Using Clustering Techniques to Improve Hit Selection in High-Throughput Screening

Gagarin, Andrei; Zentilli, Pablo

doi:10.1177/1087057106293590

Cited by 18 publications

(10 citation statements)

References 14 publications

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“…The third method, k-means clustering, has been used previously in the analysis of multivariate biological data. 22,23 All three of these tests performed similarly to the mp-value in correctly identifying negative controls, with the t-test performing worse than the others (Fig. 2A).…”

Section: Resultsmentioning

confidence: 95%

The Multidimensional Perturbation Value: A Single Metric to Measure Similarity and Activity of Treatments in High-Throughput Multidimensional Screens

Hutz

Nelson

et al. 2013

SLAS Discovery

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Screens using high-throughput, information-rich technologies such as microarrays, high-content screening (HCS), and next-generation sequencing (NGS) have become increasingly widespread. Compared with single-readout assays, these methods produce a more comprehensive picture of the effects of screened treatments. However, interpreting such multidimensional readouts is challenging. Univariate statistics such as t-tests and Z-factors cannot easily be applied to multidimensional profiles, leaving no obvious way to answer common screening questions such as “Is treatment X active in this assay?” and “Is treatment X different from (or equivalent to) treatment Y?” We have developed a simple, straightforward metric, the multidimensional perturbation value (mp-value), which can be used to answer these questions. Here, we demonstrate application of the mp-value to three data sets: a multiplexed gene expression screen of compounds and genomic reagents, a microarray-based gene expression screen of compounds, and an HCS compound screen. In all data sets, active treatments were successfully identified using the mp-value, and simulations and follow-up analyses supported the mp-value’s statistical and biological validity. We believe the mp-value represents a promising way to simplify the analysis of multidimensional data while taking full advantage of its richness.

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Section: Resultsmentioning

confidence: 95%

The Multidimensional Perturbation Value: A Single Metric to Measure Similarity and Activity of Treatments in High-Throughput Multidimensional Screens

Hutz

Nelson

et al. 2013

SLAS Discovery

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“…To identify positive hits, we adopted the traditional hit threshold selection of μ-3σ, where μ is the mean value and σ is the SD of the entire assay (20). Analysis of the HTS results revealed eight potential hit compounds that significantly inhibited the KLF5 promoter activity.…”

Section: Resultsmentioning

confidence: 99%

“…The data were validated by two independent variables: signal-to-background (S/B) ratio and Z′ factor. Briefly, S/B ratio is the mean of the signal divided by the mean of the background and Z′ is calculated using the following equation: 1- [(3σ s + 3σ b ) / (μ s − μ b )], where σ is the SD of signal (σ s ) or background (σ b ) and μ is the mean (20, 21). In addition, we calculated the coefficient of variation for the entire sample of the microtiter plates.…”

Section: Methodsmentioning

confidence: 99%

Identification of novel small-molecule compounds that inhibit the proproliferative Krüppel-like factor 5 in colorectal cancer cells by high-throughput screening

Bialkowska

et al. 2009

Molecular Cancer Therapeutics

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Colorectal cancer is one of the leading causes of cancer mortality and morbidity worldwide. Previous studies indicate that the zinc finger-containing transcription factor Krüppel-like factor 5 (KLF5) positively regulates proliferation of intestinal epithelial cells and colorectal cancer cells. Importantly, inhibition of KLF5 expression in intestinal epithelial cells and colorectal cancer cells by pharmacologic or genetic means reduces their rate of proliferation. To identify additional and novel small molecules that inhibit KLF5 expression and thus colorectal cancer proliferation, we developed a reporter assay using colorectal cancer cell line (DLD-1) that stably expressed a luciferase reporter gene directed by 1,959 bp of the human KLF5 promoter upstream of the ATG start codon and performed a cell-based high-throughput screen with the Library of Pharmacologically Active Compounds that contains 1,280 biologically active compounds. The screen identified 8 potential inhibitors and 6 potential activators of the KLF5 promoter. Three potential inhibitors, wortmannin, AG17, and AG879, were further evaluated by secondary analyses. All three significantly reduced both KLF5 promoter-luciferase activity and protein level in DLD-1 cells in a dose-and time-dependent manner when compared with controls. They also significantly reduced the rate of proliferation of DLD-1 and two other colorectal cancer cell lines, HCT116 and HT29. Our results show the principle of using high-throughput screening to identify small-molecule compounds that modulate KLF5 activity and consequently inhibit colorectal cancer proliferation.

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“…Cluster analysis is widely applied in cheminformatics for the analysis of databases of chemical structures [2,3]. Its main use is to find representative subsets from high throughput screening (HTS) [4-6], to design chemical libraries of diverse structures pertinent to pharmaceutical discovery [7-9] and to increase the diversity of these libraries through the selection of additional compounds from other data sets [10,11]. The most popular approach of cluster analysis is hierarchical clustering [12] in which data are merged together based on a tree structure called dendrogram.…”

Section: Introductionmentioning

confidence: 99%

InCHlib – interactive cluster heatmap for web applications

2014

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BackgroundHierarchical clustering is an exploratory data analysis method that reveals the groups (clusters) of similar objects. The result of the hierarchical clustering is a tree structure called dendrogram that shows the arrangement of individual clusters. To investigate the row/column hierarchical cluster structure of a data matrix, a visualization tool called ‘cluster heatmap’ is commonly employed. In the cluster heatmap, the data matrix is displayed as a heatmap, a 2-dimensional array in which the colour of each element corresponds to its value. The rows/columns of the matrix are ordered such that similar rows/columns are near each other. The ordering is given by the dendrogram which is displayed on the side of the heatmap.ResultsWe developed InCHlib (Interactive Cluster Heatmap Library), a highly interactive and lightweight JavaScript library for cluster heatmap visualization and exploration. InCHlib enables the user to select individual or clustered heatmap rows, to zoom in and out of clusters or to flexibly modify heatmap appearance. The cluster heatmap can be augmented with additional metadata displayed in a different colour scale. In addition, to further enhance the visualization, the cluster heatmap can be interconnected with external data sources or analysis tools. Data clustering and the preparation of the input file for InCHlib is facilitated by the Python utility script inchlib_clust.ConclusionsThe cluster heatmap is one of the most popular visualizations of large chemical and biomedical data sets originating, e.g., in high-throughput screening, genomics or transcriptomics experiments. The presented JavaScript library InCHlib is a client-side solution for cluster heatmap exploration. InCHlib can be easily deployed into any modern web application and configured to cooperate with external tools and data sources. Though InCHlib is primarily intended for the analysis of chemical or biological data, it is a versatile tool which application domain is not limited to the life sciences only.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-014-0044-4) contains supplementary material, which is available to authorized users.

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Using Clustering Techniques to Improve Hit Selection in High-Throughput Screening

Cited by 18 publications

References 14 publications

The Multidimensional Perturbation Value: A Single Metric to Measure Similarity and Activity of Treatments in High-Throughput Multidimensional Screens

The Multidimensional Perturbation Value: A Single Metric to Measure Similarity and Activity of Treatments in High-Throughput Multidimensional Screens

Identification of novel small-molecule compounds that inhibit the proproliferative Krüppel-like factor 5 in colorectal cancer cells by high-throughput screening

InCHlib – interactive cluster heatmap for web applications

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