Brief. Bioinformatics - Principal component analysis based methods in bioinformatics studies.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ analysi(2126) use(1163) compon(1037) }
{ framework(1458) process(801) describ(734) }
{ research(1085) discuss(1038) issu(1018) }
{ learn(2355) train(1041) set(1003) }
{ data(3963) clinic(1234) research(1004) }
{ first(2504) two(1366) second(1323) }
{ assess(1506) score(1403) qualiti(1306) }
{ problem(2511) optim(1539) algorithm(950) }
{ method(1557) propos(1049) approach(1037) }
{ system(1050) medic(1026) inform(1018) }
{ cost(1906) reduc(1198) effect(832) }
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{ model(3480) simul(1196) paramet(876) }
{ imag(1947) propos(1133) code(1026) }
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{ data(1714) softwar(1251) tool(1186) }
{ blood(1257) pressur(1144) flow(957) }
{ health(3367) inform(1360) care(1135) }
{ state(1844) use(1261) util(961) }
{ group(2977) signific(1463) compar(1072) }
{ structur(1116) can(940) graph(676) }
{ use(976) code(926) identifi(902) }
{ featur(3375) classif(2383) classifi(1994) }
{ take(945) account(800) differ(722) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

In analysis of bioinformatics data, a unique challenge arises from the high dimensionality of measurements. Without loss of generality, we use genomic study with gene expression measurements as a representative example but note that analysis techniques discussed in this article are also applicable to other types of bioinformatics studies. Principal component analysis (PCA) is a classic dimension reduction approach. It constructs linear combinations of gene expressions, called principal components (PCs). The PCs are orthogonal to each other, can effectively explain variation of gene expressions, and may have a much lower dimensionality. PCA is computationally simple and can be realized using many existing software packages. This article consists of the following parts. First, we review the standard PCA technique and their applications in bioinformatics data analysis. Second, we describe recent 'non-standard' applications of PCA, including accommodating interactions among genes, pathways and network modules and conducting PCA with estimating equations as opposed to gene expressions. Third, we introduce several recently proposed PCA-based techniques, including the supervised PCA, sparse PCA and functional PCA. The supervised PCA and sparse PCA have been shown to have better empirical performance than the standard PCA. The functional PCA can analyze time-course gene expression data. Last, we raise the awareness of several critical but unsolved problems related to PCA. The goal of this article is to make bioinformatics researchers aware of the PCA technique and more importantly its most recent development, so that this simple yet effective dimension reduction technique can be better employed in bioinformatics data analysis.

Resumo Limpo

analysi bioinformat data uniqu challeng aris high dimension measur without loss general use genom studi gene express measur repres exampl note analysi techniqu discuss articl also applic type bioinformat studi princip compon analysi pca classic dimens reduct approach construct linear combin gene express call princip compon pcs pcs orthogon can effect explain variat gene express may much lower dimension pca comput simpl can realiz use mani exist softwar packag articl consist follow part first review standard pca techniqu applic bioinformat data analysi second describ recent nonstandard applic pca includ accommod interact among gene pathway network modul conduct pca estim equat oppos gene express third introduc sever recent propos pcabas techniqu includ supervis pca spars pca function pca supervis pca spars pca shown better empir perform standard pca function pca can analyz timecours gene express data last rais awar sever critic unsolv problem relat pca goal articl make bioinformat research awar pca techniqu import recent develop simpl yet effect dimens reduct techniqu can better employ bioinformat data analysi

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