Coarse-graining of large single-cell RNA-seq data into metacells r-packagescrna-seq-datacoarse-grainingscrna-seq-analysis UpdatedMar 21, 2024 R 🦠 Regularly updated list of publicly available datasets with single-cell (scRNAseq) and T-cell/antibody immune repertoire (AIRR / RepSeq / immunose...
其实对RNA-seq数据的分析，并不能用“调参数”去形容。调参数对比较计算建模或者机器学习的问题来说更...
与bulk RNA-seq不同,scRNA-seq提供了单个细胞中每个基因表达的定量测量。然而,要分析scRNA-seq数据,需要新的方法,并且为bulk RNA-seq实验开发的方法的一些基本假设不再有效。在本课程中,我们将介绍scRNA-seq处理的所有步骤,从来自测序仪的原始读数开始。本课程包括使用最先进的方法的常用的分析策略,我们还讨论了可以...
提取转换后的值 转换函数返回一个DESeqTransform类的对象,它是RangedSummarizedExperiment的一个子类。 对于在新创建的DESeqDataSet上运行的约20个样本,rlog转换可能需要30秒,而VST转换只需要不到1秒。 当使用blind = FALSE并且DESeq函数已经运行时,运行时间会更短,因为不需要重新估计色散值。 assay函数用于提取标准化...
ChIP-Seq data analysis is often a cumbersome process, requiring tedious project setup and time-consuming, complicated data manipulation.Lasergene Genomicsmakes it quick and easy to set up your ChIP-Seq analysis project, by offering an easy-to-use wizard that guides you through project setup in ...
Computational cell type identification is a fundamental step in single-cell omics data analysis. Supervised celltyping methods have gained increasing popularity in single-cell RNA-seq data because of the superior performance and the availability of high-quality reference datasets. Recent technological advan...
an automatic tool to annotate cell types from single-cell RNA-seq data, based on a score annotation model combining differentially expressed genes and confidence levels of cell markers in databases. Evaluation on real scRNA-seq datasets that SCSA is able to assign the cells into the correct types...
wherehiis the gene-specific bandwidth parameter that controls the resolution of the kernel estimation, which is set tohi=si/4, wheresiis the sample standard deviation of thei-th gene (Figure1, step 1). In the case of RNA-seq data, a discrete Poisson kernel [26] is employed: ...
Single-cell open chromatin profiling via scATAC-seq has become a mainstream measurement of open chromatin in single-cells. Here we present epiAneufinder, an algorithm that exploits the read count information from scATAC-seq data to extract genome-wide copy number alterations (CNAs) for individual ...
wherehiis the gene-specific bandwidth parameter that controls the resolution of the kernel estimation, which is set tohi=si/4, wheresiis the sample standard deviation of thei-th gene (Figure1, step 1). In the case of RNA-seq data, a discrete Poisson kernel [26] is employed: ...