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Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data - stdeconvolve
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BioTuring

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 .
SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
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BioTuring

Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights.
Only CPU
SPARK-X
Doublet Detection: Detect doublets (technical errors) in single-cell RNA-seq count matrices
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BioTuring

Doublets are a characteristic error source in droplet-based single-cell sequencing data where two cells are encapsulated in the same oil emulsion and are tagged with the same cell barcode. Across type doublets manifest as fictitious phenotypes that can be incorrectly interpreted as novel cell types. DoubletDetection present a novel, fast, unsupervised classifier to detect across-type doublets in single-cell RNA-sequencing data that operates on a count matrix and imposes no experimental constraints. This classifier leverages the creation of in silico synthetic doublets to determine which cells in the input count matrix have gene expression that is best explained by the combination of distinct cell types in the matrix. In this notebook, we will illustrate an example workflow for detecting doublets in single-cell RNA-seq count matrices.
Multimodal single-cell chromatin analysis with Signac
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BioTuring

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.
Only CPU
Required PFP
signac

Trends

BPCells: Scaling Single Cell Analysis to Millions of Cells

BioTuring

BPCells is a package for high performance single cell analysis on RNA-seq and ATAC-seq datasets. It can analyze a 1.3M cell dataset with 2GB of RAM in under 10 minutes. This makes analysis of million-cell datasets practical on a laptop. BPCells pr(More)
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BPCells
Seurat - Integrated analysis of multimodal single-cell data

BioTuring

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 nei(More)
Only CPU
Seurat
Evaluating Performance on Single-Cell Datasets using BioTuring Alpha, Scanpy and Seurat

BioTuring

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression at the individual cell level, enabling researchers to uncover heterogeneity and dynamics within complex cellular populations. To analyze and interpret scRNA-seq da(More)
Geneformer: a deep learning model for exploring gene networks

BioTuring

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(More)
scGPT: Towards Building a Foundational Model for Single-Cell Multi-omics Using Generative AI

BioTuring

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 t(More)
Required GPU
scgpt
Seurat
BioTuring Data Converter: Seurat <=> Scanpy for single-cell data transcriptomic and spatial transcriptomics

BioTuring

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(More)
Identifying tumor cells at the single-cell level using machine learning - inferCNV

BioTuring

Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation—the(More)
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inferCNV
MuSiC: Multi-subject Single-cell Deconvolution

BioTuring

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(More)
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MuSiC
BayesPrism: Cell type and gene expression deconvolution for bulk RNA-seq data

BioTuring

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 cell(More)
Multimodal single-cell chromatin analysis with Signac

BioTuring

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-ce(More)
Only CPU
Required PFP
signac
Monocle3 - An analysis toolkit for single-cell RNA-seq

BioTuring

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 a(More)
Bioturing Massive-scale Analysis Solution: Running analysis for massive-scale data from Seurat dataset

BioTuring

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(More)
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inferCNV
PAGA: partition-based graph abstraction for trajectory analysis

BioTuring

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(More)
Harmony: fast, sensitive, and accurate integration of single cell data

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(More)
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harmonpy
WOT (Waddington-OT): A software package for analyzing snapshots of developmental processes

BioTuring

Single cell RNA-seq allows us to profile the diversity of cells along a developmental time-course. However, we cannot directly observe cellular trajectories because the measurement process is destructive. Waddington-OT is designed to infer the tempor(More)
Only CPU
WOT
Notebooks
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BPCells
Only CPU
Seurat
Required GPU
scgpt
Seurat
Only CPU
inferCNV
Only CPU
MuSiC
Required PFP
Only CPU
signac
Only CPU
inferCNV
Only CPU
harmonpy
Only CPU
WOT