omada - Machine learning tools for automated transcriptome clustering
analysis
Symptomatic heterogeneity in complex diseases reveals
differences in molecular states that need to be investigated.
However, selecting the numerous parameters of an exploratory
clustering analysis in RNA profiling studies requires deep
understanding of machine learning and extensive computational
experimentation. Tools that assist with such decisions without
prior field knowledge are nonexistent and further gene
association analyses need to be performed independently. We
have developed a suite of tools to automate these processes and
make robust unsupervised clustering of transcriptomic data more
accessible through automated machine learning based functions.
The efficiency of each tool was tested with four datasets
characterised by different expression signal strengths. Our
toolkit’s decisions reflected the real number of stable
partitions in datasets where the subgroups are discernible.
Even in datasets with less clear biological distinctions,
stable subgroups with different expression profiles and
clinical associations were found.