CellCognize: Flow Cytometry and 16S Data for Microbiota Classification

Flow cytometry and 16S rRNA sequencing data support the development of machine-learned classification for microbiota cell type diversity. The dataset includes raw and cleaned measurements from synthetic microbial communities, E. coli cultures, and chemical enrichment experiments. It is supplemented by pre-trained artificial neural network functions and detailed methodological documentation.

Use Cases

• Training neural networks for cell type classification based on flow cytometry scatter and fluorescence measurements.
• Benchmarking microbial community analysis methods using paired flow cytometry and 16S sequencing data from synthetic mixtures.
• Studying the response of microbial communities to chemical stressors like phenol and 1-octanol.
• Developing data cleaning and preprocessing pipelines for flow cytometry data in .mat and .csv formats.

Strengths

• Includes paired multimodal data: flow cytometry measurements and 16S rRNA gene sequencing reads for the same enrichment samples.
• Provides pre-trained neural network functions and a detailed supplementary methods document for reproducibility.
• Contains data from controlled synthetic communities (E. coli, A. johnsonii, P. veronii) enabling method validation.

Limitations

• Key metadata such as row count, column names, dataset size, and specific update dates are not provided in the available sources.
• The flow cytometry data is split across multiple file types (.mat, .csv) and experimental subsets, requiring integration for full analysis.

Provenance

Source

Department of Fundamental Microbiology, University of Lausanne, and Firmenich SA, Geneva, Switzerland.

Collection Method

Data was generated as supplementary material for a research publication, involving laboratory flow cytometry experiments, microbial culturing, and DNA sequencing.

Geography

Lausanne and Geneva, Switzerland (laboratory origin).