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.

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Statistics for Genomic Data Science

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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.

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Module 3

This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.

- Jeff Leek, PhDAssociate Professor, Biostatistics

Bloomberg School of Public Health

Welcome to Week 3 of Statistics for Genomic Data Science.

Last week, we talked a little bit about linear modeling, and preprocessing and

normalization.

This week we're going to be talking a little bit more about modeling.

Finishing up some of the more technical details.

And we're also going to start talking about statistical significance.

And in particular, if you know anything about statistics, or

you've ever taken a statistics class before, you've heard about the P-value.

It's the most commonly used statistic in the world.

Millions and millions of P-values are calculated every year.

We're going to talk about how do you use those.

How do you perform hypothesis tests to try to make discoveries in genomics?

When you're doing statistical significance in genomics it gets a little bit tricky

though, because there's often many, many, many things that you're testing.

And you're trying to discover sort of a few signals among

many many different things that you might be trying to study.

And so we're going to talk a lot about multiple comparisons, and

how do you check to make sure that the discovery you're making is

likely to be real and not a false positive that came up just because you're doing so

much data sifting when you're looking through the data.

So we're going to talk about statistical significance and

multiple testing this week.

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