E-spatial

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.

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.

Single-cell data analysis is revolutionizing biological research, but often these dataset sizes can be massive and pose challenges for submission process. Bioalpha-Biocolab addresses this issue by implementing advanced algorithms and leveraging efficient computational resources to overcome these challenges.

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.

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

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.

scPRINT is a large transformer model built for the inference of gene networks (connections between genes explaining the cell's expression profile) from scRNAseq data. It uses novel encoding and decoding of the cell expression profile and new pre-training methodologies to learn a cell model. scPRINT can be used to perform the following analyses: - expression denoising: increase the resolution of your scRNAseq data - cell embedding: generate a low-dimensional representation of your dataset - label prediction: predict the cell type, disease, sequencer, sex, and ethnicity of your cells - gene network inference: generate a gene network from any cell or cell cluster in your scRNAseq dataset

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.

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.

Single-cell RNA data allows cell-cell communications (***CCC***) methods to infer CCC at either the individual cell or cell cluster/cell type level, but physical distances between cells are not preserved Almet, Axel A., et al., (2021). On the other hand, spatial data provides spatial distances between cells, but single-cell or gene resolution is potentially lost. Therefore, integrating two types of data in a proper manner can complement their strengths and limitations, from that improve CCC analysis. In this pipeline, we analyze CCC on Visium data with single-cell data as a reference. The pipeline includes 4 sub-notebooks as following 01-deconvolution: This step involves deconvolution and cell type annotation for Visium data, with cell type information obtained from a relevant single-cell dataset. The deconvolution method is SpatialDWLS which is integrated in Giotto package. 02-giotto: performs spatial based CCC and expression based CCC on Visium data using Giotto method. 03-nichenet: performs spatial based CCC and expression based CCC on Visium data using NicheNet method. 04-visualization: visualizes CCC results obtained from Giotto and NicheNet.

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

Perturbation effects on gene programs are commonly investigated in single-cell experiments. Existing models measure perturbation responses independently across time series, disregarding the temporal consistency of specific gene programs. We introduce CellDrift, a generalized linear model based functional data analysis approach to investigate temporal gene patterns in response to perturbations. CellDrift is a python package for the evaluation of temporal perturbation effects using single-cell RNA-seq data. It includes functions below: 1. Disentangle common and cell type specific perturbation effects across time; 2. Identify patterns of genes that have similar temporal perturbation responses; 3. Prioritize genes with distinct temporal perturbation responses between perturbations or cell types; 4. Infer differential genes of perturbational states in the pseudo-time trajectories.

Support run fastq from CellRanger and upload to BBrowserX

Droplet-based single-cell RNA sequence analyses assume that all acquired RNAs are endogenous to cells. However, there is a certain amount of cell-free mRNAs floating in the input solution (referred to as 'the soup'), created from cells in the input solution being lysed. These background mRNAs are then distributed into the droplets with cells and sequenced alongside them, resulting in background contamination that confounds the biological interpretation of single-cell transcriptomic data. SoupX (Young and Behjati, 2020) is one of the methods proposed for ambient mRNA removal. In this notebook, we will illustrate a workflow example that applies SoupX to correct the ambient RNA in a dataset of 10k PBMC cells. The output of SoupX is a modified counts matrix, which can be used for any downstream analysis tool.

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.