微阵列矩阵数据结构： (1)time series data (2) case-control data assay 1234 gene sample 1 gene d1 d2d3 n1n2n3 2 名 g1 3 3 4 gi 行
微阵列矩阵数据结构: (1) time series data (2) case-control data
SVD analysis of gene expression data Let X denote an m x n matrix of microarray data and has rank r.In the case of microarray data,xis the expression level of the ith gene in the jth assay. (1)The elements of the ith row of X form the n-dimensional vector gi,which we refer to as the transcriptional response of the ith gene. (2)Alternatively,the elements of the jth column of X form the m-dimensional vector a,,which we refer to as the expression profile of the ith assay. 关键是赋予计算结果的生物意义
SVD analysis of gene expression data Let X denote an m x n matrix of microarray data and has rank r. In the case of microarray data, xij is the expression level of the ith gene in the jth assay. (1) The elements of the ith row of X form the n-dimensional vector gi, which we refer to as the transcriptional response of the ith gene. (2) Alternatively, the elements of the jth column of X form the m-dimensional vector aj, which we refer to as the expression profile of the jth assay. 关 键 是 赋 予 计 算 结 果 的 生 物 意 义
SVD analysis of gene expression data Concepts: X=USVT Eigenassay Singular Eigengene a Value a n n n n X I n n g Si m m m×n m×n n×n n×n
SVD analysis of gene expression data Concepts:
SVD analysis of gene expression data the gene transcriptional responses {gi} the assay expression profiles fa the right singular vectors span the space of the gene transcriptional responses,so the right singular vectors v is defined as eigengenes. the left singular vectors span the space of the assay expression profiles,so the left singular vectors {u is defined as eigenassays
SVD analysis of gene expression data the gene transcriptional responses { g i } the assay expression profiles { aj}. the right singular vectors span the space of the gene transcriptional responses, so the right singular vectors { v k} is defined as eigengenes. the left singular vectors span the space of the assay expression profiles, so the left singular vectors { u k} is defined as eigenassays
SVD analysis of gene expression data g=∑4Vg,i:l,m a,=∑yu,jl,n
SVD analysis of gene expression data