Lecture

Protein Mass Spectrometry and Proteomics: Randomization Overview

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In course

CH-419: Protein mass spectrometry and proteomics

In systems biology, proteomics represents an essential pillar. The understanding of protein function and regulation provides key information to decipher the complexity of living systems. Proteomic technology now enables deep quantitative proteome mapping v ...

Description

This lecture covers the principles of protein mass spectrometry and proteomics, focusing on the importance of randomization in experimental design. The instructor explains the concept of random allocation, the need for randomization to minimize bias, different types of randomization methods, and the process of checking randomization. Practical examples and tools for randomization are also discussed, emphasizing the critical role of randomization in ensuring the validity and reliability of research outcomes.

Instructor

Loïc Dayon

Teaching & PhD Courses Protein mass spectrometry and proteomics CH-419 In systems biology, proteomics represents an essential pillar. The understanding of protein function and regulation provides key information to decipher the complexity of living systems. Proteomic technology now enables deep quantitative proteome mapping via mass spectrometry.

Official source

Ontological neighbourhood

Statistics

Data collection: Experiment

Related concepts (32)

External validity

External validity is the validity of applying the conclusions of a scientific study outside the context of that study. In other words, it is the extent to which the results of a study can be generalized to and across other situations, people, stimuli, and times. In contrast, internal validity is the validity of conclusions drawn within the context of a particular study. Because general conclusions are almost always a goal in research, external validity is an important property of any study.

Randomization

Randomization is the process of making something random. Randomization is not haphazard; instead, a random process is a sequence of random variables describing a process whose outcomes do not follow a deterministic pattern, but follow an evolution described by probability distributions. For example, a random sample of individuals from a population refers to a sample where every individual has a known probability of being sampled. This would be contrasted with nonprobability sampling where arbitrary individuals are selected.

Validity (statistics)

Validity is the main extent to which a concept, conclusion or measurement is well-founded and likely corresponds accurately to the real world. The word "valid" is derived from the Latin validus, meaning strong. The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below.

Ecological validity

In the behavioral sciences, ecological validity is often used to refer to the judgment of whether a given study's variables and conclusions (often collected in lab) are sufficiently relevant to its population (e.g. the "real world" context). Psychological studies are usually conducted in laboratories though the goal of these studies is to understand human behavior in the real-world. Ideally, an experiment would have generalizable results that predict behavior outside of the lab, thus having more ecological validity.

Internal validity

Internal validity is the extent to which a piece of evidence supports a claim about cause and effect, within the context of a particular study. It is one of the most important properties of scientific studies and is an important concept in reasoning about evidence more generally. Internal validity is determined by how well a study can rule out alternative explanations for its findings (usually, sources of systematic error or 'bias').

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