Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Passive Filters01:27

Passive Filters

966
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
966
Active Filters01:25

Active Filters

1.3K
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
1.3K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.0K
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.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.0K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

43.1K
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...
43.1K
Blind Procedures02:07

Blind Procedures

13.4K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
13.4K
Data Reporting and Recording01:24

Data Reporting and Recording

5.4K
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...
5.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Per- and polyfluoroalkyl substances (PFAS) in early life is associated with childhood intestinal inflammation: analyses of three birth cohorts.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2026
Same author

Impact of pharmacist involvement on hospital sepsis response teams.

American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists·2026
Same author

Demographic, birth, parental characteristics, and the risk of early-onset colorectal cancer: A population-based nested case-control study in California.

Cancer·2026
Same author

Body-weight-specific and shared metabolomic responses to acute sleep loss in young adults.

Journal of translational medicine·2026
Same author

UNLOCKING MULTI-SAMPLE DIFFERENTIAL EXPRESSION FOR SPATIAL TRANSCRIPTOMICS DATA WITH TESSERA.

bioRxiv : the preprint server for biology·2026
Same author

Targeted and non-targeted analyses of per-and polyfluoroalkyl substances in newborn dried blood spots and risk of childhood acute lymphoblastic leukemia.

Journal of exposure science & environmental epidemiology·2026

Related Experiment Video

Updated: Jan 23, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

4.2K

Filtering procedures for untargeted LC-MS metabolomics data.

Courtney Schiffman1, Lauren Petrick2,3, Kelsi Perttula4

  • 1Division of Biostatistics, UC Berkeley, Berkeley, 94720, USA. courtneys@berkeley.edu.

BMC Bioinformatics
|June 16, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a data-adaptive pipeline to filter uninformative features in untargeted metabolomics data. The method effectively removes noise, improving biomarker discovery and pathway analysis in complex biological samples.

Keywords:
Data-adaptiveFilteringMetabolomicsPreprocessing

More Related Videos

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

10.6K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K

Related Experiment Videos

Last Updated: Jan 23, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

4.2K
Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

10.6K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K

Area of Science:

  • Metabolomics
  • Biochemistry
  • Bioinformatics

Background:

  • Untargeted metabolomics data often contains numerous uninformative features.
  • These features can hinder crucial analyses like biomarker discovery and metabolic pathway analysis.
  • A need exists for adaptable methods to pre-process metabolomics data effectively.

Purpose of the Study:

  • To propose and evaluate a novel data-adaptive pipeline for filtering untargeted metabolomics data.
  • To enhance the quality of metabolomics datasets for downstream biological investigations.
  • To provide a versatile tool for handling complex biospecimen data.

Main Methods:

  • Development of a data-adaptive filtering pipeline for liquid chromatography-mass spectrometry (LC-MS) data.
  • Incorporation of novel filtering criteria including blank samples, missing value proportions, and intra-class correlation coefficients.
  • Utilized human blood and public LC-MS datasets for validation.

Main Results:

  • The data-adaptive filtering method demonstrated superior performance compared to traditional threshold-based approaches.
  • Effectively removed noisy features while preserving high-quality, biologically relevant information.
  • R code for the pipeline is publicly available.

Conclusions:

  • The proposed data-adaptive pipeline offers an intuitive and effective solution for filtering uninformative features in untargeted metabolomics.
  • This method is particularly valuable for analyzing complex biological matrices.
  • Enhances the reliability of biological phenomenon interrogation from metabolomics data.