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Statistical Inference and Modeling for High-throughput Experiments

A focus on the techniques commonly used to perform statistical inference on high throughput data.

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Statistical Inference and Modeling for High-throughput Experiments

A focus on the techniques commonly used to perform statistical inference on high throughput data.

4 semanas
2–4 horas por semana
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Hay una sesión disponible:

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Comienza el 5 jun
Termina el 29 nov
Inscríbete
Statistical Inference and Modeling for High-throughput Experiments

Sobre este curso

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In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. We provide several examples of how these concepts are applied in next generation sequencing and microarray data. Finally, we will discuss hierarchical models and empirical bayes along with some examples of how these are used in practice. We provide R programming examples in a way that will help make the connection between concepts and implementation.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up two Professional Certificates and are self-paced:

Data Analysis for Life Sciences:

Genomics Data Analysis:

This class was supported in part by NIH grant R25GM114818.

De un vistazo

  • Idioma: English
  • Transcripción de video: English
  • Associated skills:Next-Generation Sequencing, Maximum Likelihood, Statistical Modeling, Data Warehousing, Bayesian Statistics, Software Engineering, Statistics, Functional Genomics, Exploratory Data Analysis, Life Sciences, Research, Statistical Inference, Data Analysis, Bioconductor (Bioinformatics Software), Biology, DNA Sequencing, Matrix Algebra, R (Programming Language), Linear Model

Lo que aprenderás

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  • Organizing high throughput data
  • Multiple comparison problem
  • Family Wide Error Rates
  • False Discovery Rate
  • Error Rate Control procedures
  • Bonferroni Correction
  • q-values
  • Statistical Modeling
  • Hierarchical Models and the basics of Bayesian Statistics
  • Exploratory Data Analysis for High throughput data

Acerca de los instructores

Más información sobre este curso

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This course is part ofData Analysis for Life Sciences Professional Certificate Program

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Instrucción por expertos
4 cursos de capacitación
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4 meses
2 - 4 horas semanales

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Costo

129 US$

Gratis

Acceso a los materiales del curso

Ilimitado

Limitado

Caduca el 3 jul

Instituciones y universidades de primera categoría

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Certificado para compartir al finalizar

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