MOTIVATION: Understanding the dynamics of gene expression across different cellular states is crucial for discerning the mechanisms underneath cellular differentiation. Genes that exhibit variation in mean expression as a function of Pseudotime and between branching trajectories are expected to govern cell fate decisions. We introduce scMaSigPro, a method for the identification of differential gene expression patterns along Pseudotime and branching paths simultaneously. RESULTS: We assessed the performance of scMaSigPro using synthetic and public datasets. Our evaluation shows that scMaSigPro outperforms existing methods in controlling the False Positive Rate and is computationally efficient. AVAILABILITY AND IMPLEMENTATION: scMaSigPro is available as a free R package (version 4.0 or higher) under the GPL(≥2) license on GitHub at 'github.com/BioBam/scMaSigPro' and archived with version 0.03 on Zenodo at 'zenodo.org/records/12568922'.

MOTIVATION: Batch effects in omics datasets are usually a source of technical noise that masks the biological signal and hampers data analysis. Batch effect removal has been widely addressed for individual omics technologies. However, multi-omic datasets may combine data obtained in different batches where omics type and batch are often confounded. Moreover, systematic biases may be introduced without notice during data acquisition, which creates a hidden batch effect. Current methods fail to address batch effect correction in these cases. RESULTS: In this article, we introduce the MultiBaC R package, a tool for batch effect removal in multi-omics and hidden batch effect scenarios. The package includes a diversity of graphical outputs for model validation and assessment of the batch effect correction. AVAILABILITY AND IMPLEMENTATION: MultiBaC package is available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/MultiBaC.html) and GitHub (https://github.com/ConesaLab/MultiBaC.git). The data underlying this article are available in Gene Expression Omnibus repository (accession numbers GSE11521, GSE1002, GSE56622 and GSE43747). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: As sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data. Results: Iso-maSigPro is a new functionality of the R package maSigPro for transcriptomics time series data analysis. Iso-maSigPro identifies genes with a differential isoform usage across time. The package also includes new clustering and visualization functions that allow grouping of genes with similar expression patterns at the isoform level, as well as those genes with a shift in major expressed isoform. Availability and implementation: The package is freely available under the LGPL license from the Bioconductor web site. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: The widespread adoption of RNA-seq to quantitatively measure gene expression has increased the scope of sequencing experimental designs to include time-course experiments. maSigPro is an R package specifically suited for the analysis of time-course gene expression data, which was developed originally for microarrays and hence was limited in its application to count data. RESULTS: We have updated maSigPro to support RNA-seq time series analysis by introducing generalized linear models in the algorithm to support the modeling of count data while maintaining the traditional functionalities of the package. We show a good performance of the maSigPro-GLM method in several simulated time-course scenarios and in a real experimental dataset. AVAILABILITY AND IMPLEMENTATION: The package is freely available under the LGPL license from the Bioconductor Web site (http://bioconductor.org).

MOTIVATION: Time-course microarray experiments study the progress of gene expression along time across one or several experimental conditions. Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information. The assessment of the functional aspects of time-course transcriptomics data requires the use of approaches that exploit the activation dynamics of the functional categories to where genes are annotated. METHODS: We present three novel methodologies for the functional assessment of time-course microarray data. i) maSigFun derives from the maSigPro method, a regression-based strategy to model time-dependent expression patterns and identify genes with differences across series. maSigFun fits a regression model for groups of genes labeled by a functional class and selects those categories which have a significant model. ii) PCA-maSigFun fits a PCA model of each functional class-defined expression matrix to extract orthogonal patterns of expression change, which are then assessed for their fit to a time-dependent regression model. iii) ASCA-functional uses the ASCA model to rank genes according to their correlation to principal time expression patterns and assess functional enrichment on a GSA fashion. We used simulated and experimental datasets to study these novel approaches. Results were compared to alternative methodologies. RESULTS: Synthetic and experimental data showed that the different methods are able to capture different aspects of the relationship between genes, functions and co-expression that are biologically meaningful. The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.

MOTIVATION: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. RESULTS: In this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. AVAILABILITY: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. RESULTS: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.