Abstract:This paper examines the performance of commercial scanning white light interferometers in a range of measurement tasks. A step height artefact is used to investigate the response of the instruments at a discontinuity, while gratings with sinusoidal and rectangular profiles are used to investigate the effects of surface gradient and spatial frequency. Results are compared with measurements made with tapping mode atomic force microscopy and discrepancies are discussed with reference to error mechanisms put forward in the published literature. As expected it is found that most instruments report errors when used in regions close to a discontinuity or those with a surface gradient that is large compared to the acceptance angle of the objective lens. Amongst other findings, however, we report systematic errors that are observed when the surface gradient is considerably smaller. Although these errors are typically less than the mean wavelength they are significant compared to the instrument resolution and indicate that current scanning white light interferometers should be used with some caution if sub-wavelength accuracy is required.

Automated flow cytometry (FC) data analysis tools for cell population identification and characterization are increasingly being used in academic, biotechnology, pharmaceutical, and clinical laboratories. The development of these computational methods is designed to overcome reproducibility and process bottleneck issues in manual gating, however, the take‐up of these tools remains (anecdotally) low. Here, we performed a comprehensive literature survey of state‐of‐the‐art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts. Dimensionality reduction methods ranked highly, such as generic t‐distributed stochastic neighbor embedding (t‐SNE) and its initial Matlab‐based implementation for cytometry data viSNE. Software with graphical user interfaces also ranked highly, including PhenoGraph, SPADE1, FlowSOM, and Citrus, with unsupervised learning methods outnumbering supervised learning methods, and algorithm type popularity spread across K‐Means, hierarchical, density‐based, model‐based, and other classes of clustering algorithms. Additionally, to illustrate the actual use typically within clinical spaces alongside frequent citations, a survey issued by UK NEQAS Leucocyte Immunophenotyping to identify software usage trends among clinical laboratories was completed. The survey revealed 53% of laboratories have not yet taken up automated cell population identification methods, though among those that have, Infinicyt software is the most frequently identified. Survey respondents considered data output quality to be the most important factor when using automated FC data analysis software, followed by software speed and level of technical support. This review found differences in software usage between biomedical institutions, with tools for discovery, data exploration, and visualization more popular in academia, whereas automated tools for specialized targeted analysis that apply supervised learning methods were more used in clinical settings.

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