Vebjorn Ljosa

APLA: Indexing Arbitrary Probability Distributions

The ability to store and query uncertain information is of great benefit to databases that infer values from a set of observations, including databases of moving objects, sensor readings, historical business transactions, and biomedical images. These observations are often inexact to begin with, and even if they are exact, a set of observations of an attribute of an object is better represented by a probability distribution than by a single number, such as a mean. In this paper, we present adaptive, piecewise-linear approximations (APLAs), which represent arbitrary probability distributions compactly with guaranteed quality. We also present the APLA-tree, an index structure for APLAs. Because APLA is more precise than existing approximation techniques, the APLA-tree can answer probabilistic range queries twice as fast. APLA generalizes to multiple dimensions, and the APLA-tree can index multivariate distributions using either one-dimensional or multidimensional APLAs. Finally, we propose a new definition of k-NN queries on uncertain data. The new definition allows APLA and the APLA-tree to answer k-NN queries quickly, even on arbitrary probability distributions. No efficient k-NN search was previously possible on such distributions.

Annotated high-throughput microscopy image sets for validation

as a resource for testing and validating automated image-analysis algorithms. The BBBC is particularly useful for high-throughput experiments and for providing biological ground truth for evaluating image-analysis algorithms. If an algorithm is sufficiently robust across samples to handle high-throughput experiments, lowthoughput applications also benefit because tolerance to variability in sample preparation and imaging makes the algorithm more likely to generalize to new image sets. Each image set in the BBBC is accompanied by a brief description of its motivating biological application and a set of groundtruth data against which algorithms can be evaluated. The ground truth sets can consist of cell or nucleus counts, foreground and background pixels, outlines of individual objects, or biological labels based on treatment conditions or orthogonal assays (such as a dose-response curve or positiveand negative-control images). We describe canonical ways to measure an algorithm’s performance so that algorithms can be compared against each other fairly, and we provide an optional framework to do so conveniently within CellProfiler. For each image set, we list any published results of which we are aware. The BBBC is freely available from http://www.broadinstitute. org/bbbc/. The collection currently contains 18 image sets, including images of cells (Homo sapiens and Drosophila melanogaster) as well as of whole organisms (Caenorhabditis elegans) assayed in high throughput. We are continuing to extend the collection during the course of our research, and we encourage the submission of additional image sets, ground truth and published results of algorithms.

An image analysis toolbox for high-throughput C. elegans assays

We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available through the open-source CellProfiler project and enables objective scoring of whole-worm high-throughput image-based assays of C. elegans for the study of diverse biological pathways that are relevant to human disease.

Top-k Spatial Joins of Probabilistic Objects

Probabilistic data have recently become popular in applications such as scientific and geospatial databases. For images and other spatial datasets, probabilistic values can capture the uncertainty in extent and class of the objects in the images. Relating one such dataset to another by spatial joins is an important operation for data management systems. We consider probabilistic spatial join (PSJ) queries, which rank the results according to a score that incorporates both the uncertainties associated with the objects and the distances between them. We present algorithms for two kinds of PSJ queries: Threshold PSJ queries, which return all pairs that score above a given threshold, and top-k PSJ queries, which return the k top-scoring pairs. For threshold PSJ queries, we propose a plane sweep algorithm that, because it exploits the special structure of the problem, runs in 0(n (log n + k)) time, where n is the number of points and k is the number of results. We extend the algorithms to 2-D data and to top-k PSJ queries. To further speed up top-k PSJ queries, we develop a scheduling technique that estimates the scores at the level of blocks, then hands the blocks to the plane sweep algorithm. By finding high-scoring pairs early, the scheduling allows a large portion of the datasets to be pruned. Experiments demonstrate speed-ups of two orders of magnitude.

Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling

Significance A large compound screening collection is usually constructed to be tested in many distinct assays, each one designed to find modulators of a different biological process. However, it is generally not known to what extent a compound collection actually contains molecules with distinct biological effects (or even any effect) until it has been tested for a couple of years. This study explores a cost-effective way of rapidly assessing the biological performance diversity of a screening collection in a single assay. By simultaneously measuring a large number of cellular features, unbiased profiling assays can distinguish compound effects with high resolution and thus measure performance diversity. We show that this approach could be used as a filtering strategy to build effective screening collections. High-throughput screening has become a mainstay of small-molecule probe and early drug discovery. The question of how to build and evolve efficient screening collections systematically for cell-based and biochemical screening is still unresolved. It is often assumed that chemical structure diversity leads to diverse biological performance of a library. Here, we confirm earlier results showing that this inference is not always valid and suggest instead using biological measurement diversity derived from multiplexed profiling in the construction of libraries with diverse assay performance patterns for cell-based screens. Rather than using results from tens or hundreds of completed assays, which is resource intensive and not easily extensible, we use high-dimensional image-based cell morphology and gene expression profiles. We piloted this approach using over 30,000 compounds. We show that small-molecule profiling can be used to select compound sets with high rates of activity and diverse biological performance.

Introduction to the Quantitative Analysis of Two-Dimensional Fluorescence Microscopy Images for Cell-Based Screening

Modern automated microscopes collect digital images at an astonishing pace. Automated image analysis can measure biological phenotypes quantitatively and reliably, and has therefore become a powerful tool for probing a wide variety of biological questions using microscopy. In this tutorial, we acquaint biologists with this important computational field and introduce some basic principles of image analysis, using typical strategies for two-dimensional images of cultured cells in high-throughput screens as the primary example.

Multiplex Cytological Profiling Assay to Measure Diverse Cellular States

Computational methods for image-based profiling are under active development, but their success hinges on assays that can capture a wide range of phenotypes. We have developed a multiplex cytological profiling assay that “paints the cell” with as many fluorescent markers as possible without compromising our ability to extract rich, quantitative profiles in high throughput. The assay detects seven major cellular components. In a pilot screen of bioactive compounds, the assay detected a range of cellular phenotypes and it clustered compounds with similar annotated protein targets or chemical structure based on cytological profiles. The results demonstrate that the assay captures subtle patterns in the combination of morphological labels, thereby detecting the effects of chemical compounds even though their targets are not stained directly. This image-based assay provides an unbiased approach to characterize compound- and disease-associated cell states to support future probe discovery.

Comparison of Methods for Image-Based Profiling of Cellular Morphological Responses to Small-Molecule Treatment

Quantitative microscopy has proven a versatile and powerful phenotypic screening technique. Recently, image-based profiling has shown promise as a means for broadly characterizing molecules’ effects on cells in several drug-discovery applications, including target-agnostic screening and predicting a compound’s mechanism of action (MOA). Several profiling methods have been proposed, but little is known about their comparative performance, impeding the wider adoption and further development of image-based profiling. We compared these methods by applying them to a widely applicable assay of cultured cells and measuring the ability of each method to predict the MOA of a compendium of drugs. A very simple method that is based on population means performed as well as methods designed to take advantage of the measurements of individual cells. This is surprising because many treatments induced a heterogeneous phenotypic response across the cell population in each sample. Another simple method, which performs factor analysis on the cellular measurements before averaging them, provided substantial improvement and was able to predict MOA correctly for 94% of the treatments in our ground-truth set. To facilitate the ready application and future development of image-based phenotypic profiling methods, we provide our complete ground-truth and test data sets, as well as open-source implementations of the various methods in a common software framework.

Indexing spatially sensitive distance measures using multi-resolution lower bounds

Comparison of images requires a distance metric that is sensitive to the spatial location of objects and features. Such sensitive distance measures can, however, be computationally infeasible due to the high dimensionality of feature spaces coupled with the need to model the spatial structure of the images. We present a novel multi-resolution approach to indexing spatially sensitive distance measures. We derive practical lower bounds for the earth mover’s distance (EMD). Multiple levels of lower bounds, one for each resolution of the index structure, are incorporated into algorithms for answering range queries and k-NN queries, both by sequential scan and using an M-tree index structure. Experiments show that using the lower bounds reduces the running time of similarity queries by a factor of up to 36 compared to a sequential scan without lower bounds. Computing separately for each dimension of the feature vector yields a speedup of ~14. By combining the two techniques, similarity queries can be answered more than 500 times faster.

CellProfiler Analyst: interactive data exploration, analysis, and classification of large biological image sets

Abstract Summary: CellProfiler Analyst allows the exploration and visualization of image-based data, together with the classification of complex biological phenotypes, via an interactive user interface designed for biologists and data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery). Availability and Implementation: CellProfiler Analyst 2.0 is free and open source, available at http://www.cellprofiler.org and from GitHub (https://github.com/CellProfiler/CellProfiler-Analyst) under the BSD license. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. We implemented an automatic build process that supports nightly updates and regular release cycles for the software. Contact: anne@broadinstitute.org Supplementary information: Supplementary data are available at Bioinformatics online.