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CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq
Single-cell RNA sequencing (scRNA-seq) has the potential to provide powerful, high-resolution signatures to inform disease prognosis and precision medicine. -
Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity
The dataset used for single-cell RNA sequencing data where cells are modeled as signals on gene interaction graphs or genes are modeled as signals on cell-similarity graphs. -
Single-cell RNA sequencing and mass cytometry data
The dataset used in the paper is a combination of genomic (single-cell RNA-seq) and proteomic (mass cytometry) data from single cells. -
CBMC dataset
The dataset contains single-cell RNA sequencing data from cord blood mononuclear cells. -
Zeisel dataset
The dataset contains single-cell RNA sequencing data from mouse cortex and hippocampus. -
Mapping Information-Rich Genotype-Phenotype Landscapes with Genome-Scale Pert...
Replogle dataset is a collection of single-cell RNA sequencing data from cancer-related genes. -
SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks
SERGIO is a technique for simulating single-cell RNA sequencing dynamics using stochastic differential equations.
