PhenoGMM: Gaussian mixture modelling of microbial cytometry data enables efficient predictions of biodiversity

Abstract

Motivation:Microbial flow cytometry allows to rapidly characterize microbial community diversity and dynamics. Recent research has demonstrated a strong connection between the cytometric diversity and taxonomic diversity based on 16S rRNA gene amplicon sequencing data. This creates the opportunity to integrate both types of data to study and predict the microbial community diversity in an automated and efficient way. However, microbial flow cytometry data results in a number of unique challenges that need to be addressed. Results: The results of our work are threefold: i) We expand current microbial cytometry fingerprinting approaches by using a model-based fingerprinting approach based upon Gaussian Mixture Models, which we called PhenoGMM. ii) We show that microbial diversity can be rapidly estimated by PhenoGMM. In combination with a supervised machine learning model, diversity estimations based on 16S rRNA gene amplicon sequencing data can be predicted. iii) We evaluate our method extensively by using multiple datasets from different ecosystems and compare its predictive power with a generic binning fingerprinting approach that is commonly used in microbial flow cytometry. These results confirm the strong connection between the genetic make-up of a microbial community and its phenotypic properties as measured by flow cytometry.

Cytometric fingerprints of gut microbiota predict Crohn’s disease state

“…Additional automated denoising was performed using the FlowAI package (v1.4.4., target channel: FL1, changepoint detection: 150) (19). Cytometric fingerprinting Cytometric fingerprints were determined using PhenoGMM (13). In brief, all samples were first subsampled to the same number of cell counts (288 per sample).…”

“…In this work we set forth to demonstrate that these differences are reflected in the cytometry data as well, and in addition, compare the predictive power of both technologies in a straightforward way. We used PhenoGMM, which is an adaptive cytometric fingerprinting strategy based on Gaussian Mixture Models, to cluster individual cells in operational groups (13). This results in a contingency table, which stores cell counts per mixture and sample.…”

Cytometric fingerprints of gut microbiota predict Crohn’s disease state

“…In this work, we set forth to demonstrate that these differences are reflected in the cytometry data as well, and in addition, compare the predictive power of both technologies in a straightforward way. We used PhenoGMM, an adaptive cytometric fingerprinting strategy based on Gaussian mixture models, to cluster individual cells in operational groups [16]. This results in a relative cell count contingency table that describes samples by groups of phenotypically similar cells instead of grouped sequences.…”