## Heat Maps - Data Analysis with R

• 0:00 - 0:03
The last plot that we'll make for this course is called
• 0:03 - 0:05
a Heat Map. For our data set we want to display
• 0:05 - 0:09
each combination of gene and sample case, the difference in gene
• 0:09 - 0:13
expression and the sample from the base line. We want to display combinations
• 0:13 - 0:17
where a gene is over expressed in red. in combinations where
• 0:17 - 0:20
it is under expressed in blue. Here's the code to make that
• 0:20 - 0:23
Heat Map. First, we'll run all of this in order to
• 0:23 - 0:26
melt our data to a long format. And then we just run
• 0:26 - 0:29
our ggplot code using the geom, geom tile. Now,
• 0:29 - 0:32
this last line is going to give us a scale gradient.
• 0:32 - 0:34
And we're going to use the colors from blue to red.
• 0:34 - 0:36
So, let's see what the output looks like. And, there's
• 0:36 - 0:40
our Heat Map. Even with such a dense display, we
• 0:40 - 0:43
aren't looking at all the data. In particular, we're just
• 0:43 - 0:46
showing the first 200 genes. That's 200 genes of over
• 0:46 - 0:50
6,000 of them. And since this data set was produced.
• 0:50 - 0:53
Genomic data sets of these kind, sometimes called
• 0:53 - 0:57
micro data are only getting larger, and more complex.
• 0:57 - 1:00
What's most interesting, is that other data sets also
• 1:00 - 1:03
look like this. For example, internet companies run lots
• 1:03 - 1:07
of randomized experiments. Where in the simplest versions, users
• 1:07 - 1:10
are randomly assigned to a treatment like a new
• 1:10 - 1:12
version of a website or some sort of new
• 1:12 - 1:15
feature or product or a control condition. Then the
• 1:15 - 1:18
difference in outcome between the treatment and control can
• 1:18 - 1:21
be computed for a number of metrics of interest.
• 1:21 - 1:23
In many situations, there might have been hundreds or
• 1:23 - 1:26
thousands of experiments and hundreds of metrics. This data
• 1:26 - 1:28
looks very similar to the genomic data in some
• 1:28 - 1:31
ways. And this is why the useful maxim plot
• 1:31 - 1:34
all the data might not always apply to a
• 1:34 - 1:36
data set as it did to most of this course.
タイトル：
Heat Maps - Data Analysis with R
Video Language:
English
Team:
Udacity
プロジェクト：
UD651: Exploratory Data Analysis
Duration:
01:37
 Udacity Robot edited 英語(米国) subtitles for Heat Maps - Data Analysis with R Cogi-Admin edited 英語(米国) subtitles for Heat Maps - Data Analysis with R

# English subtitles

## 改訂 Compare revisions

• API
Udacity Robot
• API