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CellRank2 (Weiler et al, 2023) is a powerful framework for studying cellular fate using single-cell RNA sequencing data. It can handle millions of cells and different data types efficiently. This tool can identify cell fate and probabilities across various data sets. It also allows for analyzing transitions over time and uncovering key genes in developmental processes. Additionally, CellRank2 estimates cell-specific transcription and degradation rates, aiding in understanding differentiation trajectories and regulatory mechanisms.
In this notebook, we will use a primary tumor sample of patient T71 from the dataset GSE137804 (Dong R. et al, 2020) as an example. We have performed RNA-velocity analysis and pseudotime calculation on this dataset in scVelo (Bergen et al, 2020) notebook. The output will be then loaded into this CellRank2 notebook for further analysis.
This notebook is based on the tutorial provided on CellRank2 documentation. We have modified the notebook and changed the input data to show how the tool works on BioTuring's platform.
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.
BioTuring
Spatial transcriptomics (ST) technology has allowed to capture of topographical gene expression profiling of tumor tissues, but single-cell resolution is potentially lost. Identifying cell identities in ST datasets from tumors or other samples remains challenging for existing cell-type deconvolution methods.
Spatial Cellular Estimator for Tumors (SpaCET) is an R package for analyzing cancer ST datasets to estimate cell lineages and intercellular interactions in the tumor microenvironment. Generally, SpaCET infers the malignant cell fraction through a gene pattern dictionary, then calibrates local cell densities and determines immune and stromal cell lineage fractions using a constrained regression model. Finally, the method can reveal putative cell-cell interactions in the tumor microenvironment.
In this notebook, we will illustrate an example workflow for cell type deconvolution and interaction analysis on breast cancer ST data from 10X Visium. The notebook is inspired by SpaCET's vignettes and modified to demonstrate how the tool works on BioTuring's platform.
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.
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BioTuring
Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Deepcell shows that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types. The authors share their experience in designing and optimizing deep convolutional neural networks for this task and propose some design rules to achieve stable performance. The authors conclude that deep convolutional neural networks are an accurate, time-saving, applicable method for many types of cells, from bacteria to animal cells, and expand the capabilities of live-cell imaging to include multi-cell systems.
Deepcell library allows users to apply pre-existing models to imaging data as well as to develop new deep learning models for single-cell analysis. This library specializes in models for cell segmentation (whole-cell and nuclear) in 2D and 3D images as well as cell tracking in 2D time-lapse datasets. These models are applicable to data ranging from multiplexed images of tissues to dynamic live-cell imaging movies.
deepcell-tf which is written in Python using TensorFlow, is a deep learning library for single-cell analysis of biological images. It is one of several resources created by the Van Valen lab to facilitate the development and application of new deep learning methods to biology.