DiNiro can uncover novel and relevant mechanistic models that not only predict but also explain differential cellular gene expression programs. These mechanisms can be presented as small, easily interpretable transcriptional regulatory network modules. Start exploring the possibilities of scRNA-seq technology with DiNiro today and unlock a new level of understanding in gene function and disease mechanisms. 

SCANet is a Python package that incorporates the inference of gene co-expression networks from single-cell gene expression data. It offers a complete analysis of the identified modules through trait and cell type associations, hub genes detection, deciphering of co-regulatory signals in co-expression, and drug-gene interactions identification. This will likely accelerate network analysis pipelines and advance systems biology research.

Scellnetor is a novel clustering tool for scRNA-seq data that takes Scanpy generated AnnData objects in H5AD file-format as input. With Scellnetor you can compare two sets of cells that you manually select on one of your Scanpy-generated plots. The output will be connected components of genes where the genes are either differently or similarly expressed in the two sets. You can also do a clustering of a single set, where the genes in the connected components are similarly expressed. For every cluster, you get a plot showing mean gene expression and the genes' 95 % confidence intervals and a table with statistically significant GO-terms.