TiSA: TimeSeriesAnalysis-a pipeline for the analysis of longitudinal transcriptomics data.

Area of Science:

• Genomics and Bioinformatics
• Molecular Biology

Background:

• Advancements in transcriptomic sequencing enable longitudinal studies, generating vast datasets.
• Existing methods lack comprehensive approaches for analyzing longitudinal transcriptomic experiments.

Purpose of the Study:

• To introduce the Time Series Analysis (TiSA) pipeline for analyzing longitudinal transcriptomic data.
• To provide a unified framework combining differential gene expression, clustering, and functional enrichment.

Main Methods:

• TiSA performs differential gene expression analysis along temporal and conditional axes.
• Clustering of differentially expressed genes is followed by functional enrichment analysis for each cluster.
• The pipeline supports analysis of both microarray and RNA-seq data, accommodating various dataset sizes and missing data points.

Main Results:

• TiSA successfully analyzed diverse longitudinal transcriptomic datasets, including cell lines and COVID-19 patient data.
• The pipeline demonstrated robustness across different data complexities and formats.
• Included custom visualizations aid biological interpretation, such as PCA, MDS plots, and heatmaps.

Conclusions:

• TiSA offers the first dedicated and comprehensive solution for longitudinal transcriptomic data analysis.
• The pipeline facilitates easier biological interpretation of complex temporal gene expression patterns.
• TiSA is a valuable tool for researchers working with time-series gene expression data.