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<li><a href="./">Computational Genomics with R</a></li>
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<li class="chapter" data-level="" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i>Preface</a><ul>
<li class="chapter" data-level="" data-path="who-is-this-book-for.html"><a href="who-is-this-book-for.html"><i class="fa fa-check"></i>Who is this book for?</a><ul>
<li class="chapter" data-level="" data-path="who-is-this-book-for.html"><a href="who-is-this-book-for.html#what-will-you-get-out-of-this"><i class="fa fa-check"></i>What will you get out of this?</a></li>
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<li class="chapter" data-level="" data-path="structure-of-the-book.html"><a href="structure-of-the-book.html"><i class="fa fa-check"></i>Structure of the book</a></li>
<li class="chapter" data-level="" data-path="software-information-and-conventions.html"><a href="software-information-and-conventions.html"><i class="fa fa-check"></i>Software information and conventions</a><ul>
<li class="chapter" data-level="" data-path="software-information-and-conventions.html"><a href="software-information-and-conventions.html#assignment-operator-convention"><i class="fa fa-check"></i>Assignment operator convention</a></li>
<li class="chapter" data-level="" data-path="software-information-and-conventions.html"><a href="software-information-and-conventions.html#packages-needed-to-run-the-book-code"><i class="fa fa-check"></i>Packages needed to run the book code</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="data-for-the-book.html"><a href="data-for-the-book.html"><i class="fa fa-check"></i>Data for the book</a></li>
<li class="chapter" data-level="" data-path="exercises-in-the-book.html"><a href="exercises-in-the-book.html"><i class="fa fa-check"></i>Exercises in the book</a></li>
<li class="chapter" data-level="" data-path="reproducibility-statement.html"><a href="reproducibility-statement.html"><i class="fa fa-check"></i>Reproducibility statement</a></li>
<li class="chapter" data-level="" data-path="acknowledgements.html"><a href="acknowledgements.html"><i class="fa fa-check"></i>Acknowledgements</a></li>
<li class="chapter" data-level="" data-path="how-to-contribute.html"><a href="how-to-contribute.html"><i class="fa fa-check"></i>How to contribute</a></li>
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<li class="chapter" data-level="" data-path="about-the-authors.html"><a href="about-the-authors.html"><i class="fa fa-check"></i>About the Authors</a></li>
<li class="chapter" data-level="1" data-path="intro.html"><a href="intro.html"><i class="fa fa-check"></i><b>1</b> Introduction to Genomics</a><ul>
<li class="chapter" data-level="1.1" data-path="genes-dna-and-central-dogma.html"><a href="genes-dna-and-central-dogma.html"><i class="fa fa-check"></i><b>1.1</b> Genes, DNA and central dogma</a><ul>
<li class="chapter" data-level="1.1.1" data-path="genes-dna-and-central-dogma.html"><a href="genes-dna-and-central-dogma.html#what-is-a-genome"><i class="fa fa-check"></i><b>1.1.1</b> What is a genome?</a></li>
<li class="chapter" data-level="1.1.2" data-path="genes-dna-and-central-dogma.html"><a href="genes-dna-and-central-dogma.html#what-is-a-gene"><i class="fa fa-check"></i><b>1.1.2</b> What is a gene?</a></li>
<li class="chapter" data-level="1.1.3" data-path="genes-dna-and-central-dogma.html"><a href="genes-dna-and-central-dogma.html#how-are-genes-controlled-transcriptional-and-post-transcriptional-regulation"><i class="fa fa-check"></i><b>1.1.3</b> How are genes controlled? Transcriptional and post-transcriptional regulation</a></li>
<li class="chapter" data-level="1.1.4" data-path="genes-dna-and-central-dogma.html"><a href="genes-dna-and-central-dogma.html#what-does-a-gene-look-like"><i class="fa fa-check"></i><b>1.1.4</b> What does a gene look like?</a></li>
</ul></li>
<li class="chapter" data-level="1.2" data-path="elements-of-gene-regulation.html"><a href="elements-of-gene-regulation.html"><i class="fa fa-check"></i><b>1.2</b> Elements of gene regulation</a><ul>
<li class="chapter" data-level="1.2.1" data-path="elements-of-gene-regulation.html"><a href="elements-of-gene-regulation.html#transcriptional-regulation"><i class="fa fa-check"></i><b>1.2.1</b> Transcriptional regulation</a></li>
<li class="chapter" data-level="1.2.2" data-path="elements-of-gene-regulation.html"><a href="elements-of-gene-regulation.html#post-transcriptional-regulation"><i class="fa fa-check"></i><b>1.2.2</b> Post-transcriptional regulation</a></li>
</ul></li>
<li class="chapter" data-level="1.3" data-path="shaping-the-genome-dna-mutation.html"><a href="shaping-the-genome-dna-mutation.html"><i class="fa fa-check"></i><b>1.3</b> Shaping the genome: DNA mutation</a></li>
<li class="chapter" data-level="1.4" data-path="high-throughput-experimental-methods-in-genomics.html"><a href="high-throughput-experimental-methods-in-genomics.html"><i class="fa fa-check"></i><b>1.4</b> High-throughput experimental methods in genomics</a><ul>
<li class="chapter" data-level="1.4.1" data-path="high-throughput-experimental-methods-in-genomics.html"><a href="high-throughput-experimental-methods-in-genomics.html#the-general-idea-behind-high-throughput-techniques"><i class="fa fa-check"></i><b>1.4.1</b> The general idea behind high-throughput techniques</a></li>
<li class="chapter" data-level="1.4.2" data-path="high-throughput-experimental-methods-in-genomics.html"><a href="high-throughput-experimental-methods-in-genomics.html#high-throughput-sequencing"><i class="fa fa-check"></i><b>1.4.2</b> High-throughput sequencing</a></li>
</ul></li>
<li class="chapter" data-level="1.5" data-path="visualization-and-data-repositories-for-genomics.html"><a href="visualization-and-data-repositories-for-genomics.html"><i class="fa fa-check"></i><b>1.5</b> Visualization and data repositories for genomics</a></li>
</ul></li>
<li class="chapter" data-level="2" data-path="Rintro.html"><a href="Rintro.html"><i class="fa fa-check"></i><b>2</b> Introduction to R for Genomic Data Analysis</a><ul>
<li class="chapter" data-level="2.1" data-path="steps-of-genomic-data-analysis.html"><a href="steps-of-genomic-data-analysis.html"><i class="fa fa-check"></i><b>2.1</b> Steps of (genomic) data analysis</a><ul>
<li class="chapter" data-level="2.1.1" data-path="steps-of-genomic-data-analysis.html"><a href="steps-of-genomic-data-analysis.html#data-collection"><i class="fa fa-check"></i><b>2.1.1</b> Data collection</a></li>
<li class="chapter" data-level="2.1.2" data-path="steps-of-genomic-data-analysis.html"><a href="steps-of-genomic-data-analysis.html#data-quality-check-and-cleaning"><i class="fa fa-check"></i><b>2.1.2</b> Data quality check and cleaning</a></li>
<li class="chapter" data-level="2.1.3" data-path="steps-of-genomic-data-analysis.html"><a href="steps-of-genomic-data-analysis.html#data-processing"><i class="fa fa-check"></i><b>2.1.3</b> Data processing</a></li>
<li class="chapter" data-level="2.1.4" data-path="steps-of-genomic-data-analysis.html"><a href="steps-of-genomic-data-analysis.html#exploratory-data-analysis-and-modeling"><i class="fa fa-check"></i><b>2.1.4</b> Exploratory data analysis and modeling</a></li>
<li class="chapter" data-level="2.1.5" data-path="steps-of-genomic-data-analysis.html"><a href="steps-of-genomic-data-analysis.html#visualization-and-reporting"><i class="fa fa-check"></i><b>2.1.5</b> Visualization and reporting</a></li>
<li class="chapter" data-level="2.1.6" data-path="steps-of-genomic-data-analysis.html"><a href="steps-of-genomic-data-analysis.html#why-use-r-for-genomics"><i class="fa fa-check"></i><b>2.1.6</b> Why use R for genomics ?</a></li>
</ul></li>
<li class="chapter" data-level="2.2" data-path="getting-started-with-r.html"><a href="getting-started-with-r.html"><i class="fa fa-check"></i><b>2.2</b> Getting started with R</a><ul>
<li class="chapter" data-level="2.2.1" data-path="getting-started-with-r.html"><a href="getting-started-with-r.html#installing-packages"><i class="fa fa-check"></i><b>2.2.1</b> Installing packages</a></li>
<li class="chapter" data-level="2.2.2" data-path="getting-started-with-r.html"><a href="getting-started-with-r.html#installing-packages-in-custom-locations"><i class="fa fa-check"></i><b>2.2.2</b> Installing packages in custom locations</a></li>
<li class="chapter" data-level="2.2.3" data-path="getting-started-with-r.html"><a href="getting-started-with-r.html#getting-help-on-functions-and-packages"><i class="fa fa-check"></i><b>2.2.3</b> Getting help on functions and packages</a></li>
</ul></li>
<li class="chapter" data-level="2.3" data-path="computations-in-r.html"><a href="computations-in-r.html"><i class="fa fa-check"></i><b>2.3</b> Computations in R</a></li>
<li class="chapter" data-level="2.4" data-path="data-structures.html"><a href="data-structures.html"><i class="fa fa-check"></i><b>2.4</b> Data structures</a><ul>
<li class="chapter" data-level="2.4.1" data-path="data-structures.html"><a href="data-structures.html#vectors"><i class="fa fa-check"></i><b>2.4.1</b> Vectors</a></li>
<li class="chapter" data-level="2.4.2" data-path="data-structures.html"><a href="data-structures.html#matrices"><i class="fa fa-check"></i><b>2.4.2</b> Matrices</a></li>
<li class="chapter" data-level="2.4.3" data-path="data-structures.html"><a href="data-structures.html#data-frames"><i class="fa fa-check"></i><b>2.4.3</b> Data frames</a></li>
<li class="chapter" data-level="2.4.4" data-path="data-structures.html"><a href="data-structures.html#lists"><i class="fa fa-check"></i><b>2.4.4</b> Lists</a></li>
<li class="chapter" data-level="2.4.5" data-path="data-structures.html"><a href="data-structures.html#factors"><i class="fa fa-check"></i><b>2.4.5</b> Factors</a></li>
</ul></li>
<li class="chapter" data-level="2.5" data-path="data-types.html"><a href="data-types.html"><i class="fa fa-check"></i><b>2.5</b> Data types</a></li>
<li class="chapter" data-level="2.6" data-path="reading-and-writing-data.html"><a href="reading-and-writing-data.html"><i class="fa fa-check"></i><b>2.6</b> Reading and writing data</a><ul>
<li class="chapter" data-level="2.6.1" data-path="reading-and-writing-data.html"><a href="reading-and-writing-data.html#reading-large-files"><i class="fa fa-check"></i><b>2.6.1</b> Reading large files</a></li>
</ul></li>
<li class="chapter" data-level="2.7" data-path="plotting-in-r-with-base-graphics.html"><a href="plotting-in-r-with-base-graphics.html"><i class="fa fa-check"></i><b>2.7</b> Plotting in R with base graphics</a><ul>
<li class="chapter" data-level="2.7.1" data-path="plotting-in-r-with-base-graphics.html"><a href="plotting-in-r-with-base-graphics.html#combining-multiple-plots"><i class="fa fa-check"></i><b>2.7.1</b> Combining multiple plots</a></li>
<li class="chapter" data-level="2.7.2" data-path="plotting-in-r-with-base-graphics.html"><a href="plotting-in-r-with-base-graphics.html#saving-plots"><i class="fa fa-check"></i><b>2.7.2</b> Saving plots</a></li>
</ul></li>
<li class="chapter" data-level="2.8" data-path="plotting-in-r-with-ggplot2.html"><a href="plotting-in-r-with-ggplot2.html"><i class="fa fa-check"></i><b>2.8</b> Plotting in R with ggplot2</a><ul>
<li class="chapter" data-level="2.8.1" data-path="plotting-in-r-with-ggplot2.html"><a href="plotting-in-r-with-ggplot2.html#combining-multiple-plots-1"><i class="fa fa-check"></i><b>2.8.1</b> Combining multiple plots</a></li>
<li class="chapter" data-level="2.8.2" data-path="plotting-in-r-with-ggplot2.html"><a href="plotting-in-r-with-ggplot2.html#ggplot2-and-tidyverse"><i class="fa fa-check"></i><b>2.8.2</b> ggplot2 and tidyverse</a></li>
</ul></li>
<li class="chapter" data-level="2.9" data-path="functions-and-control-structures-for-ifelse-etc-.html"><a href="functions-and-control-structures-for-ifelse-etc-.html"><i class="fa fa-check"></i><b>2.9</b> Functions and control structures (for, if/else etc.)</a><ul>
<li class="chapter" data-level="2.9.1" data-path="functions-and-control-structures-for-ifelse-etc-.html"><a href="functions-and-control-structures-for-ifelse-etc-.html#user-defined-functions"><i class="fa fa-check"></i><b>2.9.1</b> User-defined functions</a></li>
<li class="chapter" data-level="2.9.2" data-path="functions-and-control-structures-for-ifelse-etc-.html"><a href="functions-and-control-structures-for-ifelse-etc-.html#loops-and-looping-structures-in-r"><i class="fa fa-check"></i><b>2.9.2</b> Loops and looping structures in R</a></li>
</ul></li>
<li class="chapter" data-level="2.10" data-path="exercises.html"><a href="exercises.html"><i class="fa fa-check"></i><b>2.10</b> Exercises</a><ul>
<li class="chapter" data-level="2.10.1" data-path="exercises.html"><a href="exercises.html#computations-in-r-1"><i class="fa fa-check"></i><b>2.10.1</b> Computations in R</a></li>
<li class="chapter" data-level="2.10.2" data-path="exercises.html"><a href="exercises.html#data-structures-in-r"><i class="fa fa-check"></i><b>2.10.2</b> Data structures in R</a></li>
<li class="chapter" data-level="2.10.3" data-path="exercises.html"><a href="exercises.html#reading-in-and-writing-data-out-in-r"><i class="fa fa-check"></i><b>2.10.3</b> Reading in and writing data out in R</a></li>
<li class="chapter" data-level="2.10.4" data-path="exercises.html"><a href="exercises.html#plotting-in-r"><i class="fa fa-check"></i><b>2.10.4</b> Plotting in R</a></li>
<li class="chapter" data-level="2.10.5" data-path="exercises.html"><a href="exercises.html#functions-and-control-structures-for-ifelse-etc.-1"><i class="fa fa-check"></i><b>2.10.5</b> Functions and control structures (for, if/else, etc.)</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="3" data-path="stats.html"><a href="stats.html"><i class="fa fa-check"></i><b>3</b> Statistics for Genomics</a><ul>
<li class="chapter" data-level="3.1" data-path="how-to-summarize-collection-of-data-points-the-idea-behind-statistical-distributions.html"><a href="how-to-summarize-collection-of-data-points-the-idea-behind-statistical-distributions.html"><i class="fa fa-check"></i><b>3.1</b> How to summarize collection of data points: The idea behind statistical distributions</a><ul>
<li class="chapter" data-level="3.1.1" data-path="how-to-summarize-collection-of-data-points-the-idea-behind-statistical-distributions.html"><a href="how-to-summarize-collection-of-data-points-the-idea-behind-statistical-distributions.html#describing-the-central-tendency-mean-and-median"><i class="fa fa-check"></i><b>3.1.1</b> Describing the central tendency: Mean and median</a></li>
<li class="chapter" data-level="3.1.2" data-path="how-to-summarize-collection-of-data-points-the-idea-behind-statistical-distributions.html"><a href="how-to-summarize-collection-of-data-points-the-idea-behind-statistical-distributions.html#describing-the-spread-measurements-of-variation"><i class="fa fa-check"></i><b>3.1.2</b> Describing the spread: Measurements of variation</a></li>
<li class="chapter" data-level="3.1.3" data-path="how-to-summarize-collection-of-data-points-the-idea-behind-statistical-distributions.html"><a href="how-to-summarize-collection-of-data-points-the-idea-behind-statistical-distributions.html#precision-of-estimates-confidence-intervals"><i class="fa fa-check"></i><b>3.1.3</b> Precision of estimates: Confidence intervals</a></li>
</ul></li>
<li class="chapter" data-level="3.2" data-path="how-to-test-for-differences-between-samples.html"><a href="how-to-test-for-differences-between-samples.html"><i class="fa fa-check"></i><b>3.2</b> How to test for differences between samples</a><ul>
<li class="chapter" data-level="3.2.1" data-path="how-to-test-for-differences-between-samples.html"><a href="how-to-test-for-differences-between-samples.html#randomization-based-testing-for-difference-of-the-means"><i class="fa fa-check"></i><b>3.2.1</b> Randomization-based testing for difference of the means</a></li>
<li class="chapter" data-level="3.2.2" data-path="how-to-test-for-differences-between-samples.html"><a href="how-to-test-for-differences-between-samples.html#using-t-test-for-difference-of-the-means-between-two-samples"><i class="fa fa-check"></i><b>3.2.2</b> Using t-test for difference of the means between two samples</a></li>
<li class="chapter" data-level="3.2.3" data-path="how-to-test-for-differences-between-samples.html"><a href="how-to-test-for-differences-between-samples.html#multiple-testing-correction"><i class="fa fa-check"></i><b>3.2.3</b> Multiple testing correction</a></li>
<li class="chapter" data-level="3.2.4" data-path="how-to-test-for-differences-between-samples.html"><a href="how-to-test-for-differences-between-samples.html#moderated-t-tests-using-information-from-multiple-comparisons"><i class="fa fa-check"></i><b>3.2.4</b> Moderated t-tests: Using information from multiple comparisons</a></li>
</ul></li>
<li class="chapter" data-level="3.3" data-path="relationship-between-variables-linear-models-and-correlation.html"><a href="relationship-between-variables-linear-models-and-correlation.html"><i class="fa fa-check"></i><b>3.3</b> Relationship between variables: Linear models and correlation</a><ul>
<li class="chapter" data-level="3.3.1" data-path="relationship-between-variables-linear-models-and-correlation.html"><a href="relationship-between-variables-linear-models-and-correlation.html#how-to-fit-a-line"><i class="fa fa-check"></i><b>3.3.1</b> How to fit a line</a></li>
<li class="chapter" data-level="3.3.2" data-path="relationship-between-variables-linear-models-and-correlation.html"><a href="relationship-between-variables-linear-models-and-correlation.html#how-to-estimate-the-error-of-the-coefficients"><i class="fa fa-check"></i><b>3.3.2</b> How to estimate the error of the coefficients</a></li>
<li class="chapter" data-level="3.3.3" data-path="relationship-between-variables-linear-models-and-correlation.html"><a href="relationship-between-variables-linear-models-and-correlation.html#accuracy-of-the-model"><i class="fa fa-check"></i><b>3.3.3</b> Accuracy of the model</a></li>
<li class="chapter" data-level="3.3.4" data-path="relationship-between-variables-linear-models-and-correlation.html"><a href="relationship-between-variables-linear-models-and-correlation.html#regression-with-categorical-variables"><i class="fa fa-check"></i><b>3.3.4</b> Regression with categorical variables</a></li>
<li class="chapter" data-level="3.3.5" data-path="relationship-between-variables-linear-models-and-correlation.html"><a href="relationship-between-variables-linear-models-and-correlation.html#regression-pitfalls"><i class="fa fa-check"></i><b>3.3.5</b> Regression pitfalls</a></li>
</ul></li>
<li class="chapter" data-level="3.4" data-path="exercises-1.html"><a href="exercises-1.html"><i class="fa fa-check"></i><b>3.4</b> Exercises</a><ul>
<li class="chapter" data-level="3.4.1" data-path="exercises-1.html"><a href="exercises-1.html#how-to-summarize-collection-of-data-points-the-idea-behind-statistical-distributions-1"><i class="fa fa-check"></i><b>3.4.1</b> How to summarize collection of data points: The idea behind statistical distributions</a></li>
<li class="chapter" data-level="3.4.2" data-path="exercises-1.html"><a href="exercises-1.html#how-to-test-for-differences-in-samples"><i class="fa fa-check"></i><b>3.4.2</b> How to test for differences in samples</a></li>
<li class="chapter" data-level="3.4.3" data-path="exercises-1.html"><a href="exercises-1.html#relationship-between-variables-linear-models-and-correlation-1"><i class="fa fa-check"></i><b>3.4.3</b> Relationship between variables: Linear models and correlation</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="4" data-path="unsupervisedLearning.html"><a href="unsupervisedLearning.html"><i class="fa fa-check"></i><b>4</b> Exploratory Data Analysis with Unsupervised Machine Learning</a><ul>
<li class="chapter" data-level="4.1" data-path="clustering-grouping-samples-based-on-their-similarity.html"><a href="clustering-grouping-samples-based-on-their-similarity.html"><i class="fa fa-check"></i><b>4.1</b> Clustering: Grouping samples based on their similarity</a><ul>
<li class="chapter" data-level="4.1.1" data-path="clustering-grouping-samples-based-on-their-similarity.html"><a href="clustering-grouping-samples-based-on-their-similarity.html#distance-metrics"><i class="fa fa-check"></i><b>4.1.1</b> Distance metrics</a></li>
<li class="chapter" data-level="4.1.2" data-path="clustering-grouping-samples-based-on-their-similarity.html"><a href="clustering-grouping-samples-based-on-their-similarity.html#hiearchical-clustering"><i class="fa fa-check"></i><b>4.1.2</b> Hiearchical clustering</a></li>
<li class="chapter" data-level="4.1.3" data-path="clustering-grouping-samples-based-on-their-similarity.html"><a href="clustering-grouping-samples-based-on-their-similarity.html#k-means-clustering"><i class="fa fa-check"></i><b>4.1.3</b> K-means clustering</a></li>
<li class="chapter" data-level="4.1.4" data-path="clustering-grouping-samples-based-on-their-similarity.html"><a href="clustering-grouping-samples-based-on-their-similarity.html#how-to-choose-k-the-number-of-clusters"><i class="fa fa-check"></i><b>4.1.4</b> How to choose “k”, the number of clusters</a></li>
</ul></li>
<li class="chapter" data-level="4.2" data-path="dimensionality-reduction-techniques-visualizing-complex-data-sets-in-2d.html"><a href="dimensionality-reduction-techniques-visualizing-complex-data-sets-in-2d.html"><i class="fa fa-check"></i><b>4.2</b> Dimensionality reduction techniques: Visualizing complex data sets in 2D</a><ul>
<li class="chapter" data-level="4.2.1" data-path="dimensionality-reduction-techniques-visualizing-complex-data-sets-in-2d.html"><a href="dimensionality-reduction-techniques-visualizing-complex-data-sets-in-2d.html#principal-component-analysis"><i class="fa fa-check"></i><b>4.2.1</b> Principal component analysis</a></li>
<li class="chapter" data-level="4.2.2" data-path="dimensionality-reduction-techniques-visualizing-complex-data-sets-in-2d.html"><a href="dimensionality-reduction-techniques-visualizing-complex-data-sets-in-2d.html#other-matrix-factorization-methods-for-dimensionality-reduction"><i class="fa fa-check"></i><b>4.2.2</b> Other matrix factorization methods for dimensionality reduction</a></li>
<li class="chapter" data-level="4.2.3" data-path="dimensionality-reduction-techniques-visualizing-complex-data-sets-in-2d.html"><a href="dimensionality-reduction-techniques-visualizing-complex-data-sets-in-2d.html#multi-dimensional-scaling"><i class="fa fa-check"></i><b>4.2.3</b> Multi-dimensional scaling</a></li>
<li class="chapter" data-level="4.2.4" data-path="dimensionality-reduction-techniques-visualizing-complex-data-sets-in-2d.html"><a href="dimensionality-reduction-techniques-visualizing-complex-data-sets-in-2d.html#t-distributed-stochastic-neighbor-embedding-t-sne"><i class="fa fa-check"></i><b>4.2.4</b> t-Distributed Stochastic Neighbor Embedding (t-SNE)</a></li>
</ul></li>
<li class="chapter" data-level="4.3" data-path="exercises-2.html"><a href="exercises-2.html"><i class="fa fa-check"></i><b>4.3</b> Exercises</a><ul>
<li class="chapter" data-level="4.3.1" data-path="exercises-2.html"><a href="exercises-2.html#clustering"><i class="fa fa-check"></i><b>4.3.1</b> Clustering</a></li>
<li class="chapter" data-level="4.3.2" data-path="exercises-2.html"><a href="exercises-2.html#dimension-reduction"><i class="fa fa-check"></i><b>4.3.2</b> Dimension reduction</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="5" data-path="supervisedLearning.html"><a href="supervisedLearning.html"><i class="fa fa-check"></i><b>5</b> Predictive Modeling with Supervised Machine Learning</a><ul>
<li class="chapter" data-level="5.1" data-path="how-are-machine-learning-models-fit.html"><a href="how-are-machine-learning-models-fit.html"><i class="fa fa-check"></i><b>5.1</b> How are machine learning models fit?</a><ul>
<li class="chapter" data-level="5.1.1" data-path="how-are-machine-learning-models-fit.html"><a href="how-are-machine-learning-models-fit.html#machine-learning-vs.-statistics"><i class="fa fa-check"></i><b>5.1.1</b> Machine learning vs. statistics</a></li>
</ul></li>
<li class="chapter" data-level="5.2" data-path="steps-in-supervised-machine-learning.html"><a href="steps-in-supervised-machine-learning.html"><i class="fa fa-check"></i><b>5.2</b> Steps in supervised machine learning</a></li>
<li class="chapter" data-level="5.3" data-path="use-case-disease-subtype-from-genomics-data.html"><a href="use-case-disease-subtype-from-genomics-data.html"><i class="fa fa-check"></i><b>5.3</b> Use case: Disease subtype from genomics data</a></li>
<li class="chapter" data-level="5.4" data-path="data-preprocessing.html"><a href="data-preprocessing.html"><i class="fa fa-check"></i><b>5.4</b> Data preprocessing</a><ul>
<li class="chapter" data-level="5.4.1" data-path="data-preprocessing.html"><a href="data-preprocessing.html#data-transformation"><i class="fa fa-check"></i><b>5.4.1</b> Data transformation</a></li>
<li class="chapter" data-level="5.4.2" data-path="data-preprocessing.html"><a href="data-preprocessing.html#filtering-data-and-scaling"><i class="fa fa-check"></i><b>5.4.2</b> Filtering data and scaling</a></li>
<li class="chapter" data-level="5.4.3" data-path="data-preprocessing.html"><a href="data-preprocessing.html#dealing-with-missing-values"><i class="fa fa-check"></i><b>5.4.3</b> Dealing with missing values</a></li>
</ul></li>
<li class="chapter" data-level="5.5" data-path="splitting-the-data.html"><a href="splitting-the-data.html"><i class="fa fa-check"></i><b>5.5</b> Splitting the data</a><ul>
<li class="chapter" data-level="5.5.1" data-path="splitting-the-data.html"><a href="splitting-the-data.html#holdout-test-dataset"><i class="fa fa-check"></i><b>5.5.1</b> Holdout test dataset</a></li>
<li class="chapter" data-level="5.5.2" data-path="splitting-the-data.html"><a href="splitting-the-data.html#cross-validation"><i class="fa fa-check"></i><b>5.5.2</b> Cross-validation</a></li>
<li class="chapter" data-level="5.5.3" data-path="splitting-the-data.html"><a href="splitting-the-data.html#bootstrap-resampling"><i class="fa fa-check"></i><b>5.5.3</b> Bootstrap resampling</a></li>
</ul></li>
<li class="chapter" data-level="5.6" data-path="predicting-the-subtype-with-k-nearest-neighbors.html"><a href="predicting-the-subtype-with-k-nearest-neighbors.html"><i class="fa fa-check"></i><b>5.6</b> Predicting the subtype with k-nearest neighbors</a></li>
<li class="chapter" data-level="5.7" data-path="assessing-the-performance-of-our-model.html"><a href="assessing-the-performance-of-our-model.html"><i class="fa fa-check"></i><b>5.7</b> Assessing the performance of our model</a><ul>
<li class="chapter" data-level="5.7.1" data-path="assessing-the-performance-of-our-model.html"><a href="assessing-the-performance-of-our-model.html#receiver-operating-characteristic-roc-curves"><i class="fa fa-check"></i><b>5.7.1</b> Receiver Operating Characteristic (ROC) curves</a></li>
</ul></li>
<li class="chapter" data-level="5.8" data-path="model-tuning-and-avoiding-overfitting.html"><a href="model-tuning-and-avoiding-overfitting.html"><i class="fa fa-check"></i><b>5.8</b> Model tuning and avoiding overfitting</a><ul>
<li class="chapter" data-level="5.8.1" data-path="model-tuning-and-avoiding-overfitting.html"><a href="model-tuning-and-avoiding-overfitting.html#model-complexity-and-bias-variance-trade-off"><i class="fa fa-check"></i><b>5.8.1</b> Model complexity and bias variance trade-off</a></li>
<li class="chapter" data-level="5.8.2" data-path="model-tuning-and-avoiding-overfitting.html"><a href="model-tuning-and-avoiding-overfitting.html#data-split-strategies-for-model-tuning-and-testing"><i class="fa fa-check"></i><b>5.8.2</b> Data split strategies for model tuning and testing</a></li>
</ul></li>
<li class="chapter" data-level="5.9" data-path="variable-importance.html"><a href="variable-importance.html"><i class="fa fa-check"></i><b>5.9</b> Variable importance</a></li>
<li class="chapter" data-level="5.10" data-path="how-to-deal-with-class-imbalance.html"><a href="how-to-deal-with-class-imbalance.html"><i class="fa fa-check"></i><b>5.10</b> How to deal with class imbalance</a><ul>
<li class="chapter" data-level="5.10.1" data-path="how-to-deal-with-class-imbalance.html"><a href="how-to-deal-with-class-imbalance.html#sampling-for-class-balance"><i class="fa fa-check"></i><b>5.10.1</b> Sampling for class balance</a></li>
<li class="chapter" data-level="5.10.2" data-path="how-to-deal-with-class-imbalance.html"><a href="how-to-deal-with-class-imbalance.html#altering-case-weights"><i class="fa fa-check"></i><b>5.10.2</b> Altering case weights</a></li>
<li class="chapter" data-level="5.10.3" data-path="how-to-deal-with-class-imbalance.html"><a href="how-to-deal-with-class-imbalance.html#selecting-different-classification-score-cutoffs"><i class="fa fa-check"></i><b>5.10.3</b> Selecting different classification score cutoffs</a></li>
</ul></li>
<li class="chapter" data-level="5.11" data-path="dealing-with-correlated-predictors.html"><a href="dealing-with-correlated-predictors.html"><i class="fa fa-check"></i><b>5.11</b> Dealing with correlated predictors</a></li>
<li class="chapter" data-level="5.12" data-path="trees-and-forests-random-forests-in-action.html"><a href="trees-and-forests-random-forests-in-action.html"><i class="fa fa-check"></i><b>5.12</b> Trees and forests: Random forests in action</a><ul>
<li class="chapter" data-level="5.12.1" data-path="trees-and-forests-random-forests-in-action.html"><a href="trees-and-forests-random-forests-in-action.html#decision-trees"><i class="fa fa-check"></i><b>5.12.1</b> Decision trees</a></li>
<li class="chapter" data-level="5.12.2" data-path="trees-and-forests-random-forests-in-action.html"><a href="trees-and-forests-random-forests-in-action.html#trees-to-forests"><i class="fa fa-check"></i><b>5.12.2</b> Trees to forests</a></li>
<li class="chapter" data-level="5.12.3" data-path="trees-and-forests-random-forests-in-action.html"><a href="trees-and-forests-random-forests-in-action.html#variable-importance-1"><i class="fa fa-check"></i><b>5.12.3</b> Variable importance</a></li>
</ul></li>
<li class="chapter" data-level="5.13" data-path="logistic-regression-and-regularization.html"><a href="logistic-regression-and-regularization.html"><i class="fa fa-check"></i><b>5.13</b> Logistic regression and regularization</a><ul>
<li class="chapter" data-level="5.13.1" data-path="logistic-regression-and-regularization.html"><a href="logistic-regression-and-regularization.html#regularization-in-order-to-avoid-overfitting"><i class="fa fa-check"></i><b>5.13.1</b> Regularization in order to avoid overfitting</a></li>
<li class="chapter" data-level="5.13.2" data-path="logistic-regression-and-regularization.html"><a href="logistic-regression-and-regularization.html#variable-importance-2"><i class="fa fa-check"></i><b>5.13.2</b> Variable importance</a></li>
</ul></li>
<li class="chapter" data-level="5.14" data-path="other-supervised-algorithms.html"><a href="other-supervised-algorithms.html"><i class="fa fa-check"></i><b>5.14</b> Other supervised algorithms</a><ul>
<li class="chapter" data-level="5.14.1" data-path="other-supervised-algorithms.html"><a href="other-supervised-algorithms.html#gradient-boosting"><i class="fa fa-check"></i><b>5.14.1</b> Gradient boosting</a></li>
<li class="chapter" data-level="5.14.2" data-path="other-supervised-algorithms.html"><a href="other-supervised-algorithms.html#support-vector-machines-svm"><i class="fa fa-check"></i><b>5.14.2</b> Support Vector Machines (SVM)</a></li>
<li class="chapter" data-level="5.14.3" data-path="other-supervised-algorithms.html"><a href="other-supervised-algorithms.html#neural-networks-and-deep-versions-of-it"><i class="fa fa-check"></i><b>5.14.3</b> Neural networks and deep versions of it</a></li>
<li class="chapter" data-level="5.14.4" data-path="other-supervised-algorithms.html"><a href="other-supervised-algorithms.html#ensemble-learning"><i class="fa fa-check"></i><b>5.14.4</b> Ensemble learning</a></li>
</ul></li>
<li class="chapter" data-level="5.15" data-path="predicting-continuous-variables-regression-with-machine-learning.html"><a href="predicting-continuous-variables-regression-with-machine-learning.html"><i class="fa fa-check"></i><b>5.15</b> Predicting continuous variables: Regression with machine learning</a><ul>
<li class="chapter" data-level="5.15.1" data-path="predicting-continuous-variables-regression-with-machine-learning.html"><a href="predicting-continuous-variables-regression-with-machine-learning.html#use-case-predicting-age-from-dna-methylation"><i class="fa fa-check"></i><b>5.15.1</b> Use case: Predicting age from DNA methylation</a></li>
<li class="chapter" data-level="5.15.2" data-path="predicting-continuous-variables-regression-with-machine-learning.html"><a href="predicting-continuous-variables-regression-with-machine-learning.html#reading-and-processing-the-data"><i class="fa fa-check"></i><b>5.15.2</b> Reading and processing the data</a></li>
<li class="chapter" data-level="5.15.3" data-path="predicting-continuous-variables-regression-with-machine-learning.html"><a href="predicting-continuous-variables-regression-with-machine-learning.html#running-random-forest-regression"><i class="fa fa-check"></i><b>5.15.3</b> Running random forest regression</a></li>
</ul></li>
<li class="chapter" data-level="5.16" data-path="exercises-3.html"><a href="exercises-3.html"><i class="fa fa-check"></i><b>5.16</b> Exercises</a><ul>
<li class="chapter" data-level="5.16.1" data-path="exercises-3.html"><a href="exercises-3.html#classification"><i class="fa fa-check"></i><b>5.16.1</b> Classification</a></li>
<li class="chapter" data-level="5.16.2" data-path="exercises-3.html"><a href="exercises-3.html#regression"><i class="fa fa-check"></i><b>5.16.2</b> Regression</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="6" data-path="genomicIntervals.html"><a href="genomicIntervals.html"><i class="fa fa-check"></i><b>6</b> Operations on Genomic Intervals and Genome Arithmetic</a><ul>
<li class="chapter" data-level="6.1" data-path="operations-on-genomic-intervals-with-genomicranges-package.html"><a href="operations-on-genomic-intervals-with-genomicranges-package.html"><i class="fa fa-check"></i><b>6.1</b> Operations on genomic intervals with <code>GenomicRanges</code> package</a><ul>
<li class="chapter" data-level="6.1.1" data-path="operations-on-genomic-intervals-with-genomicranges-package.html"><a href="operations-on-genomic-intervals-with-genomicranges-package.html#how-to-create-and-manipulate-a-granges-object"><i class="fa fa-check"></i><b>6.1.1</b> How to create and manipulate a GRanges object</a></li>
<li class="chapter" data-level="6.1.2" data-path="operations-on-genomic-intervals-with-genomicranges-package.html"><a href="operations-on-genomic-intervals-with-genomicranges-package.html#getting-genomic-regions-into-r-as-granges-objects"><i class="fa fa-check"></i><b>6.1.2</b> Getting genomic regions into R as GRanges objects</a></li>
<li class="chapter" data-level="6.1.3" data-path="operations-on-genomic-intervals-with-genomicranges-package.html"><a href="operations-on-genomic-intervals-with-genomicranges-package.html#finding-regions-that-dodo-not-overlap-with-another-set-of-regions"><i class="fa fa-check"></i><b>6.1.3</b> Finding regions that do/do not overlap with another set of regions</a></li>
</ul></li>
<li class="chapter" data-level="6.2" data-path="dealing-with-mapped-high-throughput-sequencing-reads.html"><a href="dealing-with-mapped-high-throughput-sequencing-reads.html"><i class="fa fa-check"></i><b>6.2</b> Dealing with mapped high-throughput sequencing reads</a><ul>
<li class="chapter" data-level="6.2.1" data-path="dealing-with-mapped-high-throughput-sequencing-reads.html"><a href="dealing-with-mapped-high-throughput-sequencing-reads.html#counting-mapped-reads-for-a-set-of-regions"><i class="fa fa-check"></i><b>6.2.1</b> Counting mapped reads for a set of regions</a></li>
</ul></li>
<li class="chapter" data-level="6.3" data-path="dealing-with-continuous-scores-over-the-genome.html"><a href="dealing-with-continuous-scores-over-the-genome.html"><i class="fa fa-check"></i><b>6.3</b> Dealing with continuous scores over the genome</a><ul>
<li class="chapter" data-level="6.3.1" data-path="dealing-with-continuous-scores-over-the-genome.html"><a href="dealing-with-continuous-scores-over-the-genome.html#extracting-subsections-of-rle-and-rlelist-objects"><i class="fa fa-check"></i><b>6.3.1</b> Extracting subsections of Rle and RleList objects</a></li>
</ul></li>
<li class="chapter" data-level="6.4" data-path="genomic-intervals-with-more-information-summarizedexperiment-class.html"><a href="genomic-intervals-with-more-information-summarizedexperiment-class.html"><i class="fa fa-check"></i><b>6.4</b> Genomic intervals with more information: SummarizedExperiment class</a><ul>
<li class="chapter" data-level="6.4.1" data-path="genomic-intervals-with-more-information-summarizedexperiment-class.html"><a href="genomic-intervals-with-more-information-summarizedexperiment-class.html#create-a-summarizedexperiment-object"><i class="fa fa-check"></i><b>6.4.1</b> Create a SummarizedExperiment object</a></li>
<li class="chapter" data-level="6.4.2" data-path="genomic-intervals-with-more-information-summarizedexperiment-class.html"><a href="genomic-intervals-with-more-information-summarizedexperiment-class.html#subset-and-manipulate-the-summarizedexperiment-object"><i class="fa fa-check"></i><b>6.4.2</b> Subset and manipulate the SummarizedExperiment object</a></li>
</ul></li>
<li class="chapter" data-level="6.5" data-path="visualizing-and-summarizing-genomic-intervals.html"><a href="visualizing-and-summarizing-genomic-intervals.html"><i class="fa fa-check"></i><b>6.5</b> Visualizing and summarizing genomic intervals</a><ul>
<li class="chapter" data-level="6.5.1" data-path="visualizing-and-summarizing-genomic-intervals.html"><a href="visualizing-and-summarizing-genomic-intervals.html#visualizing-intervals-on-a-locus-of-interest"><i class="fa fa-check"></i><b>6.5.1</b> Visualizing intervals on a locus of interest</a></li>
<li class="chapter" data-level="6.5.2" data-path="visualizing-and-summarizing-genomic-intervals.html"><a href="visualizing-and-summarizing-genomic-intervals.html#summaries-of-genomic-intervals-on-multiple-loci"><i class="fa fa-check"></i><b>6.5.2</b> Summaries of genomic intervals on multiple loci</a></li>
<li class="chapter" data-level="6.5.3" data-path="visualizing-and-summarizing-genomic-intervals.html"><a href="visualizing-and-summarizing-genomic-intervals.html#making-karyograms-and-circos-plots"><i class="fa fa-check"></i><b>6.5.3</b> Making karyograms and circos plots</a></li>
</ul></li>
<li class="chapter" data-level="6.6" data-path="exercises-4.html"><a href="exercises-4.html"><i class="fa fa-check"></i><b>6.6</b> Exercises</a><ul>
<li class="chapter" data-level="6.6.1" data-path="exercises-4.html"><a href="exercises-4.html#operations-on-genomic-intervals-with-the-genomicranges-package"><i class="fa fa-check"></i><b>6.6.1</b> Operations on genomic intervals with the <code>GenomicRanges</code> package</a></li>
<li class="chapter" data-level="6.6.2" data-path="exercises-4.html"><a href="exercises-4.html#dealing-with-mapped-high-throughput-sequencing-reads-1"><i class="fa fa-check"></i><b>6.6.2</b> Dealing with mapped high-throughput sequencing reads</a></li>
<li class="chapter" data-level="6.6.3" data-path="exercises-4.html"><a href="exercises-4.html#dealing-with-contiguous-scores-over-the-genome"><i class="fa fa-check"></i><b>6.6.3</b> Dealing with contiguous scores over the genome</a></li>
<li class="chapter" data-level="6.6.4" data-path="exercises-4.html"><a href="exercises-4.html#visualizing-and-summarizing-genomic-intervals-1"><i class="fa fa-check"></i><b>6.6.4</b> Visualizing and summarizing genomic intervals</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="7" data-path="processingReads.html"><a href="processingReads.html"><i class="fa fa-check"></i><b>7</b> Quality Check, Processing and Alignment of High-throughput Sequencing Reads</a><ul>
<li class="chapter" data-level="7.1" data-path="fasta-and-fastq-formats.html"><a href="fasta-and-fastq-formats.html"><i class="fa fa-check"></i><b>7.1</b> FASTA and FASTQ formats</a></li>
<li class="chapter" data-level="7.2" data-path="quality-check-on-sequencing-reads.html"><a href="quality-check-on-sequencing-reads.html"><i class="fa fa-check"></i><b>7.2</b> Quality check on sequencing reads</a><ul>
<li class="chapter" data-level="7.2.1" data-path="quality-check-on-sequencing-reads.html"><a href="quality-check-on-sequencing-reads.html#sequence-quality-per-basecycle"><i class="fa fa-check"></i><b>7.2.1</b> Sequence quality per base/cycle</a></li>
<li class="chapter" data-level="7.2.2" data-path="quality-check-on-sequencing-reads.html"><a href="quality-check-on-sequencing-reads.html#sequence-content-per-basecycle"><i class="fa fa-check"></i><b>7.2.2</b> Sequence content per base/cycle</a></li>
<li class="chapter" data-level="7.2.3" data-path="quality-check-on-sequencing-reads.html"><a href="quality-check-on-sequencing-reads.html#read-frequency-plot"><i class="fa fa-check"></i><b>7.2.3</b> Read frequency plot</a></li>
<li class="chapter" data-level="7.2.4" data-path="quality-check-on-sequencing-reads.html"><a href="quality-check-on-sequencing-reads.html#other-quality-metrics-and-qc-tools"><i class="fa fa-check"></i><b>7.2.4</b> Other quality metrics and QC tools</a></li>
</ul></li>
<li class="chapter" data-level="7.3" data-path="filtering-and-trimming-reads.html"><a href="filtering-and-trimming-reads.html"><i class="fa fa-check"></i><b>7.3</b> Filtering and trimming reads</a></li>
<li class="chapter" data-level="7.4" data-path="mappingaligning-reads-to-the-genome.html"><a href="mappingaligning-reads-to-the-genome.html"><i class="fa fa-check"></i><b>7.4</b> Mapping/aligning reads to the genome</a></li>
<li class="chapter" data-level="7.5" data-path="further-processing-of-aligned-reads.html"><a href="further-processing-of-aligned-reads.html"><i class="fa fa-check"></i><b>7.5</b> Further processing of aligned reads</a></li>
<li class="chapter" data-level="7.6" data-path="exercises-5.html"><a href="exercises-5.html"><i class="fa fa-check"></i><b>7.6</b> Exercises</a></li>
</ul></li>
<li class="chapter" data-level="8" data-path="rnaseqanalysis.html"><a href="rnaseqanalysis.html"><i class="fa fa-check"></i><b>8</b> RNA-seq Analysis</a><ul>
<li class="chapter" data-level="8.1" data-path="what-is-gene-expression.html"><a href="what-is-gene-expression.html"><i class="fa fa-check"></i><b>8.1</b> What is gene expression?</a></li>
<li class="chapter" data-level="8.2" data-path="methods-to-detect-gene-expression.html"><a href="methods-to-detect-gene-expression.html"><i class="fa fa-check"></i><b>8.2</b> Methods to detect gene expression</a></li>
<li class="chapter" data-level="8.3" data-path="gene-expression-analysis-using-high-throughput-sequencing-technologies.html"><a href="gene-expression-analysis-using-high-throughput-sequencing-technologies.html"><i class="fa fa-check"></i><b>8.3</b> Gene expression analysis using high-throughput sequencing technologies</a><ul>
<li class="chapter" data-level="8.3.1" data-path="gene-expression-analysis-using-high-throughput-sequencing-technologies.html"><a href="gene-expression-analysis-using-high-throughput-sequencing-technologies.html#processing-raw-data"><i class="fa fa-check"></i><b>8.3.1</b> Processing raw data</a></li>
<li class="chapter" data-level="8.3.2" data-path="gene-expression-analysis-using-high-throughput-sequencing-technologies.html"><a href="gene-expression-analysis-using-high-throughput-sequencing-technologies.html#alignment"><i class="fa fa-check"></i><b>8.3.2</b> Alignment</a></li>
<li class="chapter" data-level="8.3.3" data-path="gene-expression-analysis-using-high-throughput-sequencing-technologies.html"><a href="gene-expression-analysis-using-high-throughput-sequencing-technologies.html#quantification"><i class="fa fa-check"></i><b>8.3.3</b> Quantification</a></li>
<li class="chapter" data-level="8.3.4" data-path="gene-expression-analysis-using-high-throughput-sequencing-technologies.html"><a href="gene-expression-analysis-using-high-throughput-sequencing-technologies.html#within-sample-normalization-of-the-read-counts"><i class="fa fa-check"></i><b>8.3.4</b> Within sample normalization of the read counts</a></li>
<li class="chapter" data-level="8.3.5" data-path="gene-expression-analysis-using-high-throughput-sequencing-technologies.html"><a href="gene-expression-analysis-using-high-throughput-sequencing-technologies.html#computing-different-normalization-schemes-in-r"><i class="fa fa-check"></i><b>8.3.5</b> Computing different normalization schemes in R</a></li>
<li class="chapter" data-level="8.3.6" data-path="gene-expression-analysis-using-high-throughput-sequencing-technologies.html"><a href="gene-expression-analysis-using-high-throughput-sequencing-technologies.html#exploratory-analysis-of-the-read-count-table"><i class="fa fa-check"></i><b>8.3.6</b> Exploratory analysis of the read count table</a></li>
<li class="chapter" data-level="8.3.7" data-path="gene-expression-analysis-using-high-throughput-sequencing-technologies.html"><a href="gene-expression-analysis-using-high-throughput-sequencing-technologies.html#differential-expression-analysis"><i class="fa fa-check"></i><b>8.3.7</b> Differential expression analysis</a></li>
<li class="chapter" data-level="8.3.8" data-path="gene-expression-analysis-using-high-throughput-sequencing-technologies.html"><a href="gene-expression-analysis-using-high-throughput-sequencing-technologies.html#functional-enrichment-analysis"><i class="fa fa-check"></i><b>8.3.8</b> Functional enrichment analysis</a></li>
<li class="chapter" data-level="8.3.9" data-path="gene-expression-analysis-using-high-throughput-sequencing-technologies.html"><a href="gene-expression-analysis-using-high-throughput-sequencing-technologies.html#accounting-for-additional-sources-of-variation"><i class="fa fa-check"></i><b>8.3.9</b> Accounting for additional sources of variation</a></li>
</ul></li>
<li class="chapter" data-level="8.4" data-path="other-applications-of-rna-seq.html"><a href="other-applications-of-rna-seq.html"><i class="fa fa-check"></i><b>8.4</b> Other applications of RNA-seq</a></li>
<li class="chapter" data-level="8.5" data-path="exercises-6.html"><a href="exercises-6.html"><i class="fa fa-check"></i><b>8.5</b> Exercises</a><ul>
<li class="chapter" data-level="8.5.1" data-path="exercises-6.html"><a href="exercises-6.html#exploring-the-count-tables"><i class="fa fa-check"></i><b>8.5.1</b> Exploring the count tables</a></li>
<li class="chapter" data-level="8.5.2" data-path="exercises-6.html"><a href="exercises-6.html#differential-expression-analysis-1"><i class="fa fa-check"></i><b>8.5.2</b> Differential expression analysis</a></li>
<li class="chapter" data-level="8.5.3" data-path="exercises-6.html"><a href="exercises-6.html#functional-enrichment-analysis-1"><i class="fa fa-check"></i><b>8.5.3</b> Functional enrichment analysis</a></li>
<li class="chapter" data-level="8.5.4" data-path="exercises-6.html"><a href="exercises-6.html#removing-unwanted-variation-from-the-expression-data"><i class="fa fa-check"></i><b>8.5.4</b> Removing unwanted variation from the expression data</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="9" data-path="chipseq.html"><a href="chipseq.html"><i class="fa fa-check"></i><b>9</b> ChIP-seq analysis</a><ul>
<li class="chapter" data-level="9.1" data-path="regulatory-protein-dna-interactions.html"><a href="regulatory-protein-dna-interactions.html"><i class="fa fa-check"></i><b>9.1</b> Regulatory protein-DNA interactions</a></li>
<li class="chapter" data-level="9.2" data-path="measuring-protein-dna-interactions-with-chip-seq.html"><a href="measuring-protein-dna-interactions-with-chip-seq.html"><i class="fa fa-check"></i><b>9.2</b> Measuring protein-DNA interactions with ChIP-seq</a></li>
<li class="chapter" data-level="9.3" data-path="factors-that-affect-chip-seq-experiment-and-analysis-quality.html"><a href="factors-that-affect-chip-seq-experiment-and-analysis-quality.html"><i class="fa fa-check"></i><b>9.3</b> Factors that affect ChIP-seq experiment and analysis quality</a><ul>
<li class="chapter" data-level="9.3.1" data-path="factors-that-affect-chip-seq-experiment-and-analysis-quality.html"><a href="factors-that-affect-chip-seq-experiment-and-analysis-quality.html#antibody-specificity"><i class="fa fa-check"></i><b>9.3.1</b> Antibody specificity</a></li>
<li class="chapter" data-level="9.3.2" data-path="factors-that-affect-chip-seq-experiment-and-analysis-quality.html"><a href="factors-that-affect-chip-seq-experiment-and-analysis-quality.html#sequencing-depth"><i class="fa fa-check"></i><b>9.3.2</b> Sequencing depth</a></li>
<li class="chapter" data-level="9.3.3" data-path="factors-that-affect-chip-seq-experiment-and-analysis-quality.html"><a href="factors-that-affect-chip-seq-experiment-and-analysis-quality.html#pcr-duplication"><i class="fa fa-check"></i><b>9.3.3</b> PCR duplication</a></li>
<li class="chapter" data-level="9.3.4" data-path="factors-that-affect-chip-seq-experiment-and-analysis-quality.html"><a href="factors-that-affect-chip-seq-experiment-and-analysis-quality.html#biological-replicates"><i class="fa fa-check"></i><b>9.3.4</b> Biological replicates</a></li>
<li class="chapter" data-level="9.3.5" data-path="factors-that-affect-chip-seq-experiment-and-analysis-quality.html"><a href="factors-that-affect-chip-seq-experiment-and-analysis-quality.html#control-experiments"><i class="fa fa-check"></i><b>9.3.5</b> Control experiments</a></li>
<li class="chapter" data-level="9.3.6" data-path="factors-that-affect-chip-seq-experiment-and-analysis-quality.html"><a href="factors-that-affect-chip-seq-experiment-and-analysis-quality.html#using-tagged-proteins"><i class="fa fa-check"></i><b>9.3.6</b> Using tagged proteins</a></li>
</ul></li>
<li class="chapter" data-level="9.4" data-path="pre-processing-chip-data.html"><a href="pre-processing-chip-data.html"><i class="fa fa-check"></i><b>9.4</b> Pre-processing ChIP data</a><ul>
<li class="chapter" data-level="9.4.1" data-path="pre-processing-chip-data.html"><a href="pre-processing-chip-data.html#mapping-of-chip-seq-data"><i class="fa fa-check"></i><b>9.4.1</b> Mapping of ChIP-seq data</a></li>
</ul></li>
<li class="chapter" data-level="9.5" data-path="chip-quality-control.html"><a href="chip-quality-control.html"><i class="fa fa-check"></i><b>9.5</b> ChIP quality control</a><ul>
<li class="chapter" data-level="9.5.1" data-path="chip-quality-control.html"><a href="chip-quality-control.html#the-data"><i class="fa fa-check"></i><b>9.5.1</b> The data</a></li>
<li class="chapter" data-level="9.5.2" data-path="chip-quality-control.html"><a href="chip-quality-control.html#sample-clustering"><i class="fa fa-check"></i><b>9.5.2</b> Sample clustering</a></li>
<li class="chapter" data-level="9.5.3" data-path="chip-quality-control.html"><a href="chip-quality-control.html#visualization-in-the-genome-browser"><i class="fa fa-check"></i><b>9.5.3</b> Visualization in the genome browser</a></li>
<li class="chapter" data-level="9.5.4" data-path="chip-quality-control.html"><a href="chip-quality-control.html#plus-and-minus-strand-cross-correlation"><i class="fa fa-check"></i><b>9.5.4</b> Plus and minus strand cross-correlation</a></li>
<li class="chapter" data-level="9.5.5" data-path="chip-quality-control.html"><a href="chip-quality-control.html#gc-bias-quantification"><i class="fa fa-check"></i><b>9.5.5</b> GC bias quantification</a></li>
<li class="chapter" data-level="9.5.6" data-path="chip-quality-control.html"><a href="chip-quality-control.html#sequence-read-genomic-distribution"><i class="fa fa-check"></i><b>9.5.6</b> Sequence read genomic distribution</a></li>
</ul></li>
<li class="chapter" data-level="9.6" data-path="peak-calling.html"><a href="peak-calling.html"><i class="fa fa-check"></i><b>9.6</b> Peak calling</a><ul>
<li class="chapter" data-level="9.6.1" data-path="peak-calling.html"><a href="peak-calling.html#types-of-chip-seq-experiments"><i class="fa fa-check"></i><b>9.6.1</b> Types of ChIP-seq experiments</a></li>
<li class="chapter" data-level="9.6.2" data-path="peak-calling.html"><a href="peak-calling.html#peak-calling-sharp-peaks"><i class="fa fa-check"></i><b>9.6.2</b> Peak calling: Sharp peaks</a></li>
<li class="chapter" data-level="9.6.3" data-path="peak-calling.html"><a href="peak-calling.html#peak-calling-broad-regions"><i class="fa fa-check"></i><b>9.6.3</b> Peak calling: Broad regions</a></li>
<li class="chapter" data-level="9.6.4" data-path="peak-calling.html"><a href="peak-calling.html#peak-quality-control"><i class="fa fa-check"></i><b>9.6.4</b> Peak quality control</a></li>
<li class="chapter" data-level="9.6.5" data-path="peak-calling.html"><a href="peak-calling.html#peak-annotation"><i class="fa fa-check"></i><b>9.6.5</b> Peak annotation</a></li>
</ul></li>
<li class="chapter" data-level="9.7" data-path="motif-discovery.html"><a href="motif-discovery.html"><i class="fa fa-check"></i><b>9.7</b> Motif discovery</a><ul>
<li class="chapter" data-level="9.7.1" data-path="motif-discovery.html"><a href="motif-discovery.html#motif-comparison"><i class="fa fa-check"></i><b>9.7.1</b> Motif comparison</a></li>
</ul></li>
<li class="chapter" data-level="9.8" data-path="what-to-do-next.html"><a href="what-to-do-next.html"><i class="fa fa-check"></i><b>9.8</b> What to do next?</a></li>
<li class="chapter" data-level="9.9" data-path="exercises-7.html"><a href="exercises-7.html"><i class="fa fa-check"></i><b>9.9</b> Exercises</a><ul>
<li class="chapter" data-level="9.9.1" data-path="exercises-7.html"><a href="exercises-7.html#quality-control"><i class="fa fa-check"></i><b>9.9.1</b> Quality control</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="10" data-path="bsseq.html"><a href="bsseq.html"><i class="fa fa-check"></i><b>10</b> DNA methylation analysis using bisulfite sequencing data</a><ul>
<li class="chapter" data-level="10.1" data-path="what-is-dna-methylation.html"><a href="what-is-dna-methylation.html"><i class="fa fa-check"></i><b>10.1</b> What is DNA methylation?</a><ul>
<li class="chapter" data-level="10.1.1" data-path="what-is-dna-methylation.html"><a href="what-is-dna-methylation.html#how-dna-methylation-is-set"><i class="fa fa-check"></i><b>10.1.1</b> How DNA methylation is set ?</a></li>
<li class="chapter" data-level="10.1.2" data-path="what-is-dna-methylation.html"><a href="what-is-dna-methylation.html#how-to-measure-dna-methylation-with-bisulfite-sequencing"><i class="fa fa-check"></i><b>10.1.2</b> How to measure DNA methylation with bisulfite sequencing</a></li>
</ul></li>
<li class="chapter" data-level="10.2" data-path="analyzing-dna-methylation-data.html"><a href="analyzing-dna-methylation-data.html"><i class="fa fa-check"></i><b>10.2</b> Analyzing DNA methylation data</a></li>
<li class="chapter" data-level="10.3" data-path="processing-raw-data-and-getting-data-into-r.html"><a href="processing-raw-data-and-getting-data-into-r.html"><i class="fa fa-check"></i><b>10.3</b> Processing raw data and getting data into R</a></li>
<li class="chapter" data-level="10.4" data-path="data-filtering-and-exploratory-analysis.html"><a href="data-filtering-and-exploratory-analysis.html"><i class="fa fa-check"></i><b>10.4</b> Data filtering and exploratory analysis</a><ul>
<li class="chapter" data-level="10.4.1" data-path="data-filtering-and-exploratory-analysis.html"><a href="data-filtering-and-exploratory-analysis.html#reading-methylation-call-files"><i class="fa fa-check"></i><b>10.4.1</b> Reading methylation call files</a></li>
<li class="chapter" data-level="10.4.2" data-path="data-filtering-and-exploratory-analysis.html"><a href="data-filtering-and-exploratory-analysis.html#further-quality-check"><i class="fa fa-check"></i><b>10.4.2</b> Further quality check</a></li>
<li class="chapter" data-level="10.4.3" data-path="data-filtering-and-exploratory-analysis.html"><a href="data-filtering-and-exploratory-analysis.html#merging-samples-into-a-single-table"><i class="fa fa-check"></i><b>10.4.3</b> Merging samples into a single table</a></li>
<li class="chapter" data-level="10.4.4" data-path="data-filtering-and-exploratory-analysis.html"><a href="data-filtering-and-exploratory-analysis.html#filtering-cpgs"><i class="fa fa-check"></i><b>10.4.4</b> Filtering CpGs</a></li>
<li class="chapter" data-level="10.4.5" data-path="data-filtering-and-exploratory-analysis.html"><a href="data-filtering-and-exploratory-analysis.html#clustering-samples"><i class="fa fa-check"></i><b>10.4.5</b> Clustering samples</a></li>
<li class="chapter" data-level="10.4.6" data-path="data-filtering-and-exploratory-analysis.html"><a href="data-filtering-and-exploratory-analysis.html#principal-component-analysis-1"><i class="fa fa-check"></i><b>10.4.6</b> Principal component analysis</a></li>
</ul></li>
<li class="chapter" data-level="10.5" data-path="extracting-interesting-regions-differential-methylation-and-segmentation.html"><a href="extracting-interesting-regions-differential-methylation-and-segmentation.html"><i class="fa fa-check"></i><b>10.5</b> Extracting interesting regions: Differential methylation and segmentation</a><ul>
<li class="chapter" data-level="10.5.1" data-path="extracting-interesting-regions-differential-methylation-and-segmentation.html"><a href="extracting-interesting-regions-differential-methylation-and-segmentation.html#differential-methylation"><i class="fa fa-check"></i><b>10.5.1</b> Differential methylation</a></li>
<li class="chapter" data-level="10.5.2" data-path="extracting-interesting-regions-differential-methylation-and-segmentation.html"><a href="extracting-interesting-regions-differential-methylation-and-segmentation.html#methylation-segmentation"><i class="fa fa-check"></i><b>10.5.2</b> Methylation segmentation</a></li>
<li class="chapter" data-level="10.5.3" data-path="extracting-interesting-regions-differential-methylation-and-segmentation.html"><a href="extracting-interesting-regions-differential-methylation-and-segmentation.html#working-with-large-files"><i class="fa fa-check"></i><b>10.5.3</b> Working with large files</a></li>
</ul></li>
<li class="chapter" data-level="10.6" data-path="annotation-of-dmrsdmcs-and-segments.html"><a href="annotation-of-dmrsdmcs-and-segments.html"><i class="fa fa-check"></i><b>10.6</b> Annotation of DMRs/DMCs and segments</a><ul>
<li class="chapter" data-level="10.6.1" data-path="annotation-of-dmrsdmcs-and-segments.html"><a href="annotation-of-dmrsdmcs-and-segments.html#further-annotation-with-genes-or-gene-sets"><i class="fa fa-check"></i><b>10.6.1</b> Further annotation with genes or gene sets</a></li>
</ul></li>
<li class="chapter" data-level="10.7" data-path="other-r-packages-that-can-be-used-for-methylation-analysis.html"><a href="other-r-packages-that-can-be-used-for-methylation-analysis.html"><i class="fa fa-check"></i><b>10.7</b> Other R packages that can be used for methylation analysis</a></li>
<li class="chapter" data-level="10.8" data-path="exercises-8.html"><a href="exercises-8.html"><i class="fa fa-check"></i><b>10.8</b> Exercises</a><ul>
<li class="chapter" data-level="10.8.1" data-path="exercises-8.html"><a href="exercises-8.html#differential-methylation-1"><i class="fa fa-check"></i><b>10.8.1</b> Differential methylation</a></li>
<li class="chapter" data-level="10.8.2" data-path="exercises-8.html"><a href="exercises-8.html#methylome-segmentation"><i class="fa fa-check"></i><b>10.8.2</b> Methylome segmentation</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="11" data-path="multiomics.html"><a href="multiomics.html"><i class="fa fa-check"></i><b>11</b> Multi-omics Analysis</a><ul>
<li class="chapter" data-level="11.1" data-path="use-case-multi-omics-data-from-colorectal-cancer.html"><a href="use-case-multi-omics-data-from-colorectal-cancer.html"><i class="fa fa-check"></i><b>11.1</b> Use case: Multi-omics data from colorectal cancer</a></li>
<li class="chapter" data-level="11.2" data-path="latent-variable-models-for-multi-omics-integration.html"><a href="latent-variable-models-for-multi-omics-integration.html"><i class="fa fa-check"></i><b>11.2</b> Latent variable models for multi-omics integration</a></li>
<li class="chapter" data-level="11.3" data-path="matrix-factorization-methods-for-unsupervised-multi-omics-data-integration.html"><a href="matrix-factorization-methods-for-unsupervised-multi-omics-data-integration.html"><i class="fa fa-check"></i><b>11.3</b> Matrix factorization methods for unsupervised multi-omics data integration</a><ul>
<li class="chapter" data-level="11.3.1" data-path="matrix-factorization-methods-for-unsupervised-multi-omics-data-integration.html"><a href="matrix-factorization-methods-for-unsupervised-multi-omics-data-integration.html#multiple-factor-analysis"><i class="fa fa-check"></i><b>11.3.1</b> Multiple factor analysis</a></li>
<li class="chapter" data-level="11.3.2" data-path="matrix-factorization-methods-for-unsupervised-multi-omics-data-integration.html"><a href="matrix-factorization-methods-for-unsupervised-multi-omics-data-integration.html#joint-non-negative-matrix-factorization"><i class="fa fa-check"></i><b>11.3.2</b> Joint non-negative matrix factorization</a></li>
<li class="chapter" data-level="11.3.3" data-path="matrix-factorization-methods-for-unsupervised-multi-omics-data-integration.html"><a href="matrix-factorization-methods-for-unsupervised-multi-omics-data-integration.html#icluster"><i class="fa fa-check"></i><b>11.3.3</b> iCluster</a></li>
</ul></li>
<li class="chapter" data-level="11.4" data-path="clustering-using-latent-factors.html"><a href="clustering-using-latent-factors.html"><i class="fa fa-check"></i><b>11.4</b> Clustering using latent factors</a><ul>
<li class="chapter" data-level="11.4.1" data-path="clustering-using-latent-factors.html"><a href="clustering-using-latent-factors.html#one-hot-clustering"><i class="fa fa-check"></i><b>11.4.1</b> One-hot clustering</a></li>
<li class="chapter" data-level="11.4.2" data-path="clustering-using-latent-factors.html"><a href="clustering-using-latent-factors.html#k-means-clustering-1"><i class="fa fa-check"></i><b>11.4.2</b> K-means clustering</a></li>
</ul></li>
<li class="chapter" data-level="11.5" data-path="biological-interpretation-of-latent-factors.html"><a href="biological-interpretation-of-latent-factors.html"><i class="fa fa-check"></i><b>11.5</b> Biological interpretation of latent factors</a><ul>
<li class="chapter" data-level="11.5.1" data-path="biological-interpretation-of-latent-factors.html"><a href="biological-interpretation-of-latent-factors.html#inspection-of-feature-weights-in-loading-vectors"><i class="fa fa-check"></i><b>11.5.1</b> Inspection of feature weights in loading vectors</a></li>
<li class="chapter" data-level="11.5.2" data-path="biological-interpretation-of-latent-factors.html"><a href="biological-interpretation-of-latent-factors.html#making-sense-of-factors-using-enrichment-analysis"><i class="fa fa-check"></i><b>11.5.2</b> Making sense of factors using enrichment analysis</a></li>
<li class="chapter" data-level="11.5.3" data-path="biological-interpretation-of-latent-factors.html"><a href="biological-interpretation-of-latent-factors.html#interpretation-using-additional-covariates"><i class="fa fa-check"></i><b>11.5.3</b> Interpretation using additional covariates</a></li>
</ul></li>
<li class="chapter" data-level="11.6" data-path="exercises-9.html"><a href="exercises-9.html"><i class="fa fa-check"></i><b>11.6</b> Exercises</a><ul>
<li class="chapter" data-level="11.6.1" data-path="exercises-9.html"><a href="exercises-9.html#matrix-factorization-methods"><i class="fa fa-check"></i><b>11.6.1</b> Matrix factorization methods</a></li>
<li class="chapter" data-level="11.6.2" data-path="exercises-9.html"><a href="exercises-9.html#clustering-using-latent-factors-1"><i class="fa fa-check"></i><b>11.6.2</b> Clustering using latent factors</a></li>
<li class="chapter" data-level="11.6.3" data-path="exercises-9.html"><a href="exercises-9.html#biological-interpretation-of-latent-factors-1"><i class="fa fa-check"></i><b>11.6.3</b> Biological interpretation of latent factors</a></li>
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<li class="chapter" data-level="" data-path="references.html"><a href="references.html"><i class="fa fa-check"></i>References</a></li>
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<div id="exercises-6" class="section level2">
<h2><span class="header-section-number">8.5</span> Exercises</h2>
<div id="exploring-the-count-tables" class="section level3">
<h3><span class="header-section-number">8.5.1</span> Exploring the count tables</h3>
<p>Here, import an example count table and do some exploration of the expression data.</p>
<div class="sourceCode" id="cb442"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb442-1"><a href="exercises-6.html#cb442-1"></a>counts_file <-<span class="st"> </span><span class="kw">system.file</span>(<span class="st">"extdata/rna-seq/SRP029880.raw_counts.tsv"</span>,</span>
<span id="cb442-2"><a href="exercises-6.html#cb442-2"></a> <span class="dt">package =</span> <span class="st">"compGenomRData"</span>)</span>
<span id="cb442-3"><a href="exercises-6.html#cb442-3"></a>coldata_file <-<span class="st"> </span><span class="kw">system.file</span>(<span class="st">"extdata/rna-seq/SRP029880.colData.tsv"</span>, </span>
<span id="cb442-4"><a href="exercises-6.html#cb442-4"></a> <span class="dt">package =</span> <span class="st">"compGenomRData"</span>)</span></code></pre></div>
<ol style="list-style-type: decimal">
<li>Normalize the counts using the TPM approach. [Difficulty: <strong>Beginner</strong>]</li>
<li>Plot a heatmap of the top 500 most variable genes. Compare with the heatmap obtained using the 100 most variable genes. [Difficulty: <strong>Beginner</strong>]</li>
<li>Re-do the heatmaps setting the <code>scale</code> argument to <code>none</code>, and <code>column</code>. Compare the results with <code>scale = 'row'</code>. [Difficulty: <strong>Beginner</strong>]</li>
<li>Draw a correlation plot for the samples depicting the sample differences as ‘ellipses’, drawing only the upper end of the matrix, and order samples by hierarchical clustering results based on <code>average</code> linkage clustering method. [Difficulty: <strong>Beginner</strong>]</li>
<li>How else could the count matrix be subsetted to obtain quick and accurate clusters? Try selecting the top 100 genes that have the highest total expression in all samples and re-draw the cluster heatmaps and PCA plots. [Difficulty: <strong>Intermediate</strong>]</li>
<li>Add an additional column to the annotation data.frame object to annotate the samples and use the updated annotation data.frame to plot the heatmaps. (Hint: Assign different batch values to CASE and CTRL samples). Make a PCA plot and color samples by the added variable (e.g. batch). [Difficulty: Intermediate]</li>
<li>Try making the heatmaps using all the genes in the count table, rather than sub-selecting. [Difficulty: <strong>Advanced</strong>]</li>
<li>Use the <a href="https://cran.r-project.org/web/packages/Rtsne/Rtsne.pdf"><code>Rtsne</code> package</a> to draw a t-SNE plot of the expression values. Color the points by sample group. Compare the results with the PCA plots. [Difficulty: <strong>Advanced</strong>]</li>
</ol>
</div>
<div id="differential-expression-analysis-1" class="section level3">
<h3><span class="header-section-number">8.5.2</span> Differential expression analysis</h3>
<p>Firstly, carry out a differential expression analysis starting from raw counts.
Use the following datasets:</p>
<pre><code>counts_file <- system.file("extdata/rna-seq/SRP029880.raw_counts.tsv",
package = "compGenomRData")
coldata_file <- system.file("extdata/rna-seq/SRP029880.colData.tsv",
package = "compGenomRData")</code></pre>
<ul>
<li>Import the read counts and colData tables.</li>
<li>Set up a DESeqDataSet object.</li>
<li>Filter out genes with low counts.</li>
<li>Run DESeq2 contrasting the <code>CASE</code> sample with <code>CONTROL</code> samples.</li>
</ul>
<p>Now, you are ready to do the following exercises:</p>
<ol style="list-style-type: decimal">
<li>Make a volcano plot using the differential expression analysis results. (Hint: x-axis denotes the log2FoldChange and the y-axis represents the -log10(pvalue)). [Difficulty: <strong>Beginner</strong>]</li>
<li>Use DESeq2::plotDispEsts to make a dispersion plot and find out the meaning of this plot. (Hint: Type ?DESeq2::plotDispEsts) [Difficulty: <strong>Beginner</strong>]</li>
<li>Explore <code>lfcThreshold</code> argument of the <code>DESeq2::results</code> function. What is its default value? What does it mean to change the default value to, for instance, <code>1</code>? [Difficulty: <strong>Intermediate</strong>]</li>
<li>What is independent filtering? What happens if we don’t use it? Google <code>independent filtering statquest</code> and watch the online video about independent filtering. [Difficulty: <strong>Intermediate</strong>]</li>
<li>Re-do the differential expression analysis using the <code>edgeR</code> package. Find out how much DESeq2 and edgeR agree on the list of differentially expressed genes. [Difficulty: <strong>Advanced</strong>]</li>
<li>Use the <code>compcodeR</code> package to run the differential expression analysis using at least three different tools and compare and contrast the results following the <code>compcodeR</code> vignette. [Difficulty: <strong>Advanced</strong>]</li>
</ol>
</div>
<div id="functional-enrichment-analysis-1" class="section level3">
<h3><span class="header-section-number">8.5.3</span> Functional enrichment analysis</h3>
<ol style="list-style-type: decimal">
<li>Re-run gProfileR, this time using pathway annotations such as KEGG, REACTOME, and protein complex databases such as CORUM, in addition to the GO terms. Sort the resulting tables by columns <code>precision</code> and/or <code>recall</code>. How do the top GO terms change when sorted for <code>precision</code>, <code>recall</code>, or <code>p.value</code>? [Difficulty: <strong>Beginner</strong>]</li>
<li>Repeat the gene set enrichment analysis by trying different options for the <code>compare</code> argument of the <code>GAGE:gage</code>
function. How do the results differ? [Difficulty: <strong>Beginner</strong>]</li>
<li>Make a scatter plot of GO term sizes and obtained p-values by setting the <code>gProfiler::gprofiler</code> argument <code>significant = FALSE</code>. Is there a correlation of term sizes and p-values? (Hint: Take -log10 of p-values). If so, how can this bias be mitigated? [Difficulty: <strong>Intermediate</strong>]</li>
<li>Do a gene-set enrichment analysis using gene sets from top 10 GO terms. [Difficulty: <strong>Intermediate</strong>]</li>
<li>What are the other available R packages that can carry out gene set enrichment analysis for RNA-seq datasets? [Difficulty: <strong>Intermediate</strong>]</li>
<li>Use the topGO package (<a href="https://bioconductor.org/packages/release/bioc/html/topGO.html" class="uri">https://bioconductor.org/packages/release/bioc/html/topGO.html</a>) to re-do the GO term analysis. Compare and contrast the results with what has been obtained using the <code>gProfileR</code> package. Which tool is faster, <code>gProfileR</code> or topGO? Why? [Difficulty: <strong>Advanced</strong>]</li>
<li>Given a gene set annotated for human, how can it be utilized to work on <em>C. elegans</em> data? (Hint: See <code>biomaRt::getLDS</code>). [Difficulty: <strong>Advanced</strong>]</li>
<li>Import curated pathway gene sets with Entrez identifiers from the <a href="http://software.broadinstitute.org/gsea/msigdb/collections.jsp">MSIGDB database</a> and re-do the GSEA for all curated gene sets. [Difficulty: <strong>Advanced</strong>]</li>
</ol>
</div>
<div id="removing-unwanted-variation-from-the-expression-data" class="section level3">
<h3><span class="header-section-number">8.5.4</span> Removing unwanted variation from the expression data</h3>
<p>For the exercises below, use the datasets at:</p>
<pre><code>counts_file <- system.file('extdata/rna-seq/SRP049988.raw_counts.tsv',
package = 'compGenomRData')
colData_file <- system.file('extdata/rna-seq/SRP049988.colData.tsv',
package = 'compGenomRData')</code></pre>
<ol style="list-style-type: decimal">
<li>Run RUVSeq using multiple values of <code>k</code> from 1 to 10 and compare and contrast the PCA plots obtained from the normalized counts of each RUVSeq run. [Difficulty: <strong>Beginner</strong>]</li>
<li>Re-run RUVSeq using the <code>RUVr()</code> function. Compare PCA plots from <code>RUVs</code>, <code>RUVg</code> and <code>RUVr</code> using the same <code>k</code> values and find out which one performs the best. [Difficulty: <strong>Intermediate</strong>]</li>
<li>Do the necessary diagnostic plots using the differential expression results from the EHF count table. [Difficulty: <strong>Intermediate</strong>]</li>
<li>Use the <code>sva</code> package to discover sources of unwanted variation and re-do the differential expression analysis using variables from the output of <code>sva</code> and compare the results with <code>DESeq2</code> results using <code>RUVSeq</code> corrected normalization counts. [Difficulty: <strong>Advanced</strong>]</li>
</ol>
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