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BioTuring
The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a framework for the analysis of single-cell chromatin data, as an extension of the Seurat R toolkit for single-cell multimodal analysis.
**Signac** enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis, and interactive visualization.
Furthermore, Signac facilitates the analysis of multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance, and mitochondrial genotype. We demonstrate scaling of the Signac framework to datasets containing over 700,000 cells.
BioTuring
Advances in multi-omics have led to an explosion of multimodal datasets to address questions from basic biology to translation. While these data provide novel opportunities for discovery, they also pose management and analysis challenges, thus motivating the development of tailored computational solutions. `muon` is a Python framework for multimodal omics.
It introduces multimodal data containers as `MuData` object. The package also provides state of the art methods for multi-omics data integration. `muon` allows the analysis of both unimodal omics and multimodal omics.
BioTuring
scVI-tools (single-cell variational inference tools) is a package for end-to-end analysis of single-cell omics data primarily developed and maintained by the Yosef Lab at UC Berkeley. scvi-tools has two components
- Interface for easy use of a range of probabilistic models for single-cell omics (e.g., scVI, scANVI, totalVI).
- Tools to build new probabilistic models, which are powered by PyTorch, PyTorch Lightning, and Pyro.
BioTuring
The recent development of single-cell RNA-sequencing (scRNA-seq) technology has enabled us to infer cell-type-specific co-expression networks, enhancing our understanding of cell-type-specific biological functions. However, existing methods proposed for this task still face challenges due to unique characteristics in scRNA-seq data, such as high sequencing depth variations across cells and measurement errors.
CS-CORE (Su, C., Xu, Z., Shan, X. et al., 2023), an R package for cell-type-specific co-expression inference, explicitly models sequencing depth variations and measurement errors in scRNA-seq data.
In this notebook, we will illustrate an example workflow of CS-CORE using a dataset of Peripheral Blood Mononuclear Cells (PBMC) from COVID patients and healthy controls (Wilk et al., 2020). The notebook content is inspired by CS-CORE's vignette and modified to demonstrate how the tool works on BioTuring's platform.
Trends
BioTuring
scGen is a generative model to predict single-cell perturbation response across cell types, studies and species (Nature Methods, 2019). scGen is implemented using the scvi-tools framework.
What you can do with scGen:
Train on a dataset with multiple cell types and conditions and predict the perturbation effect on the cell type which you only have in one condition. This scenario can be extended to multiple species where you want to predict the effect of a specific species using another or all the species.
Train on a dataset where you have two conditions (e.g. control and perturbed) and predict on second dataset with similar genes.
Remove batch effect on labeled data. In this scenario you need to provide cell_type and batch labels to the method. Note that batch_removal does not require all cell types to be present in all datasets (batches). If you have dataset specific cell type it will preserved as before.
We assume there exist two conditions in you dataset (e.g. control and perturbed). You can train the model and with your data and predict the perturbation for the cell type/species of interest.
We recommend to use normalized data for the training. A simple example for normalization can be performed using scanpy