读文章 using single cell sequencing data to model the evolutionary history of a tumor

本文探讨了如何通过贝叶斯公式计算mutationsite之间的变异顺序,构建基于后验概率的有向图,并使用最小支撑树算法寻找关键路径。关键疑问集中在两个mutationsite的可能关系及第11页的错误率计算。

这篇文章的关键是如何计算两个 mutation sites 的 mutation order 的后验概率。
主要是利用贝叶斯公式。
然后再据此构建一个有权重的有向图。利用最小支持树算法找到这个有向图的最小支撑树。
这篇文章有两个地方不大清楚。
1: 两个 mutation sites xxxyyy 的关系是 x−>yx->yx>y 或者 y−>xy->xy>x 或者 xnot<−>yx not <-> yxnot<>y, 除此之外还有无别的可能。
2: 在第11面这个地方,在计算 error rates FD 和 AD 的时候,应该是一个 typo.

STELLAR (Spatially-resolved Transcriptomics with Ellipsoid Decomposition and Latent Actualization for Reconstruction) is a computational tool developed by researchers at the Broad Institute of MIT and Harvard for annotating spatially resolved single-cell data. It uses a combination of machine learning algorithms and image analysis techniques to identify cell types and characterize gene expression patterns within individual cells. To use STELLAR, researchers first generate spatially resolved single-cell data using techniques such as spatial transcriptomics or in situ sequencing. This data typically consists of spatial coordinates for each cell, as well as information on gene expression levels for a large number of genes. STELLAR then uses a number of different algorithms to analyze this data and identify cell types. First, it uses an ellipsoid decomposition algorithm to model the spatial distribution of cells within the tissue sample. This allows it to identify clusters of cells that are likely to be of the same type. Next, STELLAR uses a latent actualization algorithm to model the gene expression patterns within each cell. This allows it to identify genes that are expressed at high levels within specific cell types, and to assign cell type labels to individual cells based on their gene expression profiles. Overall, STELLAR provides a powerful tool for analyzing spatially resolved single-cell data, and has the potential to significantly advance our understanding of cellular organization and function within complex tissues.
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