Lecture

Genomic Data Analysis: Quality Control Tools

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In course

MATH-474: Statistics for genomic data analysis

After a short introduction to basic molecular biology and genomic technologies, this course covers the most useful statistical concepts and methods for the analysis of genomic data. ...

Description

This lecture covers the quality control tools used in genomic data analysis, focusing on Affymetrix QA/QC, sample preparation QC, data QC, spike-ins, controls, and MAS 5 algorithms. It explains the importance of quality metrics, RMA-QC, and the RMA additive model for gene expression. The instructor discusses the challenges with quality measures and the robustness of the analysis, emphasizing the need for robust procedures in the presence of outliers and image artifacts.

Instructor

Darlene Goldstein

Official source

Related concepts (16)

Robust regression

In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship between one or more independent variables and a dependent variable. Standard types of regression, such as ordinary least squares, have favourable properties if their underlying assumptions are true, but can give misleading results otherwise (i.e. are not robust to assumption violations).

Quality control

Quality control (QC) is a process by which entities review the quality of all factors involved in production. ISO 9000 defines quality control as "a part of quality management focused on fulfilling quality requirements". This approach places emphasis on three aspects (enshrined in standards such as ISO 9001): Elements such as controls, job management, defined and well managed processes, performance and integrity criteria, and identification of records Competence, such as knowledge, skills, experience, and qualifications Soft elements, such as personnel, integrity, confidence, organizational culture, motivation, team spirit, and quality relationships.

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Quality assurance

Quality assurance (QA) is the term used in both manufacturing and service industries to describe the systematic efforts taken to assure that the product(s) delivered to customer(s) meet with the contractual and other agreed upon performance, design, reliability, and maintainability expectations of that customer. The core purpose of Quality Assurance is to prevent mistakes and defects in the development and production of both manufactured products, such as automobiles and shoes, and delivered services, such as automotive repair and athletic shoe design.

Quality management

Quality management ensures that an organization, product or service consistently functions well. It has four main components: quality planning, quality assurance, quality control and quality improvement. Quality management is focused not only on product and service quality, but also on the means to achieve it. Quality management, therefore, uses quality assurance and control of processes as well as products to achieve more consistent quality. Quality control is also part of quality management.

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