CellCognize: a neural network pipeline for cell type classification from flow cytometry data

Readme file content

The files stored here contain the following material as supplementary and source data for the publication

Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data

Birge D. Özel Duygan1, Noushin Hadadi1, Ambrin Farizah Bab1, Markus Seyfried2, Jan R. van der Meer1

1 Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland
2 Biotechnology Department, Firmenich SA, Geneva, Switzerland

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Flow cytometry data
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FCM_files:

.mat files with cleaned data as described in the supplementary methods section

Ecoli_lakewater: raw FCM data (in .csv format) of E. coli cultures and E. coli cultures mixed to lakewater

MIX_experiment_ACL_AJH_PVR: raw FCM data (in .csv format) of the synthetic three culture experiment with E. coli, A. johnsonii and P. veronii, as described in the main text and SI methods.

PHE_OCT_enrichments: raw FCM data (in .csv format) of the phenol and 1-octanol enrichments and the 1-octanol isolates, as described in the main text and SI methods.

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Neural network data
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NN_file_example: three ANN functions, to be used in conjunction with the SI methods section

Supplementary_Methods.docx: Detailed description on the construction, usage and scripts for the ANN. To be used in conjunction with the Flow Cytometry data

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16S sequencing data
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raw fastq- files of the sample reads of the 1-octanol and phenol enrichments described in the paper, at t=0 and t=3d, each in triplicates, forward and reverse.

Readme_16S_sequence_files.txt: sample description of the read files