Software
• EBarrays: Identification of genes differentially expressed across two or more conditions in a microarray experiment
• EB-HMM: Identification of genes differentially expressed across two or more conditions over time in a microarray experiment
• GSCA: Identification of differentially co-expressed gene sets
• EBcoexpress: Identification of differentially co-expressed gene pairs
• EBSeq: Identification of genes and/or isoforms differentially expressed in an RNA-seq experiment
• EBSeq-HMM: Identification of genes and/or isoforms differentially expressed over time in an RNA-seq experiment
• Oscope: Identification and characterization of oscillatory genes in a snapshot single-cell RNA-seq experiment
• scDD: Identification of differentially distributed genes in single-cell RNA-seq experiments
• SCnorm: Normalization of data from Fluidigm single-cell RNA-seq experiments EBarrays: Identification of genes differentially expressed across two or more conditions in a microarray experiment
• EB-HMM: Identification of genes differentially expressed across two or more conditions over time in a microarray experiment
• GSCA: Identification of differentially co-expressed gene sets
• EBcoexpress: Identification of differentially co-expressed gene pairs
• EBSeq: Identification of genes and/or isoforms differentially expressed in an RNA-seq experiment
• EBSeq-HMM: Identification of genes and/or isoforms differentially expressed over time in an RNA-seq experiment
• Oscope: Identification and characterization of oscillatory genes in a snapshot single-cell RNA-seq experiment
• scDD: Identification of differentially distributed genes in single-cell RNA-seq experiments
• SCnorm: Normalization of data from Fluidigm single-cell RNA-seq experiments • CHARTS: A web application that enables researchers to compare and characterize tumor subpopulations between tumors across datasets and cancer types
• scDDboost: Identify changes in the distribution of single-cell expression data between two experimental conditions
• Dino: Normalization by Distributional Resampling of High Throughput Single-Cell RNA-Sequencing Data
• Scaffold: Simulate scRNA-seq data by statistically modeling each step of the experimental data generation process
• Spotclean: Adjusts for spot swapping in spatial transcriptomics data
• SpatialCorr: Identifies differentially correlated gene sets in spatial transcriptomics data
• SpatialView: An interactive web application for visualization of multiple samples in spatial transcriptomics experiments
• CASSIA: Robust, automated, interpretable cell annotation in single-cell RNA-sequencing data