Journal ArticleDOI
What is principal component analysis
Reads0
Chats0
TLDR
Principal component analysis is often incorporated into genome-wide expression studies, but what is it and how can it be used to explore high-dimensional data?Abstract:
Principal component analysis is often incorporated into genome-wide expression studies, but what is it and how can it be used to explore high-dimensional data?read more
Citations
More filters
Journal ArticleDOI
Principal component analysis: a review and recent developments
TL;DR: The basic ideas of PCA are introduced, discussing what it can and cannot do, and some variants of the technique have been developed that are tailored to various different data types and structures.
Journal ArticleDOI
Science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems
Andrea C. Ferrari,Francesco Bonaccorso,Francesco Bonaccorso,Vladimir I. Fal'ko,Konstantin S. Novoselov,Stephan Roche,Peter Bøggild,Stefano Borini,Frank H. L. Koppens,Vincenzo Palermo,Nicola M. Pugno,Nicola M. Pugno,Nicola M. Pugno,Jose A. Garrido,Roman Sordan,Alberto Bianco,Laura Ballerini,Maurizio Prato,Elefterios Lidorikis,Jani Kivioja,Claudio Marinelli,Tapani Ryhänen,Alberto F. Morpurgo,Jonathan N. Coleman,Valeria Nicolosi,Luigi Colombo,Albert Fert,Albert Fert,Mar García-Hernández,Adrian Bachtold,Grégory F. Schneider,Francisco Guinea,Cees Dekker,Matteo Barbone,Zhipei Sun,Costas Galiotis,Alexander N. Grigorenko,Gerasimos Konstantatos,Andras Kis,Mikhail I. Katsnelson,Lieven M. K. Vandersypen,A. Loiseau,Vittorio Morandi,Daniel Neumaier,Emanuele Treossi,Vittorio Pellegrini,Vittorio Pellegrini,Marco Polini,Alessandro Tredicucci,Gareth M. Williams,Byung Hee Hong,Jong Hyun Ahn,Jong Min Kim,Herbert Zirath,Bart J. van Wees,Herre S. J. van der Zant,Luigi Occhipinti,Andrea di Matteo,Ian A. Kinloch,Thomas Seyller,Etienne Quesnel,Xinliang Feng,K.B.K. Teo,Nalin Rupesinghe,Pertti Hakonen,Simon R. T. Neil,Quentin Tannock,Tomas Löfwander,Jari M. Kinaret +68 more
TL;DR: An overview of the key aspects of graphene and related materials, ranging from fundamental research challenges to a variety of applications in a large number of sectors, highlighting the steps necessary to take GRMs from a state of raw potential to a point where they might revolutionize multiple industries are provided.
Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling
TL;DR: Using singular value decomposition in transforming genome-wide expression data from genes x arrays space to reduced diagonalized "eigengenes" x "eigenarrays" space gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype.
Journal ArticleDOI
A critical function for transforming growth factor-beta, interleukin 23 and proinflammatory cytokines in driving and modulating human T(H)-17 responses.
Elisabetta Volpe,Nicolas Servant,Nicolas Servant,Nicolas Servant,Raphaël Zollinger,Raphaël Zollinger,Sofia I. Bogiatzi,Sofia I. Bogiatzi,Philippe Hupé,Emmanuel Barillot,Emmanuel Barillot,Emmanuel Barillot,Vassili Soumelis,Vassili Soumelis +13 more
TL;DR: It is shown here that transforming growth factor-β, interleukin 23 (IL-23) and proinflammatory cytokines ( IL-1β and IL-6) were all essential for human TH-17 differentiation and provide a framework for the global analysis of T helper responses.
Journal ArticleDOI
Single-cell dissection of transcriptional heterogeneity in human colon tumors
Piero Dalerba,Tomer Kalisky,Debashis Sahoo,Pradeep S. Rajendran,Michael E. Rothenberg,Anne A Leyrat,Sopheak Sim,Jennifer Okamoto,Jennifer Okamoto,Darius M. Johnston,Darius M. Johnston,Dalong Qian,Maider Zabala,Janet Bueno,Norma F. Neff,Jianbin Wang,Andrew A. Shelton,Brendan C. Visser,Shigeo Hisamori,Yohei Shimono,Marc van de Wetering,Hans Clevers,Michael F. Clarke,Stephen R. Quake,Stephen R. Quake +24 more
TL;DR: It is demonstrated that the transcriptional diversity of cancer tissues is largely explained by in vivo multilineage differentiation and not only by clonal genetic heterogeneity.
References
More filters
Reference EntryDOI
Principal Component Analysis
TL;DR: Principal component analysis (PCA) as discussed by the authors replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables.
Journal ArticleDOI
Molecular portraits of human breast tumours
Charles M. Perou,Therese Sørlie,Michael B. Eisen,Matt van de Rijn,Stefanie S. Jeffrey,Christian A. Rees,Jonathan R. Pollack,Douglas T. Ross,Hilde Johnsen,Lars A. Akslen,Øystein Fluge,Alexander Pergamenschikov,Cheryl A. Williams,Shirley Zhu,Per Eystein Lønning,Anne Lise Børresen-Dale,Patrick O. Brown,David Botstein +17 more
TL;DR: Variation in gene expression patterns in a set of 65 surgical specimens of human breast tumours from 42 different individuals were characterized using complementary DNA microarrays representing 8,102 human genes, providing a distinctive molecular portrait of each tumour.
Journal ArticleDOI
Independent component analysis, a new concept?
TL;DR: An efficient algorithm is proposed, which allows the computation of the ICA of a data matrix within a polynomial time and may actually be seen as an extension of the principal component analysis (PCA).
Journal ArticleDOI
The biplot graphic display of matrices with application to principal component analysis
TL;DR: In this article, a matrix of rank two can be represented as a biplot, which consists of a vector for each row and a column, chosen so that any element of the matrix is exactly the inner product of the vectors corresponding to its row and to its column.
Journal ArticleDOI
Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks
Javed Khan,Jun S. Wei,Markus Ringnér,Markus Ringnér,Lao H. Saal,Marc Ladanyi,Frank Westermann,Frank Berthold,Manfred Schwab,Cristina R. Antonescu,Carsten Peterson,Paul S. Meltzer +11 more
TL;DR: The ability of the trained ANN models to recognize SRBCTs is demonstrated, and the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy are demonstrated.
Related Papers (5)
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini,Yosef Hochberg +1 more