Novel insights enabled by combining mouse muscle datasets from the Rodent Research-1 mission
Biological space experiments are often expensive and difficult to conduct. As such, it is critical to maximize the value of the data that is collected during these experiments. One way to do this is to combine multiple–previously separate–datasets. This can increase the number of replicates for the conditions of interest (and hence statistical power), allow new multi-factor questions to be asked, and potentially highlight new patterns that otherwise would not have been identified from single-dataset studies. However, the process of combining datasets introduces noise due to inherent technical variations between experiments. To better understand the insights that can be gained from multi-dataset analyses and the problems that may arise from joining multiple datasets, several mouse muscle RNA-Seq datasets from the Rodent Research-1 mission were first selected. Then, using the R package DESeq2, principal component analysis (PCA) plots and differentially expressed gene (DEG) lists between ground and flight muscle samples were generated for individual datasets and for different pairwise combinations of datasets. Several new DEGs were identified in the combined datasets, and patterns in the PCA plots were affected depending on which datasets were joined. Understanding the results of this work will be critical for future studies that seek to perform multi-dataset analyses.