关于此课程： 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.
制作方： Johns Hopkins University
教学方： Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
| 基本信息 | 课程 7（共 8 门，基因组数据科学 Specialization ） |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
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 个视频, 3 个阅读材料
- Reading: Syllabus
- Reading: Pre Course Survey
- Reading: Introduction and Materials
- 视频: What is Statistics?
- 视频: Finding Statistics You Can Trust (4:44)
- 视频: Getting Help (3:44)
- 视频: What is Data? (4:28)
- 视频: Representing Data (5:23)
- 视频: Module 1 Overview (1:07)
- 视频: Reproducible Research (3:42)
- 视频: Achieving Reproducible Research (5:02)
- 视频: R Markdown (6:26)
- 视频: The Three Tables in Genomics (2:10)
- 视频: The Three Tables in Genomics (in R) (3:46)
- 视频: Experimental Design: Variability, Replication, and Power (14:17)
- 视频: Experimental Design: Confounding and Randomization (9:26)
- 视频: Exploratory Analysis (9:21)
- 视频: Exploratory Analysis in R Part I (7:22)
- 视频: Exploratory Analysis in R Part II (10:07)
- 视频: Exploratory Analysis in R Part III (7:26)
- 视频: Data Transforms (7:31)
- 视频: Clustering (8:43)
- 视频: Clustering in R (9:09)
已评分: Module 1 Quiz
第 2 周
Module 2
This week we will cover preprocessing, linear modeling, and batch effects.
14 个视频
- 视频: Dimension Reduction (12:13)
- 视频: Dimension Reduction (in R) (8:48)
- 视频: Pre-processing and Normalization (11:26)
- 视频: Quantile Normalization (in R) (4:49)
- 视频: The Linear Model (6:50)
- 视频: Linear Models with Categorical Covariates (4:08)
- 视频: Adjusting for Covariates (4:16)
- 视频: Linear Regression in R (13:03)
- 视频: Many Regressions at Once (3:50)
- 视频: Many Regressions in R (7:21)
- 视频: Batch Effects and Confounders (7:11)
- 视频: Batch Effects in R: Part A (8:18)
- 视频: Batch Effects in R: Part B (3:50)
已评分: Module 2 Quiz
第 3 周
Module 3
This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
15 个视频
- 视频: Logistic Regression (7:03)
- 视频: Regression for Counts (5:02)
- 视频: GLMs in R (9:28)
- 视频: Inference (4:18)
- 视频: Null and Alternative Hypotheses (4:45)
- 视频: Calculating Statistics (5:11)
- 视频: Comparing Models (7:08)
- 视频: Calculating Statistics in R
- 视频: Permutation (3:26)
- 视频: Permutation in R (3:33)
- 视频: P-values (6:04)
- 视频: Multiple Testing (8:25)
- 视频: P-values and Multiple Testing in R: Part A (5:58)
- 视频: P-values and Multiple Testing in R: Part B (4:23)
已评分: Module 3 Quiz
第 4 周
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 个视频, 1 个阅读材料
- 视频: Gene Set Enrichment (4:19)
- 视频: More Enrichment (3:59)
- 视频: Gene Set Analysis in R (7:43)
- 视频: The Process for RNA-seq (3:59)
- 视频: The Process for Chip-Seq (5:25)
- 视频: The Process for DNA Methylation (5:03)
- 视频: The Process for GWAS/WGS (6:12)
- 视频: Combining Data Types (eQTL) (6:04)
- 视频: eQTL in R (10:36)
- 视频: Researcher Degrees of Freedom (5:49)
- 视频: Inference vs. Prediction (8:52)
- 视频: Knowing When to Get Help (2:31)
- 视频: Statistics for Genomic Data Science Wrap-Up (1:53)
- Reading: Post Course Survey
已评分: Module 4 Quiz
常见问题解答
运作方式
Travail en cours
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制作方
Johns Hopkins University
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.
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评分和审阅
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!
Enjoyed it. One of better courses I have taken in Coursera. A good introduction to using statistics in Bioconductor with genomics data.
EW
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).