The ultimate goal of our research is to advance biomedical sciences and clinical practices through the development of rigorus and efficient tools. We aim to tackle the challenges provided by the modern biomedical “big data”, including high-dimensionality, heterogeneity, technical artifacts, reproducibility, etc. Over the last decade, we have developed a number of important statistical methods and widely used software packages for analyzing large-scale biomedical data, in particular high-throughput omics data. Below I provide a brief description of the research in our lab.
Methods for second-generation sequencing data
We have developed methods and tools for a variety of bulk sequencing data, including
- Bulk RNA-seq: differential expression, sample size calculation.
- Chromatin immunoprecipitation sequencing (ChIP-seq): peak calling, differential peak.
- Bisulfite sequencing (BS-seq): differential methylation.
- Methylated RNA immunoprecipitation sequencing (MeRIP-seq): RNA methylation detection, differential RNA methylation.
In particular, we published three important papers for detecting differential methylation in BS-seq data, under various experimental designs. We characterized the sequence counts from BS-seq by a hierarchical beta-binomial model, and designed different methods for parameter estimation and statistical inference. All methods are implemented in a Bioconductor package DSS, which has become one of the most widely used tool in analyzing BS-seq data, with annual downloads of over 20,000.
Single cell RNA sequencing
We have developed a number of methods, including sample size calculation, differential expression, feature selection for improved cell clustering, and new metrics for evaluating cell clustering results. Our differential expression method SC2P uses a mixture of zero-inflated Poisson and lognormal-Poisson distributions to characterize the sparse count data from scRNA-seq, and provides flexible inferences in differential expression. Our new feature selection method FEAST significantly improves the cell clustering results. All methods are implemented as software packages freely available on Github or Bioconductor. See Software page for more details.
Estimating and accounting for sample heterogeneity
The bulk high-throughput experiments are often conducted on complex tissue samples, which are mixtures of different cell types. The mixture brings complication to data analysis, and will lead to biased results if not properly accounted for. We developed a number of methods for properly analyzing the high-throughput omics data from complex tissues, mainly in two directions:
- “Signal deconvolution” methods to estimate the cell type mixing proportions and pure cell type profiles.
- Methods to account for cell type mixture in various analyses, including cell type specific differential expression/methylation and sample clustering.
We also developed method for signal deconvolution and disease prediction from cell free DNA (cfDNA) methylation. The cfDNA is a mixture of DNA segment from different tissues. We perform signal deconvolution to estimate the mixing proportions, which can accurately predict disease status. The cfDNA study represents a novel and exciting direction known as “liquid biopsy”, which has great potential in early disease diagnosis.