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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
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<meta name="author" content="Michael Love (he/him)" />
<title>Intro to Computational Biology - UNC BIOS/BCB 784</title>
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<div class="container-fluid main-container">
<div id="header">
<h1 class="title toc-ignore">Intro to Computational Biology - UNC BIOS/BCB 784</h1>
<h4 class="author"><a href="http://mikelove.github.io">Michael Love</a> (he/him)</h4>
</div>
<div id="course-information" class="section level3">
<h3>Course information</h3>
<p>BIOS/BCB 784</p>
<p>Fall 2024 – 1:25-2:40 MoWe – <a href="https://sph.unc.edu/room/2306-mcgavran-greenberg-hall/">McGavran 2306</a></p>
<p><a href="BIOS784-Fall2024-Gillings-v7.docx">2024 syllabus</a></p>
<p>This course makes extensive use of R and assumes basic familiarity with base R (not packages) as a prerequisite. A self-quiz is available <a href="https://github.com/biodatascience/compbio/blob/gh-pages/selfquiz/selfquiz.R">here</a>, with answers provided <a href="https://github.com/biodatascience/compbio/raw/gh-pages/selfquiz/selfquiz_answers.rda">here</a>. You can also find a <a href="https://github.com/biodatascience/compbio_src/blob/master/eda/functions.md">list of base R functions</a> that one should be familiar with.</p>
<p><strong>For 2024 BCB students</strong>: BCB 720 (background on statistical inference, i.e. working with the likelihood for parameter inference, conditional probabilities) is suitable as a pre-requisite for BIOS/BCB 784.</p>
</div>
<div id="schedule-and-course-notes" class="section level3">
<h3>Schedule and course notes</h3>
<p>For <code>Rmd</code> or <code>qmd</code> files, go to the <a href="https://github.com/biodatascience/compbio_src">course repo</a> and navigate the directories, or clone the repo and navigate within RStudio.</p>
<table style="width:100%;">
<colgroup>
<col width="16%" />
<col width="19%" />
<col width="16%" />
<col width="11%" />
<col width="16%" />
<col width="19%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Week</th>
<th align="left">Topic</th>
<th align="left">Dir.</th>
<th align="left">HW</th>
<th align="left">HTML</th>
<th align="left">Title</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left"></td>
<td align="left"><strong>Data analysis</strong></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Aug 19</td>
<td align="left">Biological intro / GitHub</td>
<td align="left"><code>-</code></td>
<td align="left"><a href="https://github.com/biodatascience/compbio_src/blob/master/github/github_HW.Rmd">HW0</a></td>
<td align="left"><a href="github.html">github</a></td>
<td align="left">RStudio, git, and GitHub</td>
</tr>
<tr class="odd">
<td align="left">Aug 21</td>
<td align="left">Exploring bio data</td>
<td align="left"><code>eda</code></td>
<td align="left"></td>
<td align="left"><a href="eda/EDA.html">EDA</a></td>
<td align="left">Exploratory data analysis</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="eda/brain_RNA.html">brain RNA</a></td>
<td align="left">Exploring brain RNA</td>
</tr>
<tr class="odd">
<td align="left">Aug 26 & 28</td>
<td align="left">R/Bioconductor I</td>
<td align="left"><code>bioc</code></td>
<td align="left"><a href="https://github.com/biodatascience/compbio_src/blob/master/bioc/bioc_objects_ranges_HW.Rmd">HW1</a></td>
<td align="left"><a href="bioc/objects.html">objects</a></td>
<td align="left">Bioc data objects</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="bioc/ranges.html">ranges</a></td>
<td align="left">Genomic ranges</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="bioc/GRL.html">GRL</a></td>
<td align="left">GRangesList: lists of ranges</td>
</tr>
<tr class="even">
<td align="left">Sep 2</td>
<td align="left">Labor day</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Sep 4</td>
<td align="left">“tidyomics”</td>
<td align="left"><code>TO@GH</code></td>
<td align="left"></td>
<td align="left"><a href="https://tidyomics.github.io/tidy-intro-talk/">tidy intro</a></td>
<td align="left">Tidiness-in-Bioconductor intro</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="https://tidyomics.github.io/tidy-ranges-tutorial/">tidy ranges</a></td>
<td align="left">Tidy ranges tutorial</td>
</tr>
<tr class="odd">
<td align="left">Sep 9 & 11</td>
<td align="left">R/Bioconductor II</td>
<td align="left"><code>bioc</code></td>
<td align="left"><a href="https://github.com/biodatascience/compbio_src/blob/master/bioc/bioc_anno_strings_HW.Rmd">HW2</a></td>
<td align="left"><a href="bioc/anno.html">anno</a></td>
<td align="left">Accessing annotations</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="bioc/strings.html">strings</a></td>
<td align="left">Manipulating DNA strings</td>
</tr>
<tr class="odd">
<td align="left">Sep 16 & 18</td>
<td align="left">Distances & norm. I</td>
<td align="left"><code>dist</code></td>
<td align="left"><a href="https://github.com/biodatascience/compbio_src/blob/master/dist/dist_biases_scaling_HW.Rmd">HW3</a></td>
<td align="left"><a href="dist/distances.html">distances</a></td>
<td align="left">Distances in high dimensions</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="dist/transform_clust.html">transform_clust</a></td>
<td align="left">Transformations and clustering</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="https://github.com/biodatascience/compbio_src/blob/master/dist/vst_math.qmd">vst_math</a></td>
<td align="left">VST math</td>
</tr>
<tr class="even">
<td align="left">Sep 23</td>
<td align="left">Wellness day</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Sep 25</td>
<td align="left">Spatial data analysis (guest lecture)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="https://www.linkedin.com/in/peyton-kuhlers/">Peyton Kuhlers</a> (Hoadley & Raab labs, UNC)</td>
</tr>
<tr class="even">
<td align="left">Sep 30</td>
<td align="left">BCB retreat</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Oct 2</td>
<td align="left">Sources of technical bias</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Oct 7 & 9</td>
<td align="left">Distances & norm. II</td>
<td align="left"><code>dist</code></td>
<td align="left"></td>
<td align="left"><a href="dist/batch.html">batch</a></td>
<td align="left">Batch effects and sources</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="dist/sva.html">sva</a></td>
<td align="left">Surrogate variable analysis</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">Batch effect solutions</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="https://github.com/biodatascience/compbio_src/blob/master/dist/ruv.R">ruv script</a></td>
<td align="left">RUV and friends</td>
</tr>
<tr class="even">
<td align="left">Oct 7</td>
<td align="left">Midterm assigned, due Oct 21</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left"><strong>Data modeling</strong></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Oct 14 & 16</td>
<td align="left">Hierarchical models</td>
<td align="left"><code>hier</code></td>
<td align="left"></td>
<td align="left"><a href="hier/hierarchical.html">hierarchical</a></td>
<td align="left">Hierarchical models</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="hier/jamesstein.html">jamesstein</a></td>
<td align="left">James-Stein estimator</td>
</tr>
<tr class="even">
<td align="left">Oct 21 & 23</td>
<td align="left">Models and EM</td>
<td align="left"><code>model</code></td>
<td align="left"></td>
<td align="left"><a href="model/EM.html">EM</a></td>
<td align="left">Expectation maximization</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="model/motif.html">motif</a></td>
<td align="left">EM for finding DNA motifs</td>
</tr>
<tr class="even">
<td align="left">Oct 28 & 30</td>
<td align="left">Markov models</td>
<td align="left"><code>markov</code></td>
<td align="left"><a href="https://github.com/biodatascience/compbio_src/blob/master/model/model_HW.Rmd">HW4</a></td>
<td align="left"><a href="markov/hmm.html">hmm</a></td>
<td align="left">Hidden Markov Models</td>
</tr>
<tr class="odd">
<td align="left">Nov 4</td>
<td align="left">HMM: Baum-Welch</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Nov 6</td>
<td align="left">ASHG</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Nov 11</td>
<td align="left">Biostatistics 75th event</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Nov 13</td>
<td align="left">Class time for projects</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Nov 18 & 20</td>
<td align="left">Class time for projects</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Nov 25</td>
<td align="left">Gene regulatory networks</td>
<td align="left"><code>net</code></td>
<td align="left"></td>
<td align="left"><a href="net/network.html">network</a></td>
<td align="left">Network analysis</td>
</tr>
<tr class="odd">
<td align="left">Nov 27</td>
<td align="left">Thanksgiving</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Dec 2 & 4</td>
<td align="left">Final project presentations</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Extra lectures</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">multiple testing</td>
<td align="left"><code>multiple</code></td>
<td align="left"></td>
<td align="left"><a href="multiple/multtest.html">multtest</a></td>
<td align="left">FDR and Benjamini-Hochberg</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"><a href="multiple/localfdr.html">localfdr</a></td>
<td align="left">Local false discovery rate</td>
</tr>
</tbody>
</table>
</div>
<div id="reading-list" class="section level3">
<h3>Reading list</h3>
<ul>
<li><strong>What is the role of the computational biologist / statistician?</strong>
<ul>
<li><a href="http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2002050">All biology is computational biology</a> Florian Markowetz</li>
<li><a href="https://www.stat.berkeley.edu/~sandrine/Docs/TerrySelectedWorksSpringer/Version1/Nolan/Nolan.pdf">Questions, Answers and Statistics</a> Deborah Nolan</li>
<li><a href="https://dl.dropboxusercontent.com/u/23421017/50YearsDataScience.pdf">50 Years of Data Science</a> David Donoho</li>
<li><a href="https://projecteuclid.org/euclid.aoms/1177704711">The Future of Data Analysis</a> John Tukey (this article, discussed by Donoho, is from 1962)</li>
<li><a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004961">Ten Simple Rules for Effective Statistical Practice</a> Kass, Caffo, Davidian, Meng, Yu, and Reid</li>
<li><a href="https://projecteuclid.org/euclid.ss/1009213726">Statistical Modeling: The Two Cultures</a> Leo Breiman</li>
</ul></li>
<li><strong>Exploratory data analysis</strong>
<ul>
<li><a href="http://dplyr.tidyverse.org/">dplyr - A Grammar of Data Manipulation</a></li>
<li><a href="http://ggplot2.org/">ggplot2</a></li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4547484/">The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans</a></li>
</ul></li>
<li><strong>Bioconductor</strong>
<ul>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4509590/">Orchestrating high-throughput genomic analysis with Bioconductor</a> Huber et al</li>
</ul></li>
<li><strong>Distances and normalization</strong>
<ul>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218662/">Differential expression analysis for sequence count data</a> Simon Anders and Wolfgang Huber</li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3880143/">Tackling the widespread and critical impact of batch effects in high-throughput data</a> Leek et al</li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1994707/">Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis</a> Jeffrey Leek and John Storey</li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4404308/">Normalization of RNA-seq data using factor analysis of control genes or samples</a> Risso et al</li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3398141/">Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses</a> Stegle et al</li>
<li><strong>More on factor analysis methods:</strong></li>
<li>RUV - <a href="https://doi.org/10.1093/biostatistics/kxr034">Using control genes to correct for unwanted variation in microarray data</a> Gagnon-Bartsch and Speed et al., 2012</li>
<li>RUV - <a href="https://doi.org/10.1038/s41587-022-01440-w">Removing Unwanted Variation from High Dimensional Data with Negative Controls</a> Gagnon-Bartsch et al., 2013</li>
<li>RUVSeq - <a href="https://doi.org/10.1038/nbt.2931">Normalization of RNA-seq data using factor analysis of control genes or samples</a> Risso et al., 2014</li>
<li>ZINB-WaVE - <a href="https://doi.org/10.1038/s41467-017-02554-5">A general and flexible method for signal extraction from single-cell RNA-seq data</a> Risso and Perraudeau et al., 2018</li>
<li>NewWave - <a href="https://doi.org/10.1093/bioinformatics/btac149">A scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA-seq data</a> Agostinis et al., 2022</li>
<li>GLM-PCA - <a href="https://doi.org/10.1186/s13059-019-1861-6">Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model</a> Townes et al., 2019</li>
</ul></li>
<li><strong>Multiple testing</strong>
<ul>
<li><a href="http://www.jstor.org/stable/2346101">Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing</a> Yoav Benjamini and Yosef Hochberg</li>
<li><a href="http://genomics.princeton.edu/storeylab//papers/directfdr.pdf">A direct approach to false discovery rates</a> John Storey</li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC170937/">Statistical significance for genomewide studies</a> John Storey and Robert Tibshirani</li>
<li><a href="http://www.tandfonline.com/doi/abs/10.1198/016214504000000089">Large-scale simultaneous hypothesis testing</a> Bradley Efron</li>
<li><a href="http://dx.doi.org/10.1198/016214501753382129">Empirical Bayes Analysis of a Microarray Experiment</a> Efron et al</li>
<li><a href="https://projecteuclid.org/euclid.aoas/1318514284">Measuring reproducibility of high-throughput experiments</a> Li et al</li>
</ul></li>
<li><strong>Expectation maximization</strong>
<ul>
<li><a href="https://www.nature.com/nbt/journal/v26/n8/full/nbt1406.html">What is the expectation maximization algorithm?</a> Chuong B Do and Serafim Batzoglou</li>
<li><a href="https://people.csail.mit.edu/rameshvs/content/gmm-em.pdf">Gaussian mixture models and the EM algorithm</a> Ramesh Sridharan</li>
<li><a href="http://cs229.stanford.edu/notes/cs229-notes8.pdf">EM algorithm notes</a> Andrew Ng</li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1538909/">MEME: discovering and analyzing DNA and protein sequence motifs</a> Bailey et al</li>
</ul></li>
<li><strong>Hierarchical models</strong>
<ul>
<li><a href="http://www.statsci.org/smyth/pubs/ebayes.pdf">Linear models and empirical Bayes methods for assessing differential expression in microarray experiments</a> Gordon Smyth</li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2904972/">Analyzing ’omics data using hierarchical models</a> Hongkai Ji and X Shirley Liu</li>
<li><a href="http://statweb.stanford.edu/~ckirby/brad/other/Article1977.pdf">Stein’s Paradox in Statistics</a> Bradley Efron and Carl Morris</li>
<li><a href="https://www.jstor.org/stable/2284155">Stein’s estimation rule and its competitors - an empirical Bayes approach</a> Bradley Efron and Carl Morris</li>
</ul></li>
<li><strong>Signal processing</strong>
<ul>
<li><a href="http://ieeexplore.ieee.org/abstract/document/1165342/?reload=true">An Introduction to Hidden Markov Models</a> Lawrence Rabiner and Biing-Hwang Juang</li>
<li><a href="http://www.sciencedirect.com/science/article/pii/S0047259X04000260">Hidden Markov models approach to the analysis of array CGH data</a> Fridlyand et al</li>
</ul></li>
<li><strong>Network analysis</strong>
<ul>
<li><a href="http://dx.doi.org/10.1016/j.molcel.2017.08.006">Static And Dynamic DNA Loops Form AP-1 Bound Activation Hubs During Macrophage Development</a> Phanstiel et al</li>
</ul></li>
</ul>
</div>
<div id="resources" class="section level3">
<h3>Resources</h3>
<ul>
<li><a href="http://genomicsclass.github.io/book/pages/resources.html">Online R Classes and Resources</a></li>
<li>Rafael Irizarry and Michael Love, “Data Analysis for the Life Sciences” <a href="https://leanpub.com/dataanalysisforthelifesciences">Free PDF</a>, <a href="http://genomicsclass.github.io/book/">HTML</a></li>
<li><a href="https://kasperdanielhansen.github.io/genbioconductor/">Kasper Hansen, “Bioconductor for Genomic Data Science”</a></li>
<li><a href="https://github.com/quinlan-lab/applied-computational-genomics">Aaron Quinlan, “Applied Computational Genomics” (Slides)</a></li>
<li><a href="http://stat545.com/">Jennifer Bryan et al, Stat 545</a></li>
<li><a href="https://doi.org/10.1371/journal.pcbi.1004387">Florian Markowetz, “You Are Not Working for Me; I Am Working with You”</a></li>
<li><a href="tips.html">Tips to succeed in Computational Biology research</a></li>
</ul>
<p>Some R resources</p>
<ul>
<li><a href="http://www.statmethods.net/">Quick-R</a></li>
<li><a href="http://mathesaurus.sourceforge.net/octave-r.html">R for Matlab users</a></li>
<li><a href="http://www.cookbook-r.com/">R Cookbook</a></li>
<li><a href="https://cran.r-project.org/doc/contrib/Short-refcard.pdf">R Reference Card</a></li>
<li><a href="http://adv-r.had.co.nz/">Advanced R</a></li>
<li><a href="https://github.com/mikelove/bioc-refcard">Bioconductor Reference Card</a></li>
</ul>
<hr />
<p>This page was last updated on 11/15/2024.</p>
<ul>
<li><a href="https://github.com/biodatascience/compbio_src">GitHub repo</a></li>
<li><a href="https://github.com/biodatascience/compbio_src/blob/master/LICENSE">License</a></li>
</ul>
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