Data analysis in microarray experiment

Transcription

1 Chinese Bulletin of Life Sciences Vol. 16, No. 1 Feb., (004) Q33 A Data analysis in microarray experiment YANG Chang, FANG Fu-De * (National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, CAMS and PUMC, Beijing , China) Abstract: Genechip, representing the integration of multiple scientific field progress, is a newly developing high technology used to solve biology puzzle with automated and high-flux features. It has a successful application in large-scale research in genes function. After a relative long developing period, the method how to efficiently analyse mass data generated by chip experiment has become a hotspot in the chip research community. In principal, the method can be divided into two categories, the unsupervised method and the supervised method. Besides data analysis itself, two procedures performing before and after the analysis need to be paid attention to: the former is data normalization and reduction, the later is statistical test and biologic verification. Many commonly used statistical approaches and their merits and demerits will be introduced. Key words: genechip; data analysis 1 genechip [1] biochip ( chip) microarray Affymetrix (1977 ) (1941 ) *

2 4 ONA c CDA [] (matrix) 3 (Format I) (Format II) (normalization ) [] 3.1 [] 3. [3] mrna Cy5 Cy3 3.3 [3] mrna Cy5 Cy3 Cy5 Cy3 ratio log ratio Cy5/Cy3 (vector) LOWESS (locally weighted scatterplot smoothing) 3.4 [3] A A

3 43 K T K =R K /G K R K G K [7] D's(n-1) [8] 4 (filtering) [5] (dimension reduction) [4] (unsupervised) (supervised) (non-redundant) 5.1 (unsupervised grouping) [4] (cluster analysis) 7 (1) () (classification) (hierarchical clustering) Bayesian K (K-means clustering) (self-organizing maps, SOMs) (principal component analysis, PCA) (two-way clustering) [9] (deterministic annealing) [9] (graph-theoretic) [10] (neural network clustering) (multidimensional scaling analysis) [11] (divisive) (agglomerative) K 5 (legal genes) (active genes) [6] (relevant gene informative gene) [6]

4 44 (metric distances) (semi-metric distances) (Euclidean distance) d ij i j (1)d ij 0 () d ij =d ji (3)d ii =0 (average-linkage) UPGM (unweighted pair-group method average) (4) i j k d ik d ij +d jk (x 1,x,x 3 ) (y 1,y,y 3 ) d 1 x i y i X Y i n K-means (self-organized maps, SOMs) [3] 5.1. K-means K-means (similarity matrix) k (hierarchical clustering) [3,1] (vector) (cluster) k ( 1) (single-linkage) 1 K-means (complete-linkage) (self-organized maps SOMs) [3] SOMs 3

5 principal component analysis PCA [3] PCA PCA PCA 1 SOMs) 5. (supervised grouping) [4] ( K-means Time 87.5% 8 A C E B D (classification) (support vector machine SVM) [13] SOMs SVM SOMs K-means SVM SOMs

6 46 q P q q Bonferroni Bon- P (overabundance P ferroni analysis) P P [6] htm) 7 ( PRM [15] [6] probabilistic relational models, logistic (neural network) analysis LDA) (1inear discriminant [16] GenMAPP 6 genmapp.org Pubgene t Stanford (significance 8 analysis of microarrays, SAM) Excel ~tibs/sam Ben-Dor [14] (the threshold number of misclassification, TNoM) (score)

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