MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data

Abstract

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of ten heterogeneous cytometry studies totaling 2,926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between demographic groups. Software is released to the public through Bioconductor (http://bioconductor.org/packages/release/bioc/html/MetaCyto.html).

Comprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy

“…Implementation of such standardized experimental workflows and antibody panels have been 26 proposed for flow cytometry (Finak et al, 2016;Maecker et al, 2012) but have not yet been implemented for analogous studies using mass cytometry. In addition to assessing a broad and defined set of immune cell populations, our proposed workflow will allow valuable cross-trial comparisons and would simplify and enhance meta-analyses as recently proposed (Hu et al, 2018).…”

A robust and interpretable, end-to-end deep learning model for cytometry data

“…We notice that the decision tree bi-sects the markers into positive and negative regions in a way that is consistent with manual gating. We previously have developed a computational tool named MetaCyto that can identify cell subsets based on their definitions 9 . We used MetaCyto to identify the CD8+ CD3+ CD27-CD94+ population across all 9 studies and found that the population is consistently increased in all studies ( Fig.…”

Flow cytometry data analysis: Recent tools and algorithms

“…MetaCyto is a tool designed to identify common cell populations across different studies (one study includes a specific set of samples). It then estimates the influence of external factors, such as age or ethnicity, using hierarchical models, based on the information received by all the studies …”

“…It then estimates the influence of external factors, such as age or ethnicity, using hierarchical models, based on the information received by all the studies. 35 CytoCompare is an R package that is able to compare the phenotypes of cell clusters identified by automatic gating algorithms. Its aim is to facilitate the easy identification of similar and different cell clusters, using the density distribution of the cell marker expression.…”