E-spatial

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E-spatial

Single-cell spatial explorer

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scVI-tools: single-cell variational inference tools
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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.
Required GPU
scVI
Harmony: fast, sensitive, and accurate integration of single cell data
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BioTuring

Single-cell RNA-seq datasets in diverse biological and clinical conditions provide great opportunities for the full transcriptional characterization of cell types. However, the integration of these datasets is challeging as they remain biological and techinical differences. **Harmony** is an algorithm allowing fast, sensitive and accurate single-cell data integration.
Only CPU
harmonpy
Cell2location: Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomic
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BioTuring

Cell2location is a principled Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single cell and spatial transcriptomics with higher sensitivity and resolution than existing tools. This is achieved by estimating which combination of cell types in which cell abundance could have given the mRNA counts in the spatial data, while modelling technical effects (platform/technology effect, contaminating RNA, unexplained variance). This tutorial shows how to use cell2location method for spatially resolving fine-grained cell types by integrating 10X Visium data with scRNA-seq reference of cell types. Cell2location is a principled Bayesian model that estimates which combination of cell types in which cell abundance could have given the mRNA counts in the spatial data, while modelling technical effects (platform/technology effect, contaminating RNA, unexplained variance).
Required GPU
Cell2Location
CopyKAT: Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes
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BioTuring

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.

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FunPat: Function-based Pattern analysis on RNA-seq time series data

BioTuring

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.
Only CPU
FunPat