Software

Genetic Risk

TADA: Testing for genetic association to identify risk genes using de novo and inherited genetic variants. The newest version is described here, with code on Github.

DAWN: A framework to identify risk genes and subnetworks using gene expression and genetics. Revised code to be posted soon. For current code, send email request.

UNICORN: A data harmonization pipeline to leverage external controls and boost power in GWAS. Code.

Methods for modeling bulk and single-cell RNA-seq data

sLED: Testing differences between high-dimensional covariance matrices. Code.

MIND: Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression. Code.

bMIND: Bayesian estimation of cell type-specific gene expression with prior derived from single-cell data. Code.

MarkerPen: Identification of cell-type-specific marker genes from co-expression patterns in tissue samples. Code.

PisCES: Global spectral clustering in dynamic networks. Code.

URSM: A Unified RNA Sequencing Model (URSM) for joint analysis of single cell and bulk RNA-seq data. Code.

Methods for modeling single-cell data

SOUP: Semi-soft clustering for single-cells in developmental trajectoris. Code.

locCSN: Constructing local cell-specific networks from single-cell data. Code.

aLDG: Moving from local to global gene co-expression estimation using single-cell RNA-seq with the aLDG dependence measure and others. Code.

SCEPTRE: Analysis of single-cell CRISPR screen perturbation experiments. Code.

GLMeiv: Exponential family measurement error models for single-cell CRISPR screens. Code.

scVAEIT: Probabilistic modeling for single-cell multimodal mosaic integration and imputation. Code.