Functional clustering of time series gene expression data by Granger causality

Abstract

BackgroundA common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes.ResultsIn this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence.ConclusionsThis kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.

DNA Damage Is a Potential Marker for TP53 Mutation in Colorectal Carcinogenesis

“…In 66 % of the adenocarcinoma tissues, intense oxidative DNA damage was observed and was associated with TP53 mutated expression, whereas in adenomas this value was 57 %, corroborating the findings reported in literature [25]. Oliva et al evaluated the expression of several genes related to colorectal carcinogenesis and demonstrated that 71 % of patients with high levels of oxidative stress also have elevated rates of TP53 mutated expression [22].…”

“…The Kruskal-Wallis test was used to compare the oxidative DNA damage and TP53 in different tissues, and the Spearman test was used to correlate variables. Partial correlation was used to describe the association between TP53 in normal tissues and TP53 in tumor tissues explained by the effect of oxidative DNA damage, in accordance with the directions of the program manual and the Granger hypothesis [25]. The central concept in partial correlation analysis is the partial correlation coefficient (r xy.z ) between the variables x (TP53 in normal tissue) and y (TP53 in tumor tissue), adjusted for a third variable z (oxidative DNA damage).…”

Clustering of Bacterial Growth Dynamics in Response to Growth Media by Dynamic Time Warping

“…In addition, clustering was used to divide the time series data into groups based on similarity, without advanced knowledge of the definitions of the groups [34], and has been applied in the fields of climate, environment, economy, finance, medicine, and biology [35][36][37][38][39]. Most of the applications in biology were related to genes [37,[40][41][42][43]. The present study provided the first application for bacterial growth analysis.…”

Dense time-course gene expression profiling of the Drosophila melanogaster innate immune response

“…Bivariate GC analysis between two genes A and B, as described above, does not account for possible confounding effects of other genes C, D, E which can also influence genes A and B ( Figure 8B ). Multivariate GC analysis alleviates this problem by explicitly accounting for the effects of the confounding genes by a joint modeling ( Fujita et al 2012 ;Finkle, Wu, and Bagheri 2018 ), but does not account for high-dimensionality and consequently cannot jointly model hundreds of genes based on tens of data points. We used modern high-dimensional methods (viz LASSO ( Tibshirani 1996 ) and de-biased LASSO ( Javanmard and Montanari 2014, Dezeure et al 2015 )) to address this problem and build lead-lag network models among 258 genes.…”