CeLEry: CeLEry is a machine learning method that aims to recover cell locations for single-cell RNA-seq data by leveraging information learned from spatial transcriptomics. The CeLEry program can be obtained from https://github.com/QihuangZhang/CeLEry
TESLA: TESLA is a a machine learning framework for multi-level tissue annotation on the histology image with pixel-level resolution in Spatial Transcriptomics. The TESLA program can be obtained from https://github.com/jianhuupenn/TESLA
SpaDecon: SpaDecon is a semi-supervised learning approach that incorporates gene expression, spatial location, and histology information for cell-type deconvolution. The SpaDecon program can be obtained from https://github.com/kpcoleman/SpaDecon
SciPenn: SciPenn is a multi-use deep learning algorithm for scRNA-seq and CITE-seq data integration, and protein expression prediction and imputation. The SciPenn program can be obtained from https://github.com/jlakkis/sciPENN
InteRD: InteRD is a cell-type deconvolution method that can integrate multiple scRNA-seq references and prior biological knowledge. The InteRD program can be obtained from https://cran.r-project.org/web/packages/InteRD/index.html
SpaGCN: SpaGCN is a graph convolutional network to integrate gene expression and histology to identify spatial domains and spatially variable genes. The SpaGCN program can be obtained from https://github.com/jianhuupenn
CarDEC: CarDEC is a joint deep learning computational tool that can be used to: 1) correct for batch effects in the full gene expression space, 2) denoise gene expression, and 3) cluster cells. The CarDEC program can be obtained from https://github.com/jlakkis/CarDEC
DESC: DESC is an unsupervised deep learning algorithm that can remove complex batch effects in the gene expression embedding space, and cluster cells. The DESC program can be obtained from https://eleozzr.github.io/desc
ItClust: ItClust is an iterative transfer learning algorithm for scRNA-seq clustering and cell type classification. The ItClust program can be obtained from https://github.com/jianhuupenn/ItClust
BSCET: BSCET characterizes cell-type-specific allelic expression imbalance in bulk RNA-seq data by integrating cell-type composition information inferred from scRNA-seq samples. The BSCET program can be obtained from https://github.com/Jiaxin-Fan/BSCET.github.io
SCATS: SCATS is a statistical method designed to detect differential alternative splicing events from scRNA-seq data with or without unique molecular identifiers (UMIs). The SCATS program can be obtained from https://github.com/huyustats/SCATS
ASEP: ASEP, a method that is able to detect gene-level allele-specific expression (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 https://github.com/Jiaxin-Fan/ASEP
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. The script to execute our deconvolution method can be obtained from https://github.com/xuranw/MuSiC
SCALE: SCALE detects genes exhibiting allelic differences in bursting parameters and genes whose alleles burst non-independently in scRNA-seq data. SCALE is an open-source R package available at https://github.com/yuchaojiang/SCALE
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 https://github.com/scrna-seq/TASC
PennSeq: PennSeq is a statistical method that estimates isoform-specific gene expression from RNA-seq data. PennSeq is freely available for download at http://sourceforge.net/projects/pennseq.
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. The PennDiff program can be obtained from https://github.com/tigerhu15/PennDiff
MetaDiff: MetaDiff is a Java/R-based software package that performs differential expression analysis on RNA-Seq based data. The MetaDiff program can be obtained from https://github.com/jiach/MetaDiff