Li Group: Qian Li

In the GMSimpute R package, two-Step Lasso (TS-Lasso) and compound minimum methods are used to recover the abundance of missing peaks in mass spectrum analysis. TS-Lasso is an imputation method that handles various types of missing peaks simultaneously. This package provides the procedure to generate missing peaks (or data) for simulation study, as well as a tool to estimate and visualize the proportion of missing at random.

In the IncDIFF R package, we developed an approach to detect differential expression features in long non-coding RNA low counts, using generalized linear model with zero-inflated exponential quasi likelihood ratio test. Methods implemented in this package are described in Li (2019).

ISLET is a method to conduct signal deconvolution for general -omics data. It can estimate the individual-specific and cell-type-specific reference panels, when there are multiple samples observed from each subject. It takes the input of the observed mixture data (feature by sample matrix), the cell type mixture proportions (sample by cell type matrix), and the sample-to-subject information. ISLET can solve for the reference panel on the individual-basis and conduct tests to identify cell-type-specific differential expression (csDE) genes.

Our pipeline, MICSQTL, utilizes scRNA-seq reference and bulk transcriptomes to estimate cellular composition in the matched bulk proteomes. The expression of genes and proteins at either bulk level or cell type level can be integrated by the Angle-based Joint and Individual Variation Explained (AJIVE) framework. Meanwhile, MICSQTL can perform cell-type-specific quantitative trait loci (QTL) mapping to proteins or transcripts based on the input of bulk expression data and the estimated cellular composition per molecule type, without the need for single cell sequencing. We use matched transcriptome-proteome from human brain frontal cortex tissue samples to demonstrate the input and output of our tool.

In the mtraderR package, we proposed a Joint model with Matching and Regularization (JMR) to detect operational taxonomic units (OTUs) predictive of hosts’ disease status in matched sets. We also designed and implemented a simulation pipeline to generate disease outcome and temporal high-dimensional metagenomic counts in matched sets.