Activated T-Cells

CD4+ T cells or T helper cells are a type of lymphocytes that play an important role in the immune system, particularly in the adaptive immune system. Amongst other functions, they mediate activity of other immune cells by releasing cytokines. Here, we analyze time-resolved gene expression data of human CD4+ cells that were in vitro activated (GSE136625). In particular, the data set contains 13 expression profiles that were measured after initial T cell activation at 2h intervals over a 24-hour period.


The data set contains gene expression microarrays of CD4+ T cells from the blood of two human donors. The isolated T cells were in vitro activated and expression profiles were created at 2h intervals from 0h-24h. For both donors and each time point 3 replicates were created. For our analysis, we only consider the gene expression profiles of donor 1. For each time point, we used the median value to aggregate all replicates. We used the time series data to study which biological pathways are affected by the T cell activation and at which point in time.

Data set

The input for the time series workflow is a gene expression matrix, where columns represent measurements for the investigated time points.

The processed gene expression matrix for Donor 1 can be found here.
Row names represent Official Gene Symbols for all measures protein coding genes.
Column names (T0, T2, ..., T24) represent the 13 measured time points.

Technical Background

We conduct the first clustering step using a strong threshold in order to find clusters with high similarity. In the second clustering, where we aim to identify super-clusters, we use a less strict threshold. Furthermore, we do not want to consider height differences of the curves. Therefore, we use the Euclidean distance for gradients as distance measure.



  • Difference between minimal and maximal time point: 2.0

Clustering step 1

  • Distance measure: Euclidean distance for gradients
  • Linkage method: Complete Linkage
  • Threshold for cluster: 0.8
  • Minimum number of genes for each cluster 1

Clustering step 2

  • Distance measure: Euclidean distance for gradients
  • Linkage method: Complete Linkage
  • Threshold for cluster: 0.95


  • P-value strategy: Upper tailed
  • P-value adjustment method: Benjamini-Hochberg
  • Significance level: 0.05

Step-by-step slideshow

The following slideshow depicts the different analysis steps of the GeneTrail3 time series workflow.


In the following, a few results of our conducted analysis are shown. In particular, we present the enrichment results of super-cluster SC1. Associated enrichment results show an enrichment of categories related to an early immune response.


T cell activation


T cell differentiation


T cell activation hallmarks

Cytokine signaling


Metabolic processes