E-spatial

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. User can run the tool directly through the command line. Currently, we support: - InferCNV - Identifying tumor cells at the single-cell level using machine learning

PyWGCNA is a Python library designed to do weighted correlation network analysis (WGCNA). It can be used for: - Finding clusters (modules) of highly correlated genes - For summarizing such clusters using the module eigengene - For relating modules to one another and to external sample traits (using eigengene network methodology) - For calculating module membership measures. Users can also compare WGCNA networks from different datasets, or to external gene lists, to assess the conservation or functional enrichment of each module.

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.

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.

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.

Reconstructing cell type compositions and their gene expression from bulk RNA sequencing (RNA-seq) datasets is an ongoing challenge in cancer research. BayesPrism (Chu, T., Wang, Z., Pe’er, D. et al., 2022) is a Bayesian method used to predict cellular composition and gene expression in individual cell types from bulk RNA-seq datasets, with scRNA-seq as references. This notebook illustrates an example workflow for bulk RNA-seq deconvolution using BayesPrism. The notebook content is inspired by BayesPrism's vignette and modified to demonstrate how the tool works on BioTuring's platform.

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. **STdeconvolve** provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve .

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.

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.

Expanded CRISPR-compatible CITE-seq (ECCITE-seq) which is built upon pooled CRISPR screens, allows to simultaneously measure transcriptomes, surface protein levels, and single-guide RNA (sgRNA) sequences at single-cell resolution. The technique enables multimodal characterization of each perturbation and effect exploration. However, it also encounters heterogeneity and complexity which can cause substantial noise into downstream analyses. Mixscape (Papalexi, Efthymia, et al., 2021) is a computational framework proposed to substantially improve the signal-to-noise ratio in single-cell perturbation screens by identifying and removing confounding sources of variation. In this notebooks, we demonstrate Mixscape's features using pertpy - a Python package offering a range of tools for perturbation analysis. The original pipeline of Mixscape implemented in R can be found here.

Mapping out the coarse-grained connectivity structures of complex manifolds Biological systems often change over time, as old cells die and new cells are created through differentiation from progenitor cells. This means that at any given time, not all cells will be at the same stage of development. In this sense, a single-cell sample could contain cells at different stages of differentiation. By analyzing the data, we can identify which cells are at which stages and build a model for their biological transitions. By quantifying the connectivity of partitions (groups, clusters) of the single-cell graph, partition-based graph abstraction (PAGA) generates a much simpler abstracted graph (PAGA graph) of partitions, in which edge weights represent confidence in the presence of connections. In this notebook, we will introduce the concept of single-cell Trajectory Analysis using PAGA (Partition-based graph abstraction) in the context of hematopoietic differentiation.

The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

Unique Molecular Identifiers (UMIs) are random oligonucleotide barcodes that are increasingly used in high-throughput sequencing experiments. Through a UMI, identical copies arising from distinct molecules can be distinguished from those arising through PCR-amplification of the same molecule. Existing methods often ignore or poorly address errors in UMI sequences. Here we introduce the UMI-tools, a network-based approach to account for these errors when identifying PCR duplicates. This open source improves quantification on accuracy both under simulated conditions and real iCLIP and single-cell RNA-seq datasets. UMI-tools contains tools for dealing with Unique Molecular Identifiers (UMIs)/Random Molecular Tags (RMTs) and single-cell RNA-Seq cell barcodes.

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.