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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.

Single-cell RNA sequencing (scRNA-seq) data have allowed us to investigate cellular heterogeneity and the kinetics of a biological process. Some studies need to understand how cells change state, and corresponding genes during the process, but it is challenging to track the cell development in scRNA-seq protocols. Therefore, a variety of statistical and computational methods have been proposed for lineage inference (or pseudotemporal ordering) to reconstruct the states of cells according to the developmental process from the measured snapshot data. Specifically, lineage refers to an ordered transition of cellular states, where individual cells represent points along. pseudotime is a one-dimensional variable representing each cell’s transcriptional progression toward the terminal state. Slingshot which is one of the methods suggested for lineage reconstruction and pseudotime inference from single-cell gene expression data. In this notebook, we will illustrate an example workflow for cell lineage and pseudotime inference using Slingshot. The notebook is inspired by Slingshot's vignette and modified to demonstrate how the tool works on BioTuring's platform.

Geneformer is a foundation transformer model pretrained on a large-scale corpus of ~30 million single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology. Here, we will demonstrate a basic workflow to work with ***Geneformer*** models. These notebooks include the instruction to: 1. Prepare input datasets 2. Finetune Geneformer model to perform specific task 3. Using finetuning models for cell classification and gene classification application

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.