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

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From the course by Johns Hopkins University

Statistics for Genomic Data Science

138 ratings

Johns Hopkins University

Statistics for Genomic Data Science

138 ratings

Course 7 of 8 in the Specialization Genomic Data Science

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.

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

Meet the Instructors

Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

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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