HC StratoMineR: A Web-Based Tool for the Rapid Analysis of High-Content Datasets

Abstract

High-content screening (HCS) can generate large multidimensional datasets and when aligned with the appropriate data mining tools, it can yield valuable insights into the mechanism of action of bioactive molecules. However, easy-to-use data mining tools are not widely available, with the result that these datasets are frequently underutilized. Here, we present HC StratoMineR, a web-based tool for high-content data analysis. It is a decision-supportive platform that guides even non-expert users through a high-content data analysis workflow. HC StratoMineR is built by using My Structured Query Language for storage and querying, PHP: Hypertext Preprocessor as the main programming language, and jQuery for additional user interface functionality. R is used for statistical calculations, logic and data visualizations. Furthermore, C++ and graphical processor unit power is diffusely embedded in R by using the rcpp and rpud libraries for operations that are computationally highly intensive. We show that we can use HC StratoMineR for the analysis of multivariate data from a high-content siRNA knock-down screen and a small-molecule screen. It can be used to rapidly filter out undesirable data; to select relevant data; and to perform quality control, data reduction, data exploration, morphological hit picking, and data clustering. Our results demonstrate that HC StratoMineR can be used to functionally categorize HCS hits and, thus, provide valuable information for hit prioritization.

RNA Interference (RNAi) Screening inDrosophila

“…Visualization tools are important throughout the analysis workflow to summarize data and identify potential experimental problems [ e.g. , HTSvis ( Scheeder et al 2017 ) and StratomineR HC ( Omta et al 2016 )]. It is advisable to develop analysis workflows concurrently with the cell-based assay and to test assay performance and detect potential problems.…”

“…While the cellHTS family of tools was specifically designed with focus on reporter and biochemical assays and various different normalization and hit-calling methods, HTSanalyzeR focuses on gene set enrichment analysis and subnetwork stratification of hit lists as its unique features. Software for image-based screening analysis include CellProfiler, EBImage, CellProfiler analyst, imageHTS, StratomineR, and cytominr ( Carpenter et al 2006 ; Jones et al 2008 ; Pau et al 2010 ; Omta et al 2016 ). From these, CellProfiler and EBImage can be used to derive quantitative features from acquired fluorescent images by performing all operations needed for normalizing images, reducing noise, segmenting cells, and extracting numerical features from fluorescent markers within the segmented regions of interest.…”

A Systematic Review of Open Source Clinical Software on GitHub for Improving Software Reuse in Smart Healthcare

“…This study refers to clinical software as software that is implemented in healthcare to help doctors improve patient care. Software that is developed for biological research, such as genome sequencing [20] and cell screening [21], were excluded. Therefore, "(clinical OR medical) OR (patient OR doctor)" was selected as the search term.…”

Improving Comprehension Efficiency of High Content Screening Data Through Interactive Visualizations

“…5 However, the advanced data mining knowledge required to analyze these large amounts of data is often lacking in the toolkit of most life scientists. 6 This pushes the analysis of experimental results away from the life scientist and into the domain of the data scientist, who often lacks the expertise of the life scientist. Therefore a solution should be sought that allows life scientists to analyze their own experiments.…”

“…HC StratoMineR, a tool for the analysis of true multivariate high content data, was recently published. It is a Web-based tool for the analysis of HCS data, 6 which allows the user to make use of the full potential of high content (HC) data. Within the workflow of HC StratoMineR, there are various opportunities for the user to visualize the data.…”