À propos de ce cours : An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
Créé par : Université Johns-Hopkins
Enseigné par : Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
| Informations de base | Cours 7 de 8 dans Genomic Data Science Specialization |
| Langue | Anglais |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
Module 1
This course is structured to hit the key conceptual ideas of normalization, exploratory analysis, linear modeling, testing, and multiple testing that arise over and over in genomic studies.
21 vidéos, 3 lectures
- Reading: Syllabus
- Reading: Pre Course Survey
- Reading: Introduction and Materials
- Vidéo: What is Statistics?
- Vidéo: Finding Statistics You Can Trust (4:44)
- Vidéo: Getting Help (3:44)
- Vidéo: What is Data? (4:28)
- Vidéo: Representing Data (5:23)
- Vidéo: Module 1 Overview (1:07)
- Vidéo: Reproducible Research (3:42)
- Vidéo: Achieving Reproducible Research (5:02)
- Vidéo: R Markdown (6:26)
- Vidéo: The Three Tables in Genomics (2:10)
- Vidéo: The Three Tables in Genomics (in R) (3:46)
- Vidéo: Experimental Design: Variability, Replication, and Power (14:17)
- Vidéo: Experimental Design: Confounding and Randomization (9:26)
- Vidéo: Exploratory Analysis (9:21)
- Vidéo: Exploratory Analysis in R Part I (7:22)
- Vidéo: Exploratory Analysis in R Part II (10:07)
- Vidéo: Exploratory Analysis in R Part III (7:26)
- Vidéo: Data Transforms (7:31)
- Vidéo: Clustering (8:43)
- Vidéo: Clustering in R (9:09)
Noté: Module 1 Quiz
SEMAINE 2
Module 2
This week we will cover preprocessing, linear modeling, and batch effects.
14 vidéos
- Vidéo: Dimension Reduction (12:13)
- Vidéo: Dimension Reduction (in R) (8:48)
- Vidéo: Pre-processing and Normalization (11:26)
- Vidéo: Quantile Normalization (in R) (4:49)
- Vidéo: The Linear Model (6:50)
- Vidéo: Linear Models with Categorical Covariates (4:08)
- Vidéo: Adjusting for Covariates (4:16)
- Vidéo: Linear Regression in R (13:03)
- Vidéo: Many Regressions at Once (3:50)
- Vidéo: Many Regressions in R (7:21)
- Vidéo: Batch Effects and Confounders (7:11)
- Vidéo: Batch Effects in R: Part A (8:18)
- Vidéo: Batch Effects in R: Part B (3:50)
Noté: Module 2 Quiz
SEMAINE 3
Module 3
This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
15 vidéos
- Vidéo: Logistic Regression (7:03)
- Vidéo: Regression for Counts (5:02)
- Vidéo: GLMs in R (9:28)
- Vidéo: Inference (4:18)
- Vidéo: Null and Alternative Hypotheses (4:45)
- Vidéo: Calculating Statistics (5:11)
- Vidéo: Comparing Models (7:08)
- Vidéo: Calculating Statistics in R
- Vidéo: Permutation (3:26)
- Vidéo: Permutation in R (3:33)
- Vidéo: P-values (6:04)
- Vidéo: Multiple Testing (8:25)
- Vidéo: P-values and Multiple Testing in R: Part A (5:58)
- Vidéo: P-values and Multiple Testing in R: Part B (4:23)
Noté: Module 3 Quiz
SEMAINE 4
Module 4
In this week we will cover a lot of the general pipelines people use to analyze specific data types like RNA-seq, GWAS, ChIP-Seq, and DNA Methylation studies.
14 vidéos, 1 lecture
- Vidéo: Gene Set Enrichment (4:19)
- Vidéo: More Enrichment (3:59)
- Vidéo: Gene Set Analysis in R (7:43)
- Vidéo: The Process for RNA-seq (3:59)
- Vidéo: The Process for Chip-Seq (5:25)
- Vidéo: The Process for DNA Methylation (5:03)
- Vidéo: The Process for GWAS/WGS (6:12)
- Vidéo: Combining Data Types (eQTL) (6:04)
- Vidéo: eQTL in R (10:36)
- Vidéo: Researcher Degrees of Freedom (5:49)
- Vidéo: Inference vs. Prediction (8:52)
- Vidéo: Knowing When to Get Help (2:31)
- Vidéo: Statistics for Genomic Data Science Wrap-Up (1:53)
- Reading: Post Course Survey
Noté: Module 4 Quiz
FAQ
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Créateurs
Université Johns-Hopkins
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
I think this is one of the best courses in this specialization. I found it the most helpful in building together what should be learned in genomic data science. I wish 1) this course was earlier in the specialization, 2) there was additional building from this course to build together the workflow from beginning to end, and 3) reduction or removal of some of the other courses (or other courses taught together with this one).
really a good course for people who want to learn use R to dispose genomic data
The professor is really enthusiasm, so I was really impreesed by him. And his teaching is brief, and I can learn key points through the lectures. Great course!
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HT
Great place to start learning genomics in R