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Statistics for Genomic Data Science Wrap-Up (1:53)

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Statistics for Genomic Data Science

Johns Hopkins University

4.1 (185 ratings) | 17K Students Enrolled

Course 7 of 8 in the Genomic Data Science Specialization

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.

Skills You'll Learn

Statistics, Data Analysis, R Programming, Biostatistics

Reviews

4.1 (185 ratings)

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LR

May 23, 2016

I have really enjoyed the course and I have learnt different concepts relevant for my current study.\n\nYurany

TK

Mar 25, 2019

Very good course and useful understanding statistical aspects of data.

From the lesson

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.

Module 4 Overview (1:21)1:21

Gene Set Enrichment (4:19)4:19

More Enrichment (3:59)3:59

Gene Set Analysis in R (7:43)7:43

The Process for RNA-seq (3:59)3:59

The Process for Chip-Seq (5:25)5:25

The Process for DNA Methylation (5:03)5:03

The Process for GWAS/WGS (6:12)6:12

Combining Data Types (eQTL) (6:04)6:04

eQTL in R (10:36)10:36

Researcher Degrees of Freedom (5:49)5:49

Inference vs. Prediction (8:52)8:52

Knowing When to Get Help (2:31)2:31

Statistics for Genomic Data Science Wrap-Up (1:53)1:53

Taught By

Jeff Leek, PhD
Associate Professor, Biostatistics

Transcript

Welcome to the end of the class. I hope that you've really enjoyed learning about statistics for genomic data science. As you know, statistics is one of the main branch of genomic data science. But sometimes its the one that gets left out a little bit, statistics is often the one that's the least one talked about when you talk about genomic data science. Computer science and biology often get a lot of the attention. You can see that in, this is no joke, a published paper where they put the statement, (insert statistical method here), right there in the abstract of the paper. Sort of gives you an idea that sometimes statistics isn't always on the top of people's minds. But it's really important and it's a huge component of the process. In fact, the p-value that we talked about in the class is the most widely used statistic in all of genomics, and all of science really. If every time that the guy who wrote the p-value paper got a citation he would have over 3 million citations, which would make it the most highly cited paper in the history of any scientific discipline. So statistics is incredibly important and I hope that you've learned enough about it to get you sort of started and get you excited on your journey towards being a statistical genomic data scientist. So there's a lot more to learn that what we've covered in these four weeks. Obviously we had to move pretty quickly to get through all the material in that time. But here's a couple of other things that you could go check out. There's an Advanced Statistics for Life Sciences course taught by one of my buddies, Rafa Irizarry. And you should go check that out if you have liked what you learned here and want to learn a little bit more. You could also take the other classes in the genomic data science specialization, or the classes from the Johns Hopkins data science specialization which focus more on basic statistics. Which you might want to learn about more if you're kind of into the stuff that you've learned in this class. So finally, I'd just like to thank you for sticking around. And I hope you've enjoyed the class, and good luck with your genomics career.

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