An Order Estimation Based Approach to Identify Response Genes for Microarray Time Course Data

Abstract

Gene expression profiles from microarray time course experiments provide a unique opportunity to examine genome-wide signal processing and gene responses. A fundamental issue in microarray experiments is that the treatment condition can only be controlled at the cell level rather than at the gene level. The treatment condition does not affect all genes equally. Some genes depend on other genes to detect external changes. The dependency between genes is not fully deterministic and may vary with treatment condition. Thus the expression of each gene is potentially affected by two confounding effects: the treatment effect and the gene context effect arising from the regulatory interactions among genes. This gene context effect is hard to isolate. Neither can it be simply ignored. Instead, this gene context information which may be different under different treatment conditions is of primary biological interest. We introduce an approach which deals with the confounding effects and takes into account the uncontrollable gene context effect. Our method is based on the estimation of the number of hidden states, which, in our development, corresponds to the order of a hidden Markov model (HMM). For each gene, its observed expression is modeled by a gamma distribution determined by the corresponding hidden state at each time point. Those genes showing evidence for more than one hidden state can be categorized as the signalling genes, or in a wider sense, as the response genes which are coordinated by a cell system in reaction to a specific external condition. These response genes can be used in the comparison of different treatment conditions, to investigate the gene context effect under different treatments. Microarray time course data are also analyzed to demonstrate our method.

Investigating long range correlation in DNA sequences using significance tests of conditional mutual information

“…Apparently, for sequences with LRC the estimation fails as there is no characteristic order, and one expects that increasing the length of the symbol sequence the order estimate increases as well. However, this idea is met with practical limitations, mainly due to the inefficiency of the current methods in estimating high orders (Dalevi et al, 2006;Lu et al, 2012;Papapetrou and Kugiumtzis, 2013). We deal with this problem here and propose a method that can assess whether a given symbol sequence has a characteristic order (the Markov chain order) found by saturation of the estimated order with the increase of the subsequence length (up to the actual sequence length), or alternatively the symbol sequence has LRC structure or a high Markov chain order that cannot be estimated on the basis of the given symbol sequence length.…”