Recent advances in genomic technology have led to quantitatively measuring the transcript abundance in a single cell, creating an unprecedented opportunity to investigate important biological questions that can only be answered at the single-cell level such as early cell development, cell identity and changes in cell state. This technology has already led to new biological discoveries by associating changes in the transcript profiles of individual cells with phenotypes including immune response, treatment and disease. However, this technology also presents new statistical and computational challenges that need to be addressed to accurately interpret this data. While some methods developed for measuring transcript levels in bulk populations of cells can be used for single-cell data, such as aligning raw sequencing reads to the genome, other steps in the processing, such as normalization and quality control, require new methods to account for the additional sources of variability visible only at the single-cell level. Failure to account for the additional cell-to-cell variability leads to systematic errors and biased results in downstream analyses including differential expression detection, classification of cell types and quantification of transcriptional heterogeneity. The overall goals of the proposed research are to develop novel statistical methods that will 1) remove these systematic errors induced from this high-resolution technology by accounting for variability visible only at the single-cell level and 2) quantify biological variability such as transcriptional heterogeneity within and between populations of cells. My long-term goal is to obtain the skills necessary for me to become a highly capable, independent scientist poised to bring significant statistical and methodological advances to the rapidly evolving field of genomics and transcriptomics at the single-cell level. Specifically, this award will provide the training, mentoring and career development to accomplish my research goals and transition to a tenure-track faculty at a research university with independent funding. At the completion of this award, I will become part of a new generation of researchers, proficient in statistics, computational biology and functional genomics, enabling me to work closely with biomedical researchers profiling the transcriptomes of individual cells.
The proposed statistical methodology will result in an improved quantification and understanding of transcriptional heterogeneity in individual cells. Detailed knowledge of transcriptional heterogeneity from individual cells can lead to new biological discoveries including novel cell types and the diagnosis, prognosis and therapeutic response in disease.
| Kumar, M Senthil; Slud, Eric V; Okrah, Kwame et al. (2018) Analysis and correction of compositional bias in sparse sequencing count data. BMC Genomics 19:799 |
