scholarly journals An open-source solution for advanced imaging flow cytometry data analysis using machine learning

Methods ◽  
2017 ◽  
Vol 112 ◽  
pp. 201-210 ◽  
Author(s):  
Holger Hennig ◽  
Paul Rees ◽  
Thomas Blasi ◽  
Lee Kamentsky ◽  
Jane Hung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Data Analysis ◽  
Open Source ◽  
Advanced Imaging ◽  
Flow Cytometry Data ◽  
Imaging Flow Cytometry

scholarly journals OpenCyto: An Open Source Infrastructure for Scalable, Robust, Reproducible, and Automated, End-to-End Flow Cytometry Data Analysis

2014 ◽  
Vol 10 (8) ◽  
pp. e1003806 ◽  
Author(s):  
Greg Finak ◽  
Jacob Frelinger ◽  
Wenxin Jiang ◽  
Evan W. Newell ◽  
John Ramey ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Data Analysis ◽  
Open Source ◽  
Flow Cytometry Data ◽  
End To End

scholarly journals Identification of Stem Cells from Large Cell Populations with Topological Scoring

2020 ◽  
Author(s):  
Mihaela E. Sardiu ◽  
Box C. Andrew ◽  
Jeff Haug ◽  
Michael P. Washburn
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Data Analysis ◽  
Large Scale ◽  
Topological Analysis ◽  
Topological Data Analysis ◽  
Cell Populations ◽  
Hematopoietic Stem ◽  
Flow Cytometry Data ◽  
Genomics And Proteomics

AbstractMachine learning and topological analysis methods are becoming increasingly used on various large-scale omics datasets. Modern high dimensional flow cytometry data sets share many features with other omics datasets like genomics and proteomics. For example, genomics or proteomics datasets can be sparse and have high dimensionality, and flow cytometry datasets can also share these features. This makes flow cytometry data potentially a suitable candidate for employing machine learning and topological scoring strategies, for example, to gain novel insights into patterns within the data. We have previously developed the Topological Score (TopS) and implemented it for the analysis of quantitative protein interaction network datasets. Here we show that the TopS approach for large scale data analysis is applicable to the analysis of a previously described flow cytometry sorted human hematopoietic stem cell dataset. We demonstrate that TopS is capable of effectively sorting this dataset into cell populations and identify rare cell populations. We demonstrate the utility of TopS when coupled with multiple approaches including topological data analysis, X-shift clustering, and t-Distributed Stochastic Neighbor Embedding (t-SNE). Our results suggest that TopS could be effectively used to analyze large scale flow cytometry datasets to find rare cell populations.

scholarly journals Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yersultan Mirasbekov ◽  
Adina Zhumakhanova ◽  
Almira Zhantuyakova ◽  
Kuanysh Sarkytbayev ◽  
Dmitry V. Malashenkov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Spatial Resolution ◽  
Mesocosm Experiment ◽  
Imaging Flow Cytometry ◽  
Leibler Divergence ◽  
Temporal And Spatial ◽  
High Level ◽  
Training Sets

AbstractA machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray–Curtis dissimilarity, and Kullback–Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96–100%) but relatively low intrageneric accuracy (67–78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.

scholarly journals FlowCal: A User-Friendly, Open Source Software Tool for Automatically Converting Flow Cytometry Data from Arbitrary to Calibrated Units

2016 ◽  
Vol 5 (7) ◽  
pp. 774-780 ◽  
Author(s):  
Sebastian M. Castillo-Hair ◽  
John T. Sexton ◽  
Brian P. Landry ◽  
Evan J. Olson ◽  
Oleg A. Igoshin ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Open Source ◽  
Open Source Software ◽  
Software Tool ◽  
Flow Cytometry Data ◽  
User Friendly
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