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Time-course data analysis software tools | Gene expression microarray

The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) genes from the study and determining which genes are differentially expressed. Often these analysis stages are applied disregarding the fact that the data is drawn from a time series.

Source text:
(Kalaitzis and Lawrence 2011) A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression. BMC Bioinformatics.

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Allows the analysis of multiple time course transcriptomics data. maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments. The software incorporates a clustering function to visualize genes with similar profiles. maSigPro was initially developed for microarrays and later updated to model count data. It includes Iso-maSigPro, a functionality to study differential isoform usage in time course RNA-seq experiments.
BTW / Boltzmann Time Warping
Provides a Boltzmann time warping of gene expression time series. BTW is a web server that allows users to upload two tab-separated text files, A and B of gene expression data, each possibly having a different number of time intervals of different durations. It then computes time warping distance between each gene of A with each gene of B, using a symmetric algorithm which additionally computes the Boltzmann partition function and outputs Boltzmann pair probabilities.
NACEP / Network Based Comparison of Temporal Expression Patterns
Compares temporal gene expression patterns. NACEP compares the temporal patterns of a gene between two experimental conditions, taking into consideration all of the possible co-expression modules that this gene may participate in. It utilizes the clustering information to assist the detection of different time-course expression patterns in a soft way. The results can be used to reveal and compare gene expression differences between different conditions.
GAIT / Gene expression Analysis for Interval Time
Analyzes the association of gene expression indices with interval times. GAIT works in three steps: it (i) calculates the joint probability density distribution of the event times based on the multivariate survival analysis, (ii) calculates the conditional expected interval time given the observed censored data for each sample, and (iii) calculates the statistical significances for the association between gene expression indices and the expected interval times using simple linear regression models.
Identifies gene clusters that exhibit distinctly similar or different gene expression patterns among the comparing sample conditions. TimesVector is a triclustering algorithm which is designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. This tool identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters.
DPGP / Dirichlet process Gaussian process mixture model
Measures genomic features such as gene expression levels over time. DPGP is a statistical model for clustering time series data that identifies disjoint clusters of time series gene expression observations using extensive simulations. It assumes that (i) cluster trajectories are stationary, (ii) cluster trajectories are exchangeable, (iii) each gene belongs to only one cluster, (iv) expression levels are sampled at the same time points across all genes and (v) the time point-specific residuals have a Gaussian distribution.
betr / Bayesian Estimation of Temporal Regulation
A package to identify differentially expressed genes in microarray time-course data. BETR explicitly uses the time-dependent structure of the data, employing an empirical Bayes procedure to stabilize estimates derived from the small sample sizes typical in microarray experiments. It is applicable to one- or two-color replicated microarray data, and can be used to detect differences between two conditions or changes from baseline in a single condition. BETR outperforms three commonly used techniques in the analysis of time-course data. This advantage is particularly noticeable for genes with a small but sustained differential expression signal. When the magnitude of differential expression is of similar magnitude to background noise, it is difficult to identify by examining each time point in isolation. These patterns of differential expression become easier to identify when the time series structure of the data is taken into account; a small, noisy signal becomes identifiable if it is sustained across several adjoining time points.
A package for ranking differentially expressed gene expression time courses through Gaussian process regression. gprege fits two GPs with the an RBF (+ noise diagonal) kernel on each profile. One GP kernel is initialised wih a short lengthscale hyperparameter, signal variance as the observed variance and a zero noise variance. It is optimised via scaled conjugate gradients (netlab). A second GP has fixed hyperparameters: zero inverse-width, zero signal variance and noise variance as the observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts as a score of differential expression for the profile. Comparison via ROC curves is performed against BATS.
TMRMR / Temporal Minimum Redundancy-Maximum Relevance
A filter-based feature selection method for temporal gene expression data based on maximum relevance and minimum redundancy criteria. TMRMR incorporates temporal information by combining relevance, which is calculated as an average F-statistic value across different time steps, with redundancy, which is calculated by employing dynamical time warping approach. The incorporation of the temporal information into the feature selection process leads to selection of more discriminative features.
TTCA / Transcript Time Course Analysis
Analyses sparse and heterogeneous time course data with high detection sensitivity and transparency. TTCA is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognize the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response.
Implements a wavelet-based model for analyzing transcriptome data and extends it towards more complex experimental designs. With waveTiling, the user is able to discover group-wise expressed regions, differentially expressed regions between any two groups in single-factor studies and in multifactorial designs. Moreover, for time-course experiments, it is also possible to detect linear time effects and a circadian rhythm of transcripts. By considering the expression values of the individual tiling probes as a function of genomic position, waveTiling allows to dectect effect regions regardless of existing annotation.
NETGEM / Network Embedded Temporal GEnerative Model
Treats the interaction strengths as random variables which are modulated by suitable priors. NETGEM is a tractable model rooted in Markov dynamics, for analyzing the dynamics of the interactions between proteins based on the dynamics of the expression changes of the genes that encode them. It represents an optimal trade-off between model complexity and data requirement. NETGEM was able to deduce actively interacting genes and functional categories from temporal gene expression data. It permits inference by incorporating the information available in perturbed networks.
ReTrOS / Reconstructing Transcription Open Software
Permits to processing and analysing both gene or protein expression time series data sets. ReTrOS is based on a differential equation model to account for transcription and degradation of mRNA molecules and translation and degradation of protein molecules. It permits to obtain high-resolution back-calculated transcriptional profiles and can be combined into computational or analysis pipelines. The tool can extract distributions of transcriptional ‘switching’ activity and degrade rate estimates.
HRNN / Hierarchical Recurrent Neural Network
Identifies time-delayed regulatory interactions of genes. HRNN is a method that surmounts the interpretation difficulties of recurrent neural network (RNN) for application of gene regulatory networks (GRNs) modeling. It facilitates capturing paths with different lengths from leaf nodes in the network to the target node. This model was evaluated on a real biological system and linear/nonlinear synthetic generated data for different sizes of networks and variances of noise.
MAPT / Mapping and Analysis of Pathways through Time
Provides an analytical framework to dissect such datasets and ultimately accelerate knowledge discovery through visualization and data-mining. MAPT is a user-friendly tool for both time-series and single-timepoint dataset analysis, with features such as clustering, visualization and annotation-upload. With PAICE, MAPT provides an easy to use analysis suite to facilitate time series and single time point transcriptomics analysis. In unison, MAPT and PAICE serve as a visual workbench for transcriptomics knowledge discovery, data-mining and functional annotation.
PAICE / Pathway Analysis and Integrated Coloring of Experiments
Utilizes the proven and successful KEGG web-service to map numerical expression onto biochemical pathways. PAICE is a rapid bioinformatics pathway visualization tool for KEGG-compatible accessions derived from Illumina Solexa next-gen and Affymetrix datasets. With MAPT, PAICE provides an easy to use analysis suite to facilitate time series and single time point transcriptomics analysis. In unison, MAPT and PAICE serve as a visual workbench for transcriptomics knowledge discovery, data-mining and functional annotation.
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