Software

Bioinformatics Software Packages

SpaHDmap

SpaHDmap is based on a multi-modal neural network that takes advantage of the high-dimensionality of transcriptomics data and the high-definition of image data to achieve interpretable high-definition dimension reduction.

Key Features:

• Interpretable high-resolution dimension reduction
• Integration of multiple spatial transcriptomics data

scINSIGHT

scINSIGHT uses a novel matrix factorization model to jointly analyze multiple single-cell gene expression samples from biologically heterogeneous sources, such as different disease phases, treatment groups, or developmental stages.

Key Features:

• Integration of multiple datasets with batch effect correction
• Detection of condition-specific gene expression patterns
• Automated parameter selection

scAce

scAce is an adaptive embedding and clustering method for scRNA-seq data. It consists of three major steps: a pre-training step based on a variational autoencoder, a cluster initialization step to obtain initial cluster labels, and an adaptive cluster merging step to iteratively update cluster labels and cell embeddings.

Key Features:

• Adaptive embedding and clustering
• Scalable for large scale scRNA-seq data

scBiG

scBig is a novel computational framework for learning low-dimensional embeddings of scRNA-seq data, which uses a graph autoencoder to extract high-order representations of cells and genes from the cell-gene bipartite graph.

Key Features:

• Gene-cell relationship modeling
• Preserves biological structure