Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments

被引：196

作者：

Bemis, Kyle D. ^{[1
]}

Harry, April ^{[1
]}

Eberlin, Livia S. ^{[2
]}

Ferreira, Christina ^{[2
]}

van de Ven, Stephanie M. ^{[3
]}

Mallick, Parag ^{[3
]}

Stolowitz, Mark ^{[3
]}

Vitek, Olga ^{[4
,5
]}

机构：

[1] Purdue Univ, Dept Stat, W Lafayette, IN 47907 USA

[2] Purdue Univ, Dept Chem, W Lafayette, IN 47907 USA

[3] Stanford Univ, Sch Med, Canary Ctr Stanford Canc Early Detect, Palo Alto, CA 94304 USA

[4] Northeastern Univ, Coll Sci, Boston, MA 02115 USA

[5] Northeastern Univ, Coll Comp & Informat Sci, Boston, MA 02115 USA

来源：

BIOINFORMATICS | 2015年 / 31卷 / 14期

基金：

美国国家科学基金会; 芬兰科学院;

关键词：

D O I：

10.1093/bioinformatics/btv146

中图分类号：

Q5 [生物化学];

学科分类号：

070307 [化学生物学];

摘要：

Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to pre-defined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization.

引用

页码：2418 / 2420

页数：3

共 7 条

[1]

Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering [J].