Other Omics

CASE is an R package designed for multi-trait fine-mapping analysis, with a particular focus on single-cell eQTL fine-mapping.

Faculty: Hongyu Zhao, PhD

Download: GitHub / CASE package

Platform: R

In this study, we present DeepCDR which integrates multi-omics profiles of cancer cells and explores intrinsic chemical structures of drugs for predicting CDR. Specifically, DeepCDR is a hybrid graph convolutional network consisting of a uniform graph convolutional network and multiple subnetworks. Unlike prior studies modeling hand-crafted features of drugs, DeepCDR automatically learns the latent representation of topological structures among atoms and bonds of drugs. Extensive experiments showed that DeepCDR outperformed state-of-the-art methods in both classification and regression settings under various data settings. We also evaluated the contribution of different types of omics profiles for assessing drug response. Furthermore, we provided an exploratory strategy for identifying potential cancer-associated genes concerning specific cancer types. Our results highlighted the predictive power of DeepCDR and its potential translational value in guiding disease-specific drug design.

Faculty: Qiao Liu, PhD

Download: Liu Lab / DeepCDR package

Platform: Python

Reference: doi.org (DeepCDR)

KaKs_Calculator adopts model selection and model averaging to calculate nonsynonymous (Ka) and synonymous (Ks) substitution rates, attempting to include as many features as needed for accurately capturing evolutionary information in protein-coding sequences. In addition, several existing methods for calculating Ka and Ks are also incorporated into KaKs_Calculator.

Faculty: Jeffrey Townsend, PhD

Download: Google Code / KaKs_Calculator package

Reference: doi.org (KaKs_Calculator)

A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data.

Faculty: Hongyu Zhao, PhD

Download: GitHub / MARBLES package

Platform: R

Reference: academic.oup.com (MARBLES)

Faculty: Leying Guan, PhD

Download: GitHub / msCCA package

Platform: R

The Nebula package implements Network-based latent-Dirichlet subtype analysis (Nebula) algorithm. Practically, this can be used to incorporate biological networks/pathways to inform clustering solutions. Flexible sparsity parameters for multiple input data types allows for control over which data types need sparse vs rich feature selection.

Faculty: Yize Zhao, PhD

Download: GitHub / nebula package

Platform: R

Reference: doi.org (nebula)

A deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.

Faculty: Hongyu Zhao, PhD

Download: GitHub / scNAT package

Platform: Python

Reference: genomebiology.biomedcentral.com (scNAT)

Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive modeling. However, multi-omics integration and predictive modeling are generally performed independently in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful. Spear is a sparse supervised bayesian factor model for multi-omics analysis.

Faculty: Leying Guan, PhD

Download: Bitbucket / SPEAR package

Platform: R

Reference: doi.org (SPEAR)