The US government invests substantial sums to control the HIV/AIDS epidemic. To monitor progress toward epidemic control, PEPFAR, or the President’s Emergency Plan for AIDS Relief, oversees a data reporting system that includes standard indicators, reporting formats, information systems, and data warehouses. These data, reported quarterly, inform understanding of the global epidemic, resource allocation, and identification of trouble spots. PEPFAR has developed tools to assess the quality of data reported. These tools made important contributions but are limited in the methods used to identify anomalous data points. The most advanced consider univariate probability distributions, whereas correlations between indicators suggest a multivariate approach is better suited. For temporal analysis, the same tool compares values to the averages of preceding periods, though does not consider underlying trends and seasonal factors. To that end, we apply two methods to identify anomalous data points among routinely collected facility-level HIV/AIDS data. One approach is Recommender Systems, an unsupervised machine learning method that captures relationships between users and items. We apply the approach in a novel way by predicting reported values, comparing predicted to reported values, and identifying the greatest deviations. For a temporal perspective, we apply time series models that are flexible to include trend and seasonality. Results of these methods were validated against manual review (95% agreement on non-anomalies, 56% agreement on anomalies for recommender systems; 96% agreement on non-anomalies, 91% agreement on anomalies for time series). This tool will apply greater methodological sophistication to monitoring data quality in an accelerated and standardized manner.

Accurately identifying and extracting clusters from atom probe tomography (APT) reconstructions is extremely challenging, yet critical to many applications. Currently, the most prevalent approach to detect clusters is the maximum separation method, a heuristic that relies heavily upon parameters manually chosen by the user. In this work, a new clustering algorithm, Gaussian mixture model Expectation Maximization Algorithm (GEMA), was developed. GEMA utilizes a Gaussian mixture model to probabilistically distinguish clusters from random fluctuations in the matrix. This machine learning approach maximizes the data likelihood via expectation maximization: given atomic positions, the algorithm learns the position, size, and width of each cluster. A key advantage of GEMA is that atoms are probabilistically assigned to clusters, thus reflecting scientifically meaningful uncertainty regarding atoms located near precipitate/matrix interfaces. GEMA outperforms the maximum separation method in cluster detection accuracy when applied to several realistically simulated data sets. Lastly, GEMA was successfully applied to real APT data.

X-ray fluorescence (XRF) microscopy is an important tool for studying trace metals in biology, enabling simultaneous detection of multiple elements of interest and allowing quantification of metals in organelles without the need for subcellular fractionation. Currently, analysis of XRF images is often done using manually defined regions of interest (ROIs). However, since advances in synchrotron instrumentation have enabled the collection of very large data sets encompassing hundreds of cells, manual approaches are becoming increasingly impractical. We describe here the use of soft clustering to identify cell ROIs based on elemental contents, using data collected over a sample of the malaria parasite Plasmodium falciparum as a test case. Soft clustering was able to successfully classify regions in infected erythrocytes as “parasite,” “food vacuole,” “host,” or “background.” In contrast, hard clustering using the k-means algorithm was found to have difficulty in distinguishing cells from background. While initial tests showed convergence on two or three distinct solutions in 60% of the cells studied, subsequent modifications to the clustering routine improved results to yield 100% consistency in image segmentation. Data extracted using soft cluster ROIs were found to be as accurate as data extracted using manually defined ROIs, and analysis time was considerably improved.