Diagnosis of multiple cancer types by shrunken centroids of gene expression

By Robert Tibshirani, Trevor J. Hastie, Balasubramanian Narasimhan, Gilbert Chu. 2002

3,009 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Towards Open Set Deep Networks (2015, methodology): “To recognize outliers using AVs, we adapt the concepts of Nearest Class Mean [29, 12] or Nearest Non-Outlier [1] and apply them per class within the activation vector, as a first approximation.”
Gene selection and classification of microarray data using random forest (2006, neutral): “• Shrunken centroids (SC) The method of "nearest shrunken centroids" was originally described in [33].”
A Simple Method for Detecting Interactions between a Treatment and a Large Number of Covariates (2012, methodology): “We applied the nearest shrunken centroid classifier [Tibshirani et al., 2001] to the modified data (6) with the shrinkage parameter selected via 10 fold crossvalidation.”
Automated identification of stratifying signatures in cellular subpopulations (2014, methodology): “Accord-ingly, Citrus constructs classification models using the lasso-regularized logistic regression and nearest shrunken centroid methods (21, 22).”
Increased expression of genes converting adrenal androgens to testosterone in androgen-independent prostate cancer. (2006, methodology): “The PAM algorithm was used to determine which genes were the most predictive classifiers of primary versus metastatic androgenindependent prostate cancer (36).”
Extracting Conserved Gene Expression Motifs from Gene Expression Data (2002, background): “3 ; 8 When the goal is to analyse the data in an unsupervised manner, techniques such as k -means clustering and hierarchical clustering 9 are common.”
Transcriptomic responses to heat stress and bleaching in the elkhorn coral Acropora palmata (2010, positive): “Missing values were imputed using PAMR (Tibshirani et al. 2002).”
Network-based stratification of tumor mutations (2013, methodology): “For this purpose, we explored the nearest shrunken centroid approach 38 , a standard method for sample classification that summarizes each subtype with a class ‘centroid’ and assigns new samples to the subtype with closest centroid.”
Classifying Gene Expression Profiles from Pairwise mRNA Comparisons (2004, positive): “Detection of marker gene pairs is a problem in feature selection, and plays the same role in our analysis as finding individual marker genes does in more standard methods (Dudoit et al. (2002); Sebastiani et al. (2003); Tibshirani et al. (2002); Stolovitzky (2003)).”
The Herbicide Saflufenacil (Kixor™) is a New Inhibitor of Protoporphyrinogen IX Oxidase Activity (2010, methodology): “For modeof-action cluster analysis, prediction analysis of microarrays (PAM, described in Tibshirani et al. 2002) and partial least square–discriminant analysis (PLS-DA, described in van Ravenzwaay et al. 2007) were used as multivariate statistical data analysis.”
Weighted k-Nearest-Neighbor Techniques and Ordinal Classification (2004, methodology): “First, in addition to our nearest neighbor techniques we apply a recent method called prediction analysis of microarray (PAM) which was especially designed for high-dimensional microarray data (Tibshirani et al. (2002)) and works without gene selection.”
On testing the significance of sets of genes (2006, methodology): “As a baseline competitor, we applied the nearest shrunken centroid classifier of (Tibshirani et al. 2001).”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, background): “Future developments in this field will likely borrow from the mass cytometry (e.g. Tibshirani et al , 2002; Arvaniti & Claassen, 2017; Lun et al , 2017; Weber et al , 2018) or the microbiome literature (Gloor et al , 2017), where compositional data analysis has received more attention.”
Bug isolation via remote program sampling (2003, methodology): “Some methods [15, 24] calculate a univariate correlation coefficient independently for each feature; other methods, such as decision trees [6], are more computationally intensive.”