Just published: UMAPs have become a very popular tool for visualizing high-dimensional data in biology, but they have significant drawbacks. In https://doi.org/10.1186/s12859-024-05927-y Kitanovski et al describe scBubbletree (sc because our focus is on single cell data) as a better alternative: it is easier to interpret, quantitative, and allows for integration of different types of data. The method is implemented in a free, open source R package (https://bioconductor.org/packages/release/bioc/html/scBubbletree.html). I am sure that this type of quantitative visualization is beneficial beyond single cell gene expression data.
#umap #scRNA_seq #bioinformatics
scBubbletree: computational approach for visualization of single cell RNA-seq data - BMC Bioinformatics
Background Visualization approaches transform high-dimensional data from single cell RNA sequencing (scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell relationships, and as a means of reporting biological insights. Yet, many standard approaches generate visuals that suffer from overplotting, lack of quantitative information, and distort global and local properties of biological patterns relative to the original high-dimensional space. Results We present scBubbletree, a new, scalable method for visualization of scRNA-seq data. The method identifies clusters of cells of similar transcriptomes and visualizes such clusters as “bubbles” at the tips of dendrograms (bubble trees), corresponding to quantitative summaries of cluster properties and relationships. scBubbletree stacks bubble trees with further cluster-associated information in a visually easily accessible way, thus facilitating quantitative assessment and biological interpretation of scRNA-seq data. We demonstrate this with large scRNA-seq data sets, including one with over 1.2 million cells. Conclusions To facilitate coherent quantification and visualization of scRNA-seq data we developed the R-package scBubbletree, which is freely available as part of the Bioconductor repository at: https://bioconductor.org/packages/scBubbletree/
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New tool decodes complex, single-cell genomic data
Unlocking biological information from complex single-cell genomic data has just become easier and more precise, thanks to the innovative scLENS tool developed by the Biomedical Mathematics Group within the IBS Center for Mathematical and Computational Sciences led by Chief Investigator Kim Jae Kyoung, who is also a Professor at KAIST. This represents a significant leap forward in the field of single-cell transcriptomics.
Need to integrate
#SingleCellOmics datasets? This new paper by SIB's
@Carmona et al.
introduces STACAS, a flexible framework to reduce batch effect in
#scrna_seq data by taking advantage of prior cell type knowledge
https://www.nature.com/articles/s41467-024-45240-z
Semi-supervised integration of single-cell transcriptomics data - Nature Communications
Batch effects hinder multi-sample single-cell data analyses. Here, authors present STACAS, a scalable single-cell RNA-seq data integration tool that uses prior cell type knowledge to preserve biological variability, demonstrating robustness to noisy input cell type labels.
'Invisible' cell types and gene expression revealed with sequencing data analysis improvement
In 2018, researchers in the Caltech laboratory of Yuki Oka, professor of biology and Heritage Medical Research Institute Investigator, made a major discovery: They identified a type of neuron, or brain cell, that mediates thirst satiation. But they were running into a problem: A state-of-the-art technique called single-cell RNA sequencing (scRNA-seq) could not find those thirst-related neurons in samples of brain tissue (specifically, from a region called the media preoptic nucleus) that were known to contain them.
Improved RNA sequencing technologies provide deeper insights into bacteria
How do cells work in a normal state? How do they change when they cause disease? Do they react as desired to new drugs? Nowadays, anyone seeking answers to these—and other related—questions in the laboratory can hardly do without a special technique: single-cell RNA sequencing, or "scRNA-seq" for short. This technique provides an accurate picture of gene expression in a single cell at a specific point of time, as well as the associated regulatory networks, allowing conclusions to be drawn about the molecular basis of cell activity.
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