A computer-implemented method for identifying expression distinguishers comprising: accessing expression data of two or more biological samples, the expression data comprising signal intensity values corresponding to a respective two or more genes; generating an expression matrix from the signal intensities of the expression data and having dimensions representative of each gene and each individual biological sample; eliminating a subset of the two or more genes in the intensity matrix, wherein the subset comprises individual genes having an outlier signal intensity in the respective expression data of an individual biological sample, the outlier signal intensity deviating from signal intensities relative to other biological samples in the two or more biological samples, to generate an adjusted expression matrix; generating a conditional expression matrix, Q of size g x g from the expression matrix, wherein an element, Q i,j, of the gene-gene conditional expression matrix is: Q ̅ i, j = Pr f 2 = i | f 1 = j wherein i and j denote genes of two genes, f 1 and f 2; normalizing all rows of the joint expression matrix to generate a conditional expression matrix; determining a first gene expression distinguisher based on the highest magnitude row of the conditional expression matrix; re-centering the conditional expression matrix about the first gene expression distinguisher to generate a re-centered conditional expression matrix; determining a second gene expression distinguisher based on the highest magnitude row of the re-centered conditional expression matrix; determining two or more subsequent gene expression distinguishers based on respective highest magnitude orthogonal projections of each rows of the re-centered conditional expression matrix with another row of the re-centered conditional expression matrix; wherein, in re-centering, a row corresponding to a tentative gene expression distinguisher is subtracted from each row in the conditional expression matrix row; and generating an output that the genes associated with the highest magnitude rows are the gene expression distinguishers. accessing expression data of two or more biological samples, the expression data comprising signal intensity values corresponding to a respective two or more genes; generating an expression matrix from the signal intensities of the expression data and having dimensions representative of each gene and each individual biological sample; eliminating a subset of the two or more genes in the intensity matrix, wherein the subset comprises individual genes having an outlier signal intensity in the respective expression data of an individual biological sample, the outlier signal intensity deviating from signal intensities relative to other biological samples in the two or more biological samples, to generate an adjusted expression matrix; generating a conditional expression matrix, Q of size g x g from the expression matrix, wherein an element, Q i,j, of the gene-gene conditional expression matrix is: Q ̅ i, j = Pr f 2 = i | f 1 = j wherein i and j denote genes of two genes, f 1 and f 2; normalizing all rows of the joint expression matrix to generate a conditional expression matrix; determining a first gene expression distinguisher based on the highest magnitude row of the conditional expression matrix; re-centering the conditional expression matrix about the first gene expression distinguisher to generate a re-centered conditional expression matrix; determining a second gene expression distinguisher based on the highest magnitude row of the re-centered conditional expression matrix; determining two or more subsequent gene expression distinguishers based on respective highest magnitude orthogonal projections of each rows of the re-centered conditional expression matrix with another row of the re-centered conditional expression matrix; wherein, in re-centering, a row corresponding to a tentative gene expression distinguisher is subtracted from each row in the conditional expression matrix row; and generating an output that the genes associated with the highest magnitude rows are the gene expression distinguishers.