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Related Concept Videos

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Updated: Feb 8, 2026

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Identifying unmeasured heterogeneity in microbiome data via quantile thresholding (QuanT).

Jiuyao Lu1, Glen A Satten2, Katie A Meyer3

  • 1Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, 265 South 37th Street, Philadelphia, 19104 PA, USA.

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|February 7, 2026
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Quantile thresholding (QuanT) identifies unmeasured heterogeneity in microbiome data. This novel method improves downstream analyses for more accurate microbiome research.

Keywords:
Batch effectsConditional quantile regressionMicrobiome dataUnmeasured heterogeneityZero inflation

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput microbiome data exhibit technical heterogeneity from experimental variations.
  • Unmeasured factors cause spurious conclusions, a growing issue in multi-center studies.
  • Existing methods for RNA-seq data fail to address microbiome data's sparsity and over-dispersion.

Purpose of the Study:

  • Introduce a novel non-parametric approach for identifying unmeasured heterogeneity in microbiome data.
  • Develop a tool tailored to the unique characteristics of microbiome datasets.
  • Enhance the accuracy and reliability of microbiome data analysis.

Main Methods:

  • Quantile thresholding (QuanT) uses quantile regression across multiple levels.
  • Microbiome abundance data are thresholded to uncover latent heterogeneity.
  • Thresholded binary residual matrices are generated for analysis.

Main Results:

  • QuanT effectively identifies and mitigates unmeasured heterogeneity in microbiome data.
  • Validation on synthetic and real datasets demonstrates QuanT's superiority.
  • Improved accuracy in downstream analyses including prediction, differential abundance, and diversity evaluations.

Conclusions:

  • QuanT is a novel tool for comprehensive identification of unmeasured heterogeneity in microbiome data.
  • The non-parametric method significantly enhances downstream analyses.
  • QuanT serves as a valuable tool for microbiome data integration and analysis.