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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
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Mass Spectrometry: Overview01:19

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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass.  One common type of ionization, known as electrospray ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave...
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Flow Cytometry01:23

Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
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Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

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An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a low-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.
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Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and signal-to-noise ratio for the analyte. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.
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Sample Preparation for Mass Cytometry Analysis
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Compositional Data Analysis using Kernels in mass cytometry data.

Pratyaydipta Rudra1, Ryan Baxter2, Elena W Y Hsieh2,3

  • 1Department of Statistics, Oklahoms State University, Stillwater, OK 74078, USA.

Bioinformatics Advances
|February 28, 2022
PubMed
Summary
This summary is machine-generated.

We developed Compositional Data Analysis using Kernels (CODAK), a novel method for analyzing cell type abundance data from mass cytometry. CODAK effectively handles high-dimensional and small sample size data, outperforming existing methods.

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Immunology

Background:

  • Mass cytometry generates compositional cell-type abundance data, which are non-Euclidean and unsuitable for classical association tests.
  • Existing methods for analyzing cell type abundance data have limitations, particularly with high-dimensional datasets and small sample sizes.

Purpose of the Study:

  • To introduce a new multivariate statistical learning methodology for analyzing compositional cell-type abundance data.
  • To address the limitations of existing methods in high-dimensional mass cytometry data analysis, especially for small sample sizes.

Main Methods:

  • Proposed Compositional Data Analysis using Kernels (CODAK), a method based on the kernel distance covariance (KDC) framework.
  • CODAK is designed to test associations between cell type compositions and predictors like disease status.
  • The methodology is implemented in R, with code and data publicly available.

Main Results:

  • CODAK demonstrates scalability for high-dimensional data.
  • The method shows satisfactory performance even with small sample sizes (n < 25).
  • Simulation studies confirmed CODAK's superior performance compared to existing methods.
  • CODAK was successfully applied to a high-dimensional dataset involving Systemic Lupus Erythematosus (SLE) patients and healthy controls.

Conclusions:

  • CODAK provides a robust and effective approach for analyzing compositional cell-type abundance data from mass cytometry.
  • The method offers a valuable tool for high-dimensional biological data analysis, particularly in immunology and disease research.
  • The availability of R implementation facilitates its adoption and application in the research community.