E-spatial

SCANPY integrates the analysis possibilities of established R-based frameworks and provides them in a scalable and modular form. Specifically, SCANPY provides preprocessing comparable to SEURAT and CELL RANGER, visualization through TSNE, graph-drawing and diffusion maps, clustering similar to PHENOGRAPH, identification of marker genes for clusters via differential expression tests and pseudotemporal ordering via diffusion pseudotime, which compares favorably with MONOCLE 2, and WISHBONE.

This notebook illustrates how to convert data from a Seurat object into a Scanpy annotation data and a Scanpy annotation data into a Seurat object using the BioStudio data transformation library (currently under development). It facilitates continued research using libraries that interact with Scanpy in Python and Seurat in R. seurat.to.adata function can retain information about reductions (such as PCA, t-SNE, UMAP and Seurat Clusters) and spatial information.

This notebook provides an overview of the Bioconductor package edgeR for differential expression analyses of read counts arising from RNA-Seq, SAGE or similar technologies. The package can be applied to any technology that produces read counts for genomic features. edgeR implements statistical methods based on generalized linear models (glms), suitable for multifactor experiments of any complexity. The glm functions can test for differential expression using either likelihood ratio tests or quasi-likelihood F-tests. A particular feature of edgeR functionality, both classic and glm, are empirical Bayes methods that permit the estimation of gene-specific biological variation, even for experiments with minimal levels of biological replication. edgeR can be applied to differential expression at the gene, exon, transcript or tag level. In fact, read counts can be summarized by any genomic feature. edgeR analyses at the exon level are easily extended to detect differential splicing or isoform-specific differential expression.

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop **CellChat**, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying **CellChat** to mouse and human skin datasets shows its ability to extract complex signaling patterns.

Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. **Tangram** can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.

This tool provides a user-friendly and automated way to analyze large-scale single-cell RNA-seq datasets stored in RDS (Seurat) format. It allows users to run various analysis tools on their data in one command, streamlining the analysis workflow and saving time. Note that this notebook is only for the demonstration of the tool. Users can run the tool directly through the command line. Currently, we support: - CellChat - Inference and analysis of cell-cell communication using CellChat

Generative pre-trained models have demonstrated exceptional success in various fields, including natural language processing and computer vision. In line with this progress, scGPT has been developed as a foundational model tailored specifically for the field of single-cell biology. It employs the generative pre-training transformer framework on an extensive dataset comprising more than 33 million cells. scGPT effectively extracts valuable biological insights related to genes and cells and can be fine-tuned to excel in numerous downstream applications.

Classification of tumor and normal cells in the tumor microenvironment from scRNA-seq data is an ongoing challenge in human cancer study. Copy number karyotyping of aneuploid tumors (***copyKAT***) (Gao, Ruli, et al., 2021) is a method proposed for identifying copy number variations in single-cell transcriptomics data. It is used to predict aneuploid tumor cells and delineate the clonal substructure of different subpopulations that coexist within the tumor mass. In this notebook, we will illustrate a basic workflow of CopyKAT based on the tutorial provided on CopyKAT's repository. We will use a dataset of triple negative cancer tumors sequenced by 10X Chromium 3'-scRNAseq (GSM4476486) as an example. The dataset contains 20,990 features across 1,097 cells. We have modified the notebook 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

Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, Monorail-pipeline tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. This is for helping potential users of the Monorail RNA-seq processing pipeline (alignment/quantification) get started running their own data through it.

WGCNA: an R package for Weighted Gene Correlation Network Analysis Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for: - Finding clusters (modules) of highly correlated genes - Summarizing such clusters using the module eigengene or an intramodular hub gene - Relating modules to one another and to external sample traits (using eigengene network methodology) - For calculating module membership measures All of these are important for identifying potential candidate genes associated with measured traits as well as identifying genes that are consistently co-expressed and could be contributing to similar molecular pathways. Using WGCNA is also extremely useful statistically as it accounts for inter-individual variation in gene expression and alleviates issues associated with multiple testing.

Dynamic expression data, nowadays obtained using high-throughput RNA sequencing (RNA-seq), are essential to monitor transient gene expression changes and to study the dynamics of their transcriptional activity in the cell or response to stimuli. FunPat is an R package designed to provide: - a useful tool to analyze time series genomic data; - a computational pipeline which integrates gene selection, clustering and functional annotations into a single framework to identify the main temporal patterns associated to functional groups of differentially expressed (DE) genes; - an easy way to exploit different types of annotations from currently available databases (e.g. Gene Ontology) to extract the most meaningful information characterizing the main expression dynamics; - a user-friendly organization and visualization of the outcome, automatically linking the DE genes and their temporal patterns to the functional information for an easy biological interpretation of the results.

Build single-cell trajectories with the software that introduced **pseudotime**. Find out about cell fate decisions and the genes regulated as they're made. Group and classify your cells based on gene expression. Identify new cell types and states and the genes that distinguish them. Find genes that vary between cell types and states, over trajectories, or in response to perturbations using statistically robust, flexible differential analysis. In development, disease, and throughout life, cells transition from one state to another. Monocle introduced the concept of **pseudotime**, which is a measure of how far a cell has moved through biological progress. Many researchers are using single-cell RNA-Seq to discover new cell types. Monocle 3 can help you purify them or characterize them further by identifying key marker genes that you can use in follow-up experiments such as immunofluorescence or flow sorting. **Single-cell trajectory analysis** shows how cells choose between one of several possible end states. The new reconstruction algorithms introduced in Monocle 3 can robustly reveal branching trajectories, along with the genes that cells use to navigate these decisions.

In this notebook, we present COMMOT (COMMunication analysis by Optimal Transport) to infer cell-cell communication (CCC) in spatial transcriptomic, a package that infers CCC by simultaneously considering numerous ligand–receptor pairs for either spatial transcriptomic data or spatially annotated scRNA-seq data equipped with spatial distances between cells estimated from paired spatial imaging data. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models.

Knowledge of cell type composition in disease relevant tissues is an important step towards the identification of cellular targets of disease. MuSiC is a method that 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. By appropriate weighting of genes showing cross-subject and cross-cell consistency, MuSiC enables the transfer of cell type-specific gene expression information from one dataset to another. MuSiC enables the characterization of cellular heterogeneity of complex tissues for understanding of disease mechanisms. As bulk tissue data are more easily accessible than single-cell RNA-seq, MuSiC allows the utilization of the vast amounts of disease relevant bulk tissue RNA-seq data for elucidating cell type contributions in disease. This notebook provides a walk through tutorial on how to use MuSiC to estimate cell type proportions from bulk sequencing data based on multi-subject single cell data by reproducing the analysis in MuSiC paper, now is published on Nature Communications.