry to interesting scientific problems waiting to be tackled.
Head of Bioinformatics and Cellular Genomics and Holyoake Research Fellow, St Vincent’s Institute of Medical Research and Melbourne Integrative Genomics, Faculty of Science, the University of Melbourne
Davis started in Bioinformatics as a UROP student (and Honours student, and RA) with Gordon Smyth in the Bioinformatics Division at the Walter and Eliza Hall Institute in Melbourne, Australia. He worked on differential expression methods for RNA-seq data, most notably the edgeR package. He completed a DPhil in Statistics at the University of Oxford under the supervision of Prof Peter Donnelly, before undertaking a postdoc in Dr Oliver Stegle’s group at the European Bioinformatics Institute in Cambridge, UK. At EBI, Davis worked on single-cell methods development and on projects linking DNA variation to single-cell gene expression. He returned to Melbourne in late 2018 to start the Bioinformatics and Cellular Genomics group at St Vincent’s Institute of Medical Research, joint with the Melbourne Integrative Genomics unit at the University of Melbourne. His research team is broadly interested in how computational approaches can drive biological discovery. In particular, they are interested in developing statistical and machine learning methods and software tools for the analysis of clinical imaging and high-throughput sequencing data, with a focus on single-cell genomic data.