diff --git a/10-data-organisation.html b/10-data-organisation.html index 7cdcf0b1..59aa1321 100644 --- a/10-data-organisation.html +++ b/10-data-organisation.html @@ -1,5 +1,5 @@ -Introduction to data analysis with R and Bioconductor: Data organisation with spreadsheets -
Introduction to data analysis with R and Bioconductor @@ -100,6 +124,33 @@
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2. R and RStudio
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3. Introduction to R
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4. Starting with data
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5. Manipulating and analysing data with dplyr
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6. Data visualization
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7. Next steps
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See all in one page - +
See all in one page +
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Home

Data organisation with spreadsheets

-

Last updated on 2024-05-07 | - +

Last updated on 2024-12-12 | + Edit this page

- - - + + +
@@ -320,8 +371,7 @@

Objectives

This episode is based on the Data Carpentries’s Data Analysis and Visualisation in R for Ecologists lesson.

-

Spreadsheet programs -

+

Spreadsheet programs

Question

  • What are basic principles for using spreadsheets for good data organization?
    • @@ -381,9 +431,7 @@

    Why aren’t we tea

-

Challenge: Discuss the following points with -your neighbour -

+

Challenge: Discuss the following points with your neighbour

  • Have you used spreadsheets, in your research, courses, or at home?
    • @@ -430,8 +478,7 @@

    Using spreadsheets for d
  • Formatting problems
  • Exporting data

-

Formatting data tables in spreadsheets -

+

Formatting data tables in spreadsheets

Questions

  • How do we format data in spreadsheets for effective data use?

Objectives

@@ -526,9 +573,7 @@

Structuring data in spreadsheets

-

Challenge: We’re going to take a messy dataset -and describe how we would clean it up. -

+

Challenge: We’re going to take a messy dataset and describe how we would clean it up.

  1. Download a messy dataset by clicking here.

  2. Open up the data in a spreadsheet program.

    3. @@ -564,9 +609,7 @@

    Challenge: We’re going to take a messy dataset

-

Challenge: Once you have tidied up the data, -answer the following questions: -

+

Challenge: Once you have tidied up the data, answer the following questions:

  • How many men and women took part in the study?
  • How many A, AB, and B types have been tested?
    • @@ -581,8 +624,7 @@

    Challenge: Once you have tidied up the data,

    An excellent reference, in particular with regard to R scripting is the Tidy Data paper @Wickham:2014.

-

Common spreadsheet errors -

+

Common spreadsheet errors

Questions

-

Exporting data -

+

Exporting data

Question

  • How can we export data from spreadsheets in a way that is useful for downstream applications?
    • @@ -981,8 +1022,7 @@

    Caveats on commas

    Summary -

    +

Summary

A typical data analysis workflow.
A typical data analysis workflow.

A typical data analysis workflow is illustrated in the figure above, @@ -1000,8 +1040,7 @@

Caveats on commas

-

Key Points -

+

Key Points

@@ -1040,20 +1079,20 @@

Key Points

This lesson is subject to the Code of Conduct

- + Edit on GitHub - + | Contributing | Source

Cite | Contact | About

- +

Materials licensed under CC-BY 4.0 by the authors

- +

Template licensed under CC-BY 4.0 by The Carpentries

-

Built with sandpaper (0.16.4), pegboard (0.7.5), and varnish (1.0.2)

+

Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)

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See all in one page - +
See all in one page +
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Previous: Data organisation

R and RStudio

-

Last updated on 2024-05-07 | - +

Last updated on 2024-12-12 | + Edit this page

- - - + + +
@@ -325,8 +376,7 @@

Objectives

This episode is based on the Data Carpentries’s Data Analysis and Visualisation in R for Ecologists lesson.

-

What is R? What is RStudio? -

+

What is R? What is RStudio?

The term R is used to refer to the programming language, the environment for statistical computing and the software that interprets the @@ -339,8 +389,7 @@

Objectives

IDE Cheat Sheet provides much more information than will be covered here, but can be useful to learn keyboard shortcuts and discover new features.

-

Why learn R? -

+

Why learn R?

R does not involve lots of pointing and clicking, and that’s a good thing

@@ -419,8 +468,7 @@

Not on this transparency, there is less chance for mistakes, and if you (or someone else) find some, you can report and fix bugs.

-

Knowing your way around RStudio -

+

Knowing your way around RStudio

Let’s start by learning about RStudio, which is an Integrated Development Environment (IDE) for working with R.

The RStudio IDE open-source product is free under the Affero General @@ -449,8 +497,7 @@

Not on highlighting for the major file types you use while developing in R, RStudio will make typing easier and less error-prone.

-

Getting set up -

+

Getting set up

It is good practice to keep a set of related data, analyses, and text self-contained in a single folder, called the working directory. All of the scripts within this folder can then use @@ -531,9 +578,7 @@

Organizing your working directory

-

Challenge: create your project directory -structure -

+

Challenge: create your project directory structure

Under the Files tab on the right of the screen, click on New Folder and create a folder named data @@ -611,8 +656,7 @@

The working directory../fig_output/fig1.pdf or the same absolute path /home/user/bioc-intro/fig_output/fig1.pdf.

-

Interacting with R -

+

Interacting with R

The basis of programming is that we write down instructions for the computer to follow, and then we tell the computer to follow those instructions. We write, or code, instructions in R because it @@ -665,8 +709,7 @@

The working directoryEsc; this will cancel the incomplete command and return you to the > prompt.

-

How to learn more during and after the course? -

+

How to learn more during and after the course?

The material we cover during this course will give you an initial taste of how you can use R to analyse data for your own research. However, you will need to learn more to do advanced operations such as @@ -677,8 +720,7 @@

The working directory -

Seeking help -

+

Seeking help

Use the built-in RStudio help interface to search for more information on R functions

@@ -785,11 +827,11 @@

R

OUTPUT

-
structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4), 
-    Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.9), Petal.Length = c(1.4, 
-    1.4, 1.3, 1.5, 1.4, 1.7), Petal.Width = c(0.2, 0.2, 0.2, 
-    0.2, 0.2, 0.4), Species = structure(c(1L, 1L, 1L, 1L, 1L, 
-    1L), levels = c("setosa", "versicolor", "virginica"), class = "factor")), row.names = c(NA, 
+
structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4),
+    Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.9), Petal.Length = c(1.4,
+    1.4, 1.3, 1.5, 1.4, 1.7), Petal.Width = c(0.2, 0.2, 0.2,
+    0.2, 0.2, 0.4), Species = structure(c(1L, 1L, 1L, 1L, 1L,
+    1L), levels = c("setosa", "versicolor", "virginica"), class = "factor")), row.names = c(NA,
 6L), class = "data.frame")

If the object is larger, provide either the raw file (i.e., your CSV @@ -828,30 +870,30 @@

R

OUTPUT

-
R version 4.3.3 (2024-02-29)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Ubuntu 22.04.4 LTS
+
R version 4.4.2 (2024-10-31)
+Platform: x86_64-pc-linux-gnu
+Running under: Ubuntu 22.04.5 LTS
 
 Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
+BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
 
 locale:
- [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
- [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
- [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
-[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
+ [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8
+ [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8
+ [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C
+[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
 
 time zone: UTC
 tzcode source: system (glibc)
 
 attached base packages:
-[1] stats     graphics  grDevices utils     datasets  methods   base     
+[1] stats     graphics  grDevices utils     datasets  methods   base
 
 loaded via a namespace (and not attached):
-[1] BiocManager_1.30.23 compiler_4.3.3      tools_4.3.3        
-[4] yaml_2.3.8          highr_0.10          knitr_1.46         
-[7] xfun_0.43           renv_1.0.7          evaluate_0.23      

+[1] BiocManager_1.30.25 compiler_4.4.2      tools_4.4.2
+[4] yaml_2.3.10         knitr_1.49          xfun_0.49
+[7] renv_1.0.11         evaluate_1.0.1

@@ -903,8 +945,7 @@

More resources

-

R packages -

+

R packages

Loading packages

As we have seen above, R packages play a fundamental role in R. The @@ -967,8 +1008,7 @@

R

-

Key Points -

+

Key Points

  • Start using R and RStudio
@@ -1005,7 +1045,7 @@

Key Points

@@ -1016,20 +1056,20 @@

Key Points

This lesson is subject to the Code of Conduct

- + Edit on GitHub - + | Contributing | Source

Cite | Contact | About

- +

Materials licensed under CC-BY 4.0 by the authors

- +

Template licensed under CC-BY 4.0 by The Carpentries

-

Built with sandpaper (0.16.4), pegboard (0.7.5), and varnish (1.0.2)

+

Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)

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See all in one page - +
See all in one page +
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Previous: R and RStudio

Introduction to R

-

Last updated on 2024-05-07 | - +

Last updated on 2024-12-12 | + Edit this page

- - - + + +
@@ -329,8 +380,7 @@

Objectives

This episode is based on the Data Carpentries’s Data Analysis and Visualisation in R for Ecologists lesson.

-

Creating objects in R -

+

Creating objects in R

You can get output from R simply by typing math in the console:

R @@ -489,8 +539,7 @@

R

-

Challenge: -

+

Challenge:

What do you think is the current content of the object weight_lb? 126.5 or 220?

@@ -498,8 +547,7 @@

Challenge:

Comments -

+

Comments

The comment character in R is #, anything to the right of a # in a script will be ignored by R. It is useful to leave notes, and explanations in your scripts.

@@ -514,8 +562,7 @@

Challenge:

-

Challenge -

+

Challenge

What are the values after each statement in the following?

@@ -531,8 +578,7 @@

R

-

Functions and their arguments -

+

Functions and their arguments

Functions are “canned scripts” that automate more complicated sets of commands including operations assignments, etc. Many functions are predefined, or can be made available by importing R packages @@ -597,7 +643,7 @@

R

OUTPUT

-
function (x, digits = 0) 
+
function (x, digits = 0, ...)
 NULL

 
 

@@ -652,8 +698,7 @@ 
OUTPUT

R
-

Challenge -

+

Challenge

Use what you just learned to create a plot that depicts how the average expression of each chromosome changes through the duration of @@ -1177,9 +1214,9 @@

Challenge
-
+

R @@ -1236,9 +1273,8 @@

R geom_line() + facet_grid(. ~ sex)

-

-ggplot2 themes -

+

+ggplot2 themes

In addition to theme_bw(), which changes the plot background to white, ggplot2 comes with several other themes which can be useful to quickly change the look of your @@ -1251,8 +1287,7 @@

Rggplot2 extensions website provides a list of packages that extend the capabilities of ggplot2, including additional themes.

-

Customisation -

+

Customisation

Let’s come back to the faceted plot of mean expression by time and gene, colored by sex.

Take a look at the ggplot2 @@ -1339,8 +1374,7 @@

R
-

Challenge -

+

Challenge

With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or @@ -1358,9 +1392,9 @@

Challenge
-
+

For example, based on this plot:

@@ -1440,8 +1474,7 @@

R

-

Composing plots -

+

Composing plots

Faceting is a great tool for splitting one plot into multiple subplots, but sometimes you may want to produce a single figure that contains multiple independent plots, i.e. plots that are based on @@ -1559,8 +1592,7 @@

R

In addition to the ncol and nrow arguments, used to make simple arrangements, there are tools for constructing more complex layouts.

-

Exporting plots -

+

Exporting plots

After creating your plot, you can save it to a file in your favorite format. The Export tab in the Plot pane in RStudio will save your plots at low resolution, which will not be accepted by many @@ -1600,8 +1632,7 @@

R

Note: The parameters width and height also determine the font size in the saved plot.

-

Other packages for visualisation -

+

Other packages for visualisation

ggplot2 is a very powerful package that fits very nicely in our tidy data and tidy tools pipeline. There are other visualization packages in R that shouldn’t be ignored.

@@ -1672,8 +1703,7 @@

The lattice package
-

Key Points -

+

Key Points

  • Visualization in R
@@ -1699,7 +1729,7 @@

Key Points

@@ -1710,20 +1740,20 @@

Key Points

This lesson is subject to the Code of Conduct

- + Edit on GitHub - + | Contributing | Source

Cite | Contact | About

- +

Materials licensed under CC-BY 4.0 by the authors

- +

Template licensed under CC-BY 4.0 by The Carpentries

-

Built with sandpaper (0.16.4), pegboard (0.7.5), and varnish (1.0.2)

+

Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)

@@ -151,7 +202,7 @@

1. Data organisation with spreadsheets - + @@ -162,7 +213,7 @@

2. R and RStudio - + @@ -173,7 +224,7 @@

3. Introduction to R - + @@ -184,7 +235,7 @@

4. Starting with data - + @@ -195,7 +246,7 @@

5. Manipulating and analysing data with dplyr - + @@ -206,7 +257,7 @@

6. Data visualization - + @@ -217,7 +268,7 @@

7. Next steps - + @@ -226,10 +277,10 @@

-
+

-

@@ -250,11 +301,11 @@

-
See all in one page - +
See all in one page +
- +
@@ -267,8 +318,7 @@

Page not found

-

Our apologies! -

+

Our apologies!

We cannot seem to find the page you are looking for. Here are some tips that may help:

  1. try going back to the previous @@ -290,20 +340,20 @@

    Page not found

@@ -325,33 +375,11 @@

Page not found

"url": "https://carpentries-incubator.github.io/bioc-intro/404.html", "identifier": "https://carpentries-incubator.github.io/bioc-intro/404.html", "dateCreated": "2020-09-14", - "dateModified": "2024-05-14", - "datePublished": "2024-05-14" + "dateModified": "2024-12-12", + "datePublished": "2024-12-12" } + diff --git a/60-next-steps.html b/60-next-steps.html index 828cf0ca..006e7975 100644 --- a/60-next-steps.html +++ b/60-next-steps.html @@ -1,5 +1,5 @@ -Introduction to data analysis with R and Bioconductor: Next steps -
Introduction to data analysis with R and Bioconductor @@ -100,6 +124,33 @@
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See all in one page - +
See all in one page +
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Next steps

-

Last updated on 2024-05-07 | - +

Last updated on 2024-12-12 | + Edit this page

- - - + + +
@@ -312,8 +363,7 @@

Objectives

-

Next steps -

+

Next steps

Data in bioinformatics is often complex. To deal with this, developers define specialised data containers (termed classes) that match the properties of the data they need to handle.

@@ -325,8 +375,7 @@

Objectives

the project as a whole.

To illustrate such an omics data container, we’ll present the SummarizedExperiment class.

-

SummarizedExperiment -

+

SummarizedExperiment

The figure below represents the anatomy of the SummarizedExperiment class.

Objects of the class SummarizedExperiment contain :

@@ -554,8 +603,8 @@

R

OUTPUT

-
class: SummarizedExperiment 
-dim: 1474 22 
+
class: SummarizedExperiment
+dim: 1474 22
 metadata(0):
 assays(1): counts
 rownames(1474): Asl Apod ... Lmx1a Pbx1
@@ -759,8 +808,8 @@ 
R
 
OUTPUT
 

-
class: SummarizedExperiment 
-dim: 5 3 
+
class: SummarizedExperiment
+dim: 5 3
 metadata(0):
 assays(1): counts
 rownames(5): Asl Apod Cyp2d22 Klk6 Fcrls
@@ -837,8 +886,8 @@ 
R
 
OUTPUT
 

-
class: SummarizedExperiment 
-dim: 7 7 
+
class: SummarizedExperiment
+dim: 7 7
 metadata(0):
 assays(1): counts
 rownames(7): Mir1901 Mir378a ... Mir128-1 Mir7682
@@ -953,8 +1002,7 @@ 
OUTPUT

R
-

Challenge -

+

Challenge

Use what you just learned to create a plot that depicts how the average expression of each chromosome changes through the duration of @@ -8320,9 +8328,9 @@

Challenge
-
+

R @@ -8380,7 +8388,7 @@

R geom_line() + facet_grid(. ~ sex)

-

+

ggplot2 themes

@@ -8396,7 +8404,7 @@

Rggplot2 extensions website provides a list of packages that extend the capabilities of ggplot2, including additional themes.

-

Customisation +

Customisation

Let’s come back to the faceted plot of mean expression by time and @@ -8485,8 +8493,7 @@

R
-

Challenge -

+

Challenge

With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or @@ -8506,9 +8513,9 @@

Challenge
-
+

For example, based on this plot:

@@ -8589,7 +8596,7 @@

R

-

Composing plots +

Composing plots

Faceting is a great tool for splitting one plot into multiple @@ -8709,7 +8716,7 @@

R

In addition to the ncol and nrow arguments, used to make simple arrangements, there are tools for constructing more complex layouts.

-

Exporting plots +

Exporting plots

After creating your plot, you can save it to a file in your favorite @@ -8751,7 +8758,7 @@

R

Note: The parameters width and height also determine the font size in the saved plot.

-

Other packages for visualisation +

Other packages for visualisation

ggplot2 is a very powerful package that fits very nicely @@ -8826,8 +8833,7 @@

The lattice package
-

Key Points -

+

Key Points

  • Visualization in R
    • @@ -8844,8 +8850,8 @@

    Key Points

    Content from Next steps

    -

    Last updated on 2024-05-07 | - +

    Last updated on 2024-12-12 | + Edit this page

    @@ -8879,7 +8885,7 @@

    Objectives

-

Next steps +

Next steps

Data in bioinformatics is often complex. To deal with this, @@ -8893,7 +8899,7 @@

Objectives

the project as a whole.

To illustrate such an omics data container, we’ll present the SummarizedExperiment class.

-

SummarizedExperiment +

SummarizedExperiment

The figure below represents the anatomy of the SummarizedExperiment @@ -9134,8 +9140,8 @@

R

OUTPUT

-
class: SummarizedExperiment 
-dim: 1474 22 
+
class: SummarizedExperiment
+dim: 1474 22
 metadata(0):
 assays(1): counts
 rownames(1474): Asl Apod ... Lmx1a Pbx1
@@ -9341,8 +9347,8 @@ 
R
 
OUTPUT
 

-
class: SummarizedExperiment 
-dim: 5 3 
+
class: SummarizedExperiment
+dim: 5 3
 metadata(0):
 assays(1): counts
 rownames(5): Asl Apod Cyp2d22 Klk6 Fcrls
@@ -9419,8 +9425,8 @@ 
R
 
OUTPUT
 

-
class: SummarizedExperiment 
-dim: 7 7 
+
class: SummarizedExperiment
+dim: 7 7
 metadata(0):
 assays(1): counts
 rownames(7): Mir1901 Mir378a ... Mir128-1 Mir7682
@@ -9535,8 +9541,7 @@ 
OUTPUT

R
-

Challenge -

+

Challenge

Use what you just learned to create a plot that depicts how the average expression of each chromosome changes through the duration of @@ -1179,9 +1216,9 @@

Challenge
-
+

R @@ -1238,9 +1275,8 @@

R geom_line() + facet_grid(. ~ sex)

-

-ggplot2 themes -

+

+ggplot2 themes

In addition to theme_bw(), which changes the plot background to white, ggplot2 comes with several other themes which can be useful to quickly change the look of your @@ -1253,8 +1289,7 @@

Rggplot2 extensions website provides a list of packages that extend the capabilities of ggplot2, including additional themes.

-

Customisation -

+

Customisation

Let’s come back to the faceted plot of mean expression by time and gene, colored by sex.

Take a look at the ggplot2 @@ -1341,8 +1376,7 @@

R
-

Challenge -

+

Challenge

With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or @@ -1360,9 +1394,9 @@

Challenge
-
+

For example, based on this plot:

@@ -1442,8 +1476,7 @@

R

-

Composing plots -

+

Composing plots

Faceting is a great tool for splitting one plot into multiple subplots, but sometimes you may want to produce a single figure that contains multiple independent plots, i.e. plots that are based on @@ -1561,8 +1594,7 @@

R

In addition to the ncol and nrow arguments, used to make simple arrangements, there are tools for constructing more complex layouts.

-

Exporting plots -

+

Exporting plots

After creating your plot, you can save it to a file in your favorite format. The Export tab in the Plot pane in RStudio will save your plots at low resolution, which will not be accepted by many @@ -1602,8 +1634,7 @@

R

Note: The parameters width and height also determine the font size in the saved plot.

-

Other packages for visualisation -

+

Other packages for visualisation

ggplot2 is a very powerful package that fits very nicely in our tidy data and tidy tools pipeline. There are other visualization packages in R that shouldn’t be ignored.

@@ -1674,8 +1705,7 @@

The lattice package
-

Key Points -

+

Key Points

  • Visualization in R
@@ -1701,7 +1731,7 @@

Key Points

@@ -1712,20 +1742,20 @@

Key Points

This lesson is subject to the Code of Conduct

- + Edit on GitHub - + | Contributing | Source

Cite | Contact | About

- +

Materials licensed under CC-BY 4.0 by the authors

- +

Template licensed under CC-BY 4.0 by The Carpentries

-

Built with sandpaper (0.16.4), pegboard (0.7.5), and varnish (1.0.2)

+

Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)

@@ -151,7 +202,7 @@

1. Data organisation with spreadsheets - + @@ -162,7 +213,7 @@

2. R and RStudio - + @@ -173,7 +224,7 @@

3. Introduction to R - + @@ -184,7 +235,7 @@

4. Starting with data - + @@ -195,7 +246,7 @@

5. Manipulating and analysing data with dplyr - + @@ -206,7 +257,7 @@

6. Data visualization - + @@ -217,7 +268,7 @@

7. Next steps - + @@ -226,10 +277,10 @@

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See all in one page - +
See all in one page +
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Page not found

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Our apologies! -

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Our apologies!

We cannot seem to find the page you are looking for. Here are some tips that may help:

  1. try going back to the previous @@ -290,20 +340,20 @@

    Page not found

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Page not found

"url": "https://carpentries-incubator.github.io/bioc-intro/instructor/404.html", "identifier": "https://carpentries-incubator.github.io/bioc-intro/instructor/404.html", "dateCreated": "2020-09-14", - "dateModified": "2024-05-14", - "datePublished": "2024-05-14" + "dateModified": "2024-12-12", + "datePublished": "2024-12-12" } + diff --git a/instructor/60-next-steps.html b/instructor/60-next-steps.html index 6909676e..41285436 100644 --- a/instructor/60-next-steps.html +++ b/instructor/60-next-steps.html @@ -1,5 +1,5 @@ -Introduction to data analysis with R and Bioconductor: Next steps -
Introduction to data analysis with R and Bioconductor @@ -100,6 +124,33 @@
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See all in one page +
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Next steps

-

Last updated on 2024-05-07 | - +

Last updated on 2024-12-12 | + Edit this page

- - - + + +

Estimated time: 90 minutes

- +
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Objectives

-

Next steps -

+

Next steps

Data in bioinformatics is often complex. To deal with this, developers define specialised data containers (termed classes) that match the properties of the data they need to handle.

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Objectives

the project as a whole.

To illustrate such an omics data container, we’ll present the SummarizedExperiment class.

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SummarizedExperiment -

+

SummarizedExperiment

The figure below represents the anatomy of the SummarizedExperiment class.

Objects of the class SummarizedExperiment contain :

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R

OUTPUT

-
class: SummarizedExperiment 
-dim: 1474 22 
+
class: SummarizedExperiment
+dim: 1474 22
 metadata(0):
 assays(1): counts
 rownames(1474): Asl Apod ... Lmx1a Pbx1
@@ -761,8 +810,8 @@ 
R
 
OUTPUT
 

-
class: SummarizedExperiment 
-dim: 5 3 
+
class: SummarizedExperiment
+dim: 5 3
 metadata(0):
 assays(1): counts
 rownames(5): Asl Apod Cyp2d22 Klk6 Fcrls
@@ -839,8 +888,8 @@ 
R
 
OUTPUT
 

-
class: SummarizedExperiment 
-dim: 7 7 
+
class: SummarizedExperiment
+dim: 7 7
 metadata(0):
 assays(1): counts
 rownames(7): Mir1901 Mir378a ... Mir128-1 Mir7682
@@ -955,8 +1004,7 @@ 
OUTPUT

R
-

Challenge -

+

Challenge

Use what you just learned to create a plot that depicts how the average expression of each chromosome changes through the duration of @@ -8328,9 +8336,9 @@

Challenge
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R @@ -8388,7 +8396,7 @@

R geom_line() + facet_grid(. ~ sex)

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ggplot2 themes

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Rggplot2 extensions website provides a list of packages that extend the capabilities of ggplot2, including additional themes.

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Customisation +

Customisation

Let’s come back to the faceted plot of mean expression by time and @@ -8493,8 +8501,7 @@

R
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Challenge -

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Challenge

With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or @@ -8514,9 +8521,9 @@

Challenge
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For example, based on this plot:

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R

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Composing plots +

Composing plots

Faceting is a great tool for splitting one plot into multiple @@ -8717,7 +8724,7 @@

R

In addition to the ncol and nrow arguments, used to make simple arrangements, there are tools for constructing more complex layouts.

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Exporting plots +

Exporting plots

After creating your plot, you can save it to a file in your favorite @@ -8759,7 +8766,7 @@

R

Note: The parameters width and height also determine the font size in the saved plot.

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Other packages for visualisation +

Other packages for visualisation

ggplot2 is a very powerful package that fits very nicely @@ -8834,8 +8841,7 @@

The lattice package
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Key Points -

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Key Points

  • Visualization in R
    • @@ -8852,8 +8858,8 @@

    Key Points

    Content from Next steps

    -

    Last updated on 2024-05-07 | - +

    Last updated on 2024-12-12 | + Edit this page

    Estimated time: 90 minutes

    @@ -8888,7 +8894,7 @@

    Objectives

-

Next steps +

Next steps

Data in bioinformatics is often complex. To deal with this, @@ -8902,7 +8908,7 @@

Objectives

the project as a whole.

To illustrate such an omics data container, we’ll present the SummarizedExperiment class.

-

SummarizedExperiment +

SummarizedExperiment

The figure below represents the anatomy of the SummarizedExperiment @@ -9143,8 +9149,8 @@

R

OUTPUT

-
class: SummarizedExperiment 
-dim: 1474 22 
+
class: SummarizedExperiment
+dim: 1474 22
 metadata(0):
 assays(1): counts
 rownames(1474): Asl Apod ... Lmx1a Pbx1
@@ -9350,8 +9356,8 @@ 
R
 
OUTPUT
 

-
class: SummarizedExperiment 
-dim: 5 3 
+
class: SummarizedExperiment
+dim: 5 3
 metadata(0):
 assays(1): counts
 rownames(5): Asl Apod Cyp2d22 Klk6 Fcrls
@@ -9428,8 +9434,8 @@ 
R
 
OUTPUT
 

-
class: SummarizedExperiment 
-dim: 7 7 
+
class: SummarizedExperiment
+dim: 7 7
 metadata(0):
 assays(1): counts
 rownames(7): Mir1901 Mir378a ... Mir128-1 Mir7682
@@ -9544,8 +9550,7 @@ 
OUTPUT