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Related Concept Videos

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
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High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput

Bogdan Mazoure1,2, Iurie Caraus1,2, Robert Nadon2,3

  • 11 Département d'Informatique, Université du Québec à Montréal, Montréal, QC, Canada.

SLAS Discovery : Advancing Life Sciences R & D
|January 19, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces new spatial bias models for high-throughput screening (HTS) data. These advanced methods accurately correct measurements affected by complex bias interactions in multiwell plates.

Keywords:
Anderson–Darling testCramer–von Mises testMann–Whitney U testdata correctionhigh-content screeninghigh-throughput screeningpartial mean polishsmall-molecule microarrayspatial bias

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Statistical Modeling

Background:

  • High-throughput screening (HTS) data often suffer from spatial bias, affecting measurement accuracy.
  • Existing bias correction methods, using simple additive or multiplicative models, may not fully address complex spatial bias interactions.
  • Accurate bias correction is crucial for reliable results in various screening technologies.

Purpose of the Study:

  • To develop and validate novel spatial bias models for HTS data.
  • To introduce a statistical procedure for detecting and removing complex additive and multiplicative spatial biases.
  • To demonstrate the applicability of these methods across diverse HTS and high-content screening (HCS) technologies.

Main Methods:

  • Proposed two novel additive and two novel multiplicative spatial bias models accounting for bias interactions.
  • Developed a statistical procedure for detecting and removing spatial biases in multiwell plates.
  • Applied the methods to data from homogeneous, microorganism, cell-based, and gene expression HTS, and area, intensity, and cell-count HCS technologies, as well as small-molecule microarrays.

Main Results:

  • The novel models effectively account for bias interactions, improving correction accuracy.
  • The statistical procedure successfully detected and removed various types of spatial biases across different screening platforms.
  • Demonstrated the broad applicability and robustness of the proposed methods.

Conclusions:

  • The developed spatial bias models and statistical procedure offer enhanced accuracy for HTS data analysis.
  • The AssayCorrector program, implementing these methods, provides a valuable tool for researchers using HTS and HCS.
  • Accurate spatial bias correction is essential for reliable interpretation of screening data across multiple technologies.