We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs).

We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs).

2023年1月24日 · We present a novel statistical framework, scShapes, for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value.

Shape Context顾名思义，是一种根据图形形状进行检测识别的算法. 它的核心思想是： 选取图形形状轮廓中一定数量的点，通过计算每个点的周围点到该点的角度和距离来构建这个点对应的周围环境（context）矩阵，通过对不同图像之间的矩阵匹配度，来判断两幅图像是否拥有同样的形状。 形状上下文是一种很流行的形状描述子，原始论文已经被引用上千次了。 自然，很多研究者进行了诸多改进，但还是不能克服shape context一些自身的缺点。 下面先简单介绍形状上下文的一 …

Description We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs).

2008年10月26日 · LaTeX 的每种字体有5种属性：编码、族，形状，系列和尺寸。 下面讨论常用的几个：形状，系列，尺寸。 1）形状指的是倾斜和高矮。 2）系列是指字体的宽度和权重。 权重刻画了笔画的粗细。 以上这些皆为声明，在遇到新的声明前一直起作用。 还有一个非常重要的声明/normalfont，它把除了字体尺寸以外的属性都设置成默认值，即中等权重的直立的罗马字体。 下面是相应的字体命令。 这些命令可以组合使用, 例如需要加粗和斜体, 则使用/textbf {/textbf {文 …

2022年12月28日 · This analysis also draws attention to genes that switch distribution shapes from a unimodal distribution to a zero-inflated distribution and raises open questions about the plausible biological mechanisms that may give rise to this, such as transcriptional bursting.

\n Example \n. This is a basic example which shows how you can use scShapes for\nidentifying differential distributions in single-cell RNA-seq data.

This is the development version of scShapes; for the stable release version, see scShapes. We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs).

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