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Advances in multi-omics have led to an explosion of multimodal datasets to address questions from basic biology to translation. While these data provide novel opportunities for discovery, they also pose management and analysis challenges, thus motivating the development of tailored computational solutions. `muon` is a Python framework for multimodal omics.
It introduces multimodal data containers as `MuData` object. The package also provides state of the art methods for multi-omics data integration. `muon` allows the analysis of both unimodal omics and multimodal omics.
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.
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.
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.
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An R toolkit for accurate and efficient estimation of cell composition ('decomposition') from bulk expression data with single-cell information.
Bisque provides two modes of operation:
* Reference-based decomposition: This method utilizes single-cell data to decompose bulk expression. Bisque assumes that both single-cell and bulk counts are measured from the same tissue. Specifically, the cell composition of the labeled single-cell data should match the expected physiological composition. While Bisque doesn't explicitly require matched samples, Bisque expect having samples with both single-cell and bulk expression measured will provide more accurate results.
* Marker-based decomposition: This method utilizes marker genes alone to decompose bulk expression when a reference profile is not available. Single-cell data is not explicitly required but can be used to identify these marker genes. This method captures relative abundances of a cell type across individuals. Note that these abundances are not proportions, so they cannot be compared between different cell types.