SpaGCN: SpaGCN is a graph convolutional network to integrate gene expression and histology to identify spatial domains and spatially variable genes. To jointly model all spots in a tissue slide, SpaGCN integrates information from gene expression, spatial locations and histological pixel intensities across spots into an undirected weighted graph. Each vertex in the graph contains gene expression information of a spot and the edge weight between two vertices quantifies their expression similarity that is driven by spatial dependency of their coordinates and the corresponding histology. To aggregate gene expression of each spot from its neighboring spots, SpaGCN utilizes a convolutional layer based on edge weights specified by the graph. The aggregated gene expression is then fed into a deep embedding clustering algorithm to cluster the spots into different spatial domains. After spatial domains are identified, genes and meta genes that are enriched in each spatial domain can be detected by differential expression analysis between domains. The SpaGCN program can be obtained from

CarDEC: CarDEC is a joint deep learning computational tool that is useful for analyses of single-cell RNA-seq data. CarDEC can be used to: 1) Correct for batch effects in the full gene expression space, allowing the investigator to remove batch effects from downstream analyses like differential expression analysis and co-expression analysis. Batch correction is also possible in a low-dimensional embedding space. 2) Denoise gene expression. 3) Cluster cells. The CarDEC program can be obtained from

DESC: DESC is an unsupervised deep learning algorithm for scRNA-seq clustering analysis. The algorithm constructs a non-linear mapping function from the original scRNA-seq data space to a low-dimensional feature space by iteratively learning cluster-specific gene expression representation and cluster assignment based on a deep neural network. This iterative procedure moves each cell to its nearest cluster, balances biological and technical differences between clusters. DESC removes complex batch effects while maintaining true biological variations. It also retains both discrete and pseudotemporal structure of the cells. The DESC program can be obtained from

ItClust: ItClust is an iterative transfer learning algorithm for scRNA-seq clustering and cell type classification. It starts from building a training neural network to extract gene-expression signatures from a well-labeled source dataset. This step enables initializing the target network with parameters estimated from the training network. The target network then leverages information in the target dataset to iteratively fine-tune parameters in an unsupervised manner, so that the target-data-specific gene-expression signatures are captured. Once fine-tuning is finished, the target network then returns clustered cells in the target data. ItClust has shown to be a powerful tool for scRNA-seq clustering and cell type classification analysis. It is robust to strong batch effect between source and target data, and is able to separate unseen cell types in the target. Furthermore, it provides confidence scores that facilitates cell type assignment. The ItClust program can be obtained from

BSCET: BSCET characterizes cell-type-specific allelic expression imbalance (AEI) in bulk RNA-seq data by integrating cell-type composition information inferred from scRNA-seq samples. As a two-step regression-based procedure, BSCET first performs cell-type deconvolution analysis to infer cell type composition in bulk RNA-seq data using scRNA-seq data as the reference. Second, given estimated cell type proportions in bulk RNA-seq samples, BSCET tests cell-type-specific AEI using allele-specific bulk RNA-seq read counts. The BSCET program can be obtained from

SCATS: SCATS is a statistical method designed to detect differential alternative splicing events from scRNA-seq data with or without unique molecular identifiers (UMIs). By modeling technical noise and grouping exons that originate from the same isoform(s), SCATS achieves high sensitivity to detect differential alternative splicing events compared to Census, DEXSeq and MISO, and is able to detect subtle gene expression changes. The SCATS program can be obtained from

ASEP: Allele-specific expression (ASE) quantifies the relative expression of two alleles in a diploid individual, and such expression imbalance potentially contributes to phenotypic variation and disease pathophysiology among individuals. Existing ASE detection methods analyze one individual at a time, therefore not only wasting shared information across individuals, but also posing a challenge for results interpretation across individuals. To overcome this limitation, we developed ASEP, a method that is able to detect gene-level ASE under one condition, as well as, ASE difference between two conditions (e.g., pre- vs post-treatment) in a population. ASEP is an open-source R package available at

MuSiC: MUlti-sample SIngle Cell deconvolution (MuSiC) utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms. The script to execute our deconvolution method can be obtained from

SCALE: Single-cell RNA sequencing allows the comparison of expression distribution between the two alleles of a diploid organism and the characterization of allele-specific bursting. SCALE was developed to analyze genome-wide allele-specific bursting, with adjustment of technical variability. SCALE detects genes exhibiting allelic differences in bursting parameters and genes whose alleles burst non-independently. SCALE is an open-source R package available at

TASC: Toolkit for Analysis of Single Cell RNA-seq is an empirical Bayes approach to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins. TASC is implemented in an open-source program with multithreading acceleration by openMP. It can be obtained from

PennSeq: PennSeq is a statistical method that aims to estimate isoform-specific gene expression from RNA-seq data. It allows each isoform to have its own non-uniform read distribution. Instead of making parametric assumptions, PennSeq gives adequate weight to the underlying data by the use of a non-parametric approach. This empirical approach thus maximally reflects the true underlying non-uniform read distribution, and thus yields more accurate isoform expression estimate than other methods particularly for isoforms demonstrating severe non-uniformity. PennSeq is freely available for download at

PennDiff:PennDiff is a statistical method that makes use of information on gene structures and pre-estimated isoform relative abundances to detect differential alternative splicing and transcription from RNA-seq data. By grouping exons and collapsing isoforms sharing the same alternative exons, PennDiff is able to detect differential alternative splicing and transcription at both exon and gene levels, thus offering more flexibility than existing methods. The PennDiff program can be obtained from

MetaDiff: MetaDiff is a Java/R-based software package that performs differential expression analysis on RNA-Seq based data. By utilizing a meta-regression framework, it is able to take advantage of the information regarding the variance of the estimates to make the inference more accurate. Meta-regression also enables incorporation of covariates other than experimental group, which makes it extremely simple to adjust for confounding parameters in an experiment. The MetaDiff program can be obtained from