#Cellprofiler vs cellprofiler analyst manual#

surface method (RSM) experiments involving MICP technique. This study applied the techniques in two existing response. In this article, the optimization of the specific urease activity (SUA) and the calcium carbonate (CaCO3) using microbially induced calcite precipitation (MICP) was compared to optimization using three algorithms based on machine learning: random forest regressor, artificial neural networks (ANNs), and multivariate linear regression. Bioinformatics (Oxford, England) 10.1093/bioinformatics/btab634 (2021). CellProfiler Analyst 3.0: Accessible data exploration and machine learning for image analysis. CellProfiler 4: improvements in speed, utility and usability. Counting by different individuals causes the largest variance.ġ. Our findings suggest that the machine learning algorithm-based evaluation leads to more replicable results than individual counting. We observed that cell counting variance ranged from 0.01 % to 1.57 % for repeated automated counting, from 0 % to 24.81 % for repeated counting by one individual, and from 2.69 % to 248.68 % for three counting individuals.

#Cellprofiler vs cellprofiler analyst manual#

Manual cell counting was performed three times by the same individual and once by three different individuals. Automated counting was performed three times by a random forest classifier created with CellProfiler 4.2.1 and CellProfiler Analyst 3.0.4. Images of primary mixed neural cell cultures –derived from postnatal rats and containing fluorescently labelled nuclei, neurons, and astrocytes– were quantified. The aim of our investigation is to analyze the variance of cell counting results by comparing cell counts obtained from different individuals and a machine learning based algorithm. Replicability of studies quantifying cell numbers in images may suffer from biases and uncertainties introduced by cell identification. Scientific studies need to be reproducible to yield reliable results.