Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data.
Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data.
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DOI：
10.1038/s41598-022-26434-1
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发表时间：
2023-01-21
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
--
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有效的降维对于单细胞RNA-seq（scRNAseq）分析至关重要。主成分分析（PCA）被广泛使用，但需要连续的正态分布数据;因此，它通常与scRNAseq应用中的对数变换相结合，这可能会扭曲数据并掩盖有意义的变化。我们描述了对应分析（CA），一个基于计数的替代PCA。CA基于卡方残差矩阵的分解，避免了失真的对数变换。为了解决scRNAseq数据中的过度分散和高稀疏性，我们提出了CA的五种适应性，这些适应性快速，可扩展，并且优于标准CA和glmPCA，以在9个数据集中的8个数据集中计算具有更高性能或可比聚类精度的细胞嵌入。特别是，我们发现CA与Freeman-Tukey残差在不同的数据集上表现得特别好。CA框架的其他优点包括在“CA双标图”中可视化基因和细胞群体之间的关联，并扩展到多表分析;我们引入corralm用于scRNAseq数据的综合多表降维。我们在corral中实现了scRNAseq数据的CA，这是一个R/Bioconductor包，它直接与Bioconductor中的单细胞类接口。从PCA到CA的切换是通过简单的管道替换实现的，并改善了scRNAseq数据集的降维。
Effective dimension reduction is essential for single cell RNA-seq (scRNAseq) analysis. Principal component analysis (PCA) is widely used, but requires continuous, normally-distributed data; therefore, it is often coupled with log-transformation in scRNAseq applications, which can distort the data and obscure meaningful variation. We describe correspondence analysis (CA), a count-based alternative to PCA. CA is based on decomposition of a chi-squared residual matrix, avoiding distortive log-transformation. To address overdispersion and high sparsity in scRNAseq data, we propose five adaptations of CA, which are fast, scalable, and outperform standard CA and glmPCA, to compute cell embeddings with more performant or comparable clustering accuracy in 8 out of 9 datasets. In particular, we find that CA with Freeman–Tukey residuals performs especially well across diverse datasets. Other advantages of the CA framework include visualization of associations between genes and cell populations in a “CA biplot,” and extension to multi-table analysis; we introduce corralm for integrative multi-table dimension reduction of scRNAseq data. We implement CA for scRNAseq data in corral, an R/Bioconductor package which interfaces directly with single cell classes in Bioconductor. Switching from PCA to CA is achieved through a simple pipeline substitution and improves dimension reduction of scRNAseq datasets.
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影响因子：
9.3
作者：
Baron M;Veres A;Wolock SL;Faust AL;Gaujoux R;Vetere A;Ryu JH;Wagner BK;Shen-Orr SS;Klein AM;Melton DA;Yanai I
通讯作者：
Yanai I
DOI：
10.1073/pnas.1507125112
发表时间：
2015-06-09
影响因子：
11.1
作者：
Darmanis S;Sloan SA;Zhang Y;Enge M;Caneda C;Shuer LM;Hayden Gephart MG;Barres BA;Quake SR
通讯作者：
Quake SR
影响因子：
1.9
作者：
BARTLETT, MS
通讯作者：
BARTLETT, MS
影响因子：
48
作者：
Amezquita, Robert A.;Lun, Aaron T. L.;Hicks, Stephanie C.
通讯作者：
Hicks, Stephanie C.
影响因子：
5.8
作者：
Busold, CH;Winter, S;Fellenberg, K
通讯作者：
Fellenberg, K