Expressionsmatrizen

Expressionsmatrizen are data matrices that store expression measurements for a set of entities across multiple conditions or samples. In biology, they are most commonly used to represent gene expression data: rows correspond to genes, and columns correspond to samples or experimental conditions. The entry in the i-th row and j-th column reflects the expression level of gene i in sample j.

The values in an expressionsmatrize can originate from different measurement technologies. In microarray experiments, entries are often in relative fluorescence units, while in RNA-sequencing experiments they are typically raw counts or normalized units such as counts per million (CPM), transcripts per million (TPM), or fragments per kilobase of transcript per million mapped reads (FPKM/RPKM). Because cross-sample comparisons require comparable scales, data are usually preprocessed: background correction, normalization to remove technical variation, and often log2 transformation (for example log2(x+1)) to stabilize variance.

Typical uses of expressionsmatrizen include differential expression analysis to identify genes with statistically significant changes between conditions, clustering or classification of samples based on expression profiles, and exploratory data analysis such as principal component analysis or heatmaps. They also underpin network inference methods that construct gene co-expression networks, and are used in integrative analyses combining expression data with other omics layers.

Key considerations when working with expressionsmatrizen include handling missing values, correcting batch effects, and choosing appropriate normalization and scaling strategies for downstream analysis. Clear documentation of gene identifiers, sample labels, and the preprocessing steps is essential for reproducibility and interpretation of results.