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Spatially informed cell-type deconvolution for spatial transcriptomics - CARD
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BioTuring

Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types. Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations. Modeling spatial correlation allows us to borrow the cell-type composition information across locations, improving accuracy of deconvolution even with a mismatched scRNA-seq reference. **CARD** can also impute cell-type compositions and gene expression levels at unmeasured tissue locations to enable the construction of a refined spatial tissue map with a resolution arbitrarily higher than that measured in the original study and can perform deconvolution without an scRNA-seq reference. Applications to four datasets, including a pancreatic cancer dataset, identified multiple cell types and molecular markers with distinct spatial localization that define the progression, heterogeneity and compartmentalization of pancreatic cancer.
Only CPU
card
Inference and analysis of cell-cell communication using CellChat
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BioTuring

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.
Required GPU
CellChat
SCEVAN: Single CEll Variational ANeuploidy analysis
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BioTuring

In the realm of cancer research, grasping the intricacies of intratumor heterogeneity and its interplay with the immune system is paramount for deciphering treatment resistance and tumor progression. While single-cell RNA sequencing unveils diverse transcriptional programs, the challenge persists in automatically discerning malignant cells from non-malignant ones within complex datasets featuring varying coverage depths. Thus, there arises a compelling need for an automated solution to this classification conundrum. SCEVAN (De Falco et al., 2023), a variational algorithm, is designed to autonomously identify the clonal copy number substructure of tumors using single-cell data. It automatically separates malignant cells from non-malignant ones, and subsequently, groups of malignant cells are examined through an optimization-driven joint segmentation process.
Required GPU
scevan
infercnvpy: Scanpy plugin to infer copy number variation from single-cell transcriptomics data
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BioTuring

InferCNV is used to explore tumor single cell RNA-Seq data to identify evidence for somatic large-scale chromosomal copy number alterations, such as gains or deletions of entire chromosomes or large segments of chromosomes. This is done by exploring expression intensity of genes across positions of tumor genome in comparison to a set of reference 'normal' cells. A heatmap is generated illustrating the relative expression intensities across each chromosome, and it often becomes readily apparent as to which regions of the tumor genome are over-abundant or less-abundant as compared to that of normal cells. **Infercnvpy** is a scalable python library to infer copy number variation (CNV) events from single cell transcriptomics data. It is heavliy inspired by InferCNV, but plays nicely with scanpy and is much more scalable.

Trends

CS-CORE: Cell-type-specific co-expression inference from single cell RNA-sequencing data

BioTuring

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.
Only CPU
CS-CORE
AlphaSC - BioTuring GPU-accelerated single-cell data analysis pipeline

BioTuring

Alpha SC, the most efficient GPU-accelerated single-cell data analysis pipeline from BioTuring Alpha, is an innovative initiative by BioTuring designed to address the challenges of analyzing large-scale biological data. Alpha SC is expected to boost the efficiency of single-cell data analysis, laying the foundation for a revolutionary shift for scientists to analyze large single-cell datasets in real time. Reading sparse matrices is an essential step in single-cell analysis workflows. However, existing implementations are often inefficient. We offer a highly optimized approach that significantly accelerates the process. Our solution enables reading a sparse matrix up to 150 times faster compared to scipy in Python and Matrix in R. Geometric sketching is a useful technique to reduce the workload of your analyses by constructing a representative subset of your dataset. With Alpha SC’s implementation, this task can now finish in under half a second even for a dataset of 1.7M cells. PCA is a widely used dimensionality reduction technique in single-cell analysis. Without special optimizations, it is very memory-intensive to run on thousands of genes and up to millions of cells. Alpha SC provides a highly optimized GPU-accelerated implementation of PCA, yielding significant performance gains, while consuming little GPU memory. With this advancement, researchers can perform PCA up to 100 times faster. Harmony, a batch removal algorithm for scRNA-seq data, helps ensure that cells are clustered by biological similarity rather than technical variations. In addition to GPU acceleration, Alpha SC incorporates several algorithmic improvements that eliminate computationally intensive matrix operations. As a result, Alpha SC achieves a remarkable up to 400x speed improvement compared to both the original harmony and harmonypy implementation. Finding approximated nearest neighboring cells is a prerequisite for many subsequent steps in the pipeline. Alpha SC provides a highly optimized GPU implementation of NN-descent to unlock unprecedented performance. Our pipeline finishes this step 300 times faster than scanpy. Louvain clustering is a common choice for identifying distinct cell populations within single-cell datasets. However, it can be computationally intensive, and time-consuming for large-scale analyses. Utilizing GPU acceleration, Alpha SC achieves an impressive 1000x speed-up for some dataset while maintaining similar clustering quality. t-SNE (t-distributed Stochastic Neighbor Embedding), and UMAP (Uniform Manifold Approximation and Projection) are the two most popular visualization algorithms for single-cell data. Both algorithms benefit from the impressive improvements in our k-NN routine. And with our GPU accelerated-implementation, Alpha SC produces 2D t-SNE embeddings 700 times faster, and UMAP embeddings 100 times faster than Scanpy. AUCell helps identify enriched gene sets in each cell. Alpha SC implementation gains up to 1000x, and 500x speedup compared to the AUCell package for R, and the pySCENIC package for Python, respectively. Venice is a fast non-parametric test designed to find differentially expressed genes between heterogeneous populations. Now with GPU acceleration, Venice offers an even more impressive performance, while maintaining the same accuracy.
Required GPU
alphaSC
ADImpute: Adaptive Dropout Imputer

BioTuring

Single-cell RNA sequencing (scRNA-seq) protocols often face challenges in measuring the expression of all genes within a cell due to various factors, such as technical noise, the sensitivity of scRNA-seq techniques, or sample quality. This limitation gives rise to a need for the prediction of unmeasured gene expression values (also known as dropout imputation) from scRNA-seq data. ADImpute (Leote A, 2023) is an R package combining several dropout imputation methods, including two existing methods (DrImpute, SAVER), two novel implementations: Network, a gene regulatory network-based approach using gene-gene relationships learned from external data, and Baseline, a method corresponding to a sample-wide average.. This notebook is to illustrate an example workflow of ADImpute on sample datasets loaded from the package. The notebook content is inspired from ADImpute's vignette and modified to demonstrate how the tool works on BioTuring's platform.
Only CPU
ADImpute
pySCENIC: Single-Cell rEgulatory Network Inference and Clustering

BioTuring

SCENIC Suite is a set of tools to study and decipher gene regulation. Its core is based on SCENIC (Single-Cell Regulatory Network Inference and Clustering) which enables you to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data. pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell Regulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.
Only CPU
pySCENIC
spacexr: Robust Cell Type Decomposition and Cell type-Specific Inference of Differential Expression

BioTuring

Recent spatial transcriptomics (ST) technologies have allowed us to capture cellular heterogeneity while retaining spatial information. However, ST datasets may lose single-cell resolution, limiting the discovery of cell-type-specific spatial patterns of localization and expression. spacexr (Spatial-eXpression-R) is an R package providing two methods, i.e., Robust Cell Type Decomposition (RCTD) (Cable, Dylan M., et al., 2022) and Cell type-Specific Inference of Differential Expression (C-SIDE) (Cable, Dylan M., et al., 2022) for ST data. RCTD is proposed for cell type deconvolution, while leveraging references from another annotated single-cell RNA-seq data. C-SIDE identifies cell type-specific differential expression, accounting for localization of other cell types. We will illustrate an example workflow in two notebooks, RCTD and C-SIDE, on a hippocampus Visium dataset provided by the authors. The notebooks are inspired from spacexr's vignettes and modified to demonstrate how the tool works on BioTuring's platform.
Only CPU
spacexr
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-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
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 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.
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 assignment of cell type or cell state to each sequenced cell—is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts. **InferCNV** is a Bayesian method, which agglomerates the expression signal of genomically adjointed genes to ascertain whether there is a gain or loss of a certain larger genomic segment. We have used **inferCNV** to call copy number variations in all samples used in the manuscript.
Only CPU
inferCNV
A workflow to analyze cell-cell communications on Visium data

BioTuring

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.
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 and techinical differences. **Harmony** is an algorithm allowing fast, sensitive and accurate single-cell data integration.
Only CPU
harmonpy
CellDrift: Temporal perturbation effects for single cell data

BioTuring

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.
Only CPU
CellDrift
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

BioTuring

Single-cell RNA sequencing (scRNA-seq) data have allowed us to investigate cellular heterogeneity and the kinetics of a biological process. Some studies need to understand how cells change state, and corresponding genes during the process, but it is challenging to track the cell development in scRNA-seq protocols. Therefore, a variety of statistical and computational methods have been proposed for lineage inference (or pseudotemporal ordering) to reconstruct the states of cells according to the developmental process from the measured snapshot data. Specifically, lineage refers to an ordered transition of cellular states, where individual cells represent points along. pseudotime is a one-dimensional variable representing each cell’s transcriptional progression toward the terminal state. Slingshot which is one of the methods suggested for lineage reconstruction and pseudotime inference from single-cell gene expression data. In this notebook, we will illustrate an example workflow for cell lineage and pseudotime inference using Slingshot. The notebook is inspired by Slingshot's vignette and modified to demonstrate how the tool works on BioTuring's platform.
Only CPU
slingshot
SpatialData: Visualizations and spatial query on 10x Genomics Visium

BioTuring

SpatialData (Marconato, Luca, et al., 2023) is a framework for processing spatial omics data, including - spatialdata-io: load data from common spatial omics technologies into spatialdata. - spatialdata-plot: static plotting library for spatialdata. - napari-spatialdata: napari plugin for interactive exploration and annotation of spatial data. In this notebook, we will illustrate the visualization functions implemented in SpatialData for Visium data. For datasets from other spatial technologies, please check this document. Also, we will use spatial queries to retrieve all the spatial elements and instances that are within a given rectangular window or polygonal shape from an example Visium brain dataset. The notebook content is inspired from SpatialData's vignette and modified to demonstrate how the tool works on BioTuring's platform.
NicheNet: modeling intercellular communication by linking ligands to target genes

BioTuring

Computational methods that model how the gene expression of a cell is influenced by interacting cells are lacking. We present NicheNet, a method that predicts ligand–target links between interacting cells by combining their expression data with prior knowledge of signaling and gene regulatory networks. We applied NicheNet to the tumor and immune cell microenvironment data and demonstrated that NicheNet can infer active ligands and their gene regulatory effects on interacting cells.
Only CPU
nichenetr