--- title: "Time Course Analysis" canonical: "https://help.biobam.com/space/OED0324/4866637826/Time+Course+Analysis" format: markdown --- > Macro (toc) ## Introduction This tutorial provides a step-by-step guide to performing a [time course differential expression analysis](https://docs.omicsbox.biobam.com/latest/Time-Course-Expression-Analysis/) and subsequent functional characterization within OmicsBox. The workflow utilizes the maSigPro algorithm, which is specifically designed to identify genes with significant expression changes over time in complex experimental designs that involve multiple conditions or experimental factors. Please cite maSigPro as: > Conesa A, Nueda MJ, Ferrer A, Talón M. maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics. 2006 May 1;22(9):1096-102. doi:10.1093/bioinformatics/btl056 > > Nueda MJ, Tarazona S, Conesa A. Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics. 2014;30(18):2598-2602. doi:10.1093/bioinformatics/btu333 ### Dataset The dataset used in this tutorial is derived from an RNA-Seq experiment on *Arabidopsis thaliana* analyzing plant-pathogen interactions. The experimental design is as follows: - Plant Lines: 2 lines, one Resistant and one Susceptible to the pathogen *Bgh*. - Pathogen Isolates: 2 different isolates used for inoculation: A6 and K1. - Time Points: 4 time points after inoculation: 6h, 12h, 18h, and 24h. - Replicates: 3 biological replicates for each combination of plant line, isolate, and time point. - Total Samples: 48 samples (2 lines × 2 isolates × 4 time points × 3 replicates). > Data Source: This dataset was originally published by [Maekawa T, et al. (2012)](https://doi.org/10.1073/pnas.1218059109) in the study *"Conservation of NLR-triggered immunity across plant lineages"*. The raw data is publicly available in the NCBI GEO Database under accession number [GSE39463](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39463). ![image-20260525-080845.png](media://36241033-80e4-49d2-a410-0ef5ba7c829f) ## Bioinformatic Analysis The analysis pipeline starts from raw sequencing reads, which first need to be cleaned and then quantified to obtain an estimate of the gene or transcript expression for each sample. These first steps are covered in the [Gene-level](https://help.biobam.com/space/OED0324/3525083201/Gene-Level+Analysis) and [Transcript-level](https://help.biobam.com/space/OED0324/3525083289/Transcript-Level+Analysis) Analysis Example Datasets, so this tutorial will start directly from a count table. Then, the time course analysis identifies groups of genes that have a similar behavior over time, as well as genes whose behavior is different in response to an experimental factor. Finally, the functional enrichment and pathway analysis provide biological meaning to those groups of genes. ### 1. Time Course In order to run the timecourse analysis, go to transcriptomics> Differential Expression Analysis > [Time Course Expression Analysis](https://docs.omicsbox.biobam.com/latest/Time-Course-Expression-Analysis/). #### Input - [Count Table](https://drive.google.com/file/d/16vbDCzlavrTvfYKyfegKcBIAxVxOLg9N/view?usp=sharing): raw gene expression quantification values across all 48 samples. - [Experimental Design](https://drive.google.com/file/d/1GQE339UIk5W3bchgAMk8IXmFKqxePlnP/view?usp=sharing): structured text file that maps each sample to its corresponding time point, replicate group, and experimental conditions (plant line and pathogen isolate) #### Parameters - Count Table File: select here the Count Table file - CPM Filter: 2 - Samples reaching CPM Filter: 10 - Experimental Design File: select here the Experimental Design file. - Design Type: Multiple Series Time Course - Experimental Factor: MLA1 - Control Condition: susceptible All other parameters are left at their default values. #### Output - **[Time Course Results](https://drive.google.com/file/d/1sT9p8cH_lJIPV1Lii2byVQh7Wzaw8Mjp/view?usp=drive_link)**. This is the primary output of the analysis. This table contains all the genes that passed the initial filtering step. To help interpret the data, the table includes a **Tag** column that labels genes with meaningful behavior. The genes identified as differentially expressed are grouped into a specific number of clusters based on the “Number of Clusters” parameter. Genes that share a similar expression profile over time are assigned to the same cluster and labeled with a corresponding cluster tag. Furthermore, an additional condition tag is applied to genes that show a clear difference in expression behavior over time between the different experimental conditions. This categorization allows for a straightforward identification of genes with similar trends or condition-specific responses. The Side Panel shows the available charts and downstream analyses. The **multidimensional scaling (MDS) plots** display distances between samples based on gene expression differences, serving as a tool to identify the primary sources of experimental variation. When inspecting the MDS plots colored by different experimental variables, it becomes evident that time after inoculation is the main driver of sample separation, as samples group together clearly according to their respective time points. The pathogen isolate also shows an impact on gene expression, demonstrated by the internal separation within individual time clusters. Conversely, the plant line exhibits minimal influence on the global expression profile because samples from the resistant and susceptible lines appear intermixed within the clusters. ![image-20260525-133357.png](media://736b6b46-546c-4328-b49c-274e206ecb92) To capture these expression dynamics, the differentially expressed genes were partitioned into a total of nine clusters. The correctness and suitability of this cluster configuration can be validated using the experiment-wide and summary expression profile plots. In the **Experiment-wide Expression Profile plots**, each of the nine clusters demonstrates a distinct, well-defined expression pattern across the samples rather than showing fuzzy, poorly defined, or highly redundant trends, which confirms that the nine clusters are an appropriate fit for this dataset. This correctness is further supported by the **Summary Expression Profiles**, which display clear and interpretable biological trends. For instance, these plots reveal distinct behaviors such as a continuous increase in gene expression over time in cluster 3, a steady decrease in cluster 2, and a late, condition-specific upregulation in the susceptible lines in cluster 8. ![experiment_wide_profiles.png](media://0d878cd4-8b4e-4fd0-9293-09f3a8af27fd) ![summary_expression_profiles.png](media://0934e928-a2a4-491b-916e-416fcf1a6c15) ### 2. Functional Enrichment Functional enrichment analysis identifies which biological functions are statistically overrepresented in a specific group of genes compared to a reference background. This step helps translate a list of genes grouped in the same cluster into meaningful biological processes, molecular functions, or cellular components. In OmicsBox, this analysis is performed using a Fisher's Exact Test, which can be launched from the object’s Side Panel > Actions > [Fisher’s Exact Test](https://docs.omicsbox.biobam.com/latest/Time-Course-Expression-Analysis/#fishers-exact-test). #### Input - [Time Course Results](https://drive.google.com/file/d/1sT9p8cH_lJIPV1Lii2byVQh7Wzaw8Mjp/view?usp=drive_link). Generated in the previous step. - [Functional Annotation](https://drive.google.com/file/d/1ljW88J-soiloh7BEpZcXBStAM3jqT7UL/view?usp=sharing). This is a file containing all the genes or transcripts along with their annotated biological functions, such as Gene Ontology (GO) and Enzyme Commission (EC) terms. In this tutorial, the annotation file was obtained using the *[Load Data from BioMart](https://docs.omicsbox.biobam.com/latest/Load-Data/#load-data-from-biomart)* tool, though it can also be generated from scratch for non-model organisms by following the *[Functional Annotation](https://help.biobam.com/space/OED0324/3525084067/Functional+Annotation)*[ tutorial](https://help.biobam.com/space/OED0324/3525084067/Functional+Annotation). #### Parameters - Groups: Cluster-3 - Reference Annotation: provide the Functional Annotation file. #### Output - [Fisher’s Exact Test Results](https://drive.google.com/file/d/1K8UOmngswlWFjZOErtYzJstV6_88rsXw/view?usp=sharing). Table listing lists all the functional terms tested, with a Tag specifying whether they have been statistically over- or under-represented in the analyzed cluster. For Cluster 3, the functional enrichment analysis highlights a significant overrepresentation of terms related to the response to stress, photosynthesis, respiration, and defense mechanisms. The upregulation of photosynthesis and respiration indicates that the plant experiences an increased demand for energy and metabolic activity to fuel its defense responses against the pathogen infection. To help interpret these results, the [Bubble Plot chart](https://docs.omicsbox.biobam.com/latest/Fisher-Exact-Test/#charts) can display the over-represented GO terms, with color indicating the statistical significance, the size representing the number of sequences in the cluster found in that GO term, and the x-axis showing the gene ratio. ![image-20260525-160925.png](media://30b795ba-c8c4-4fa6-8706-71c86171be06)