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<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
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<channel>
<title>Yutong Wang</title>
<link>http://yu-tong-wang.github.io/</link>
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<description>Yutong Wang</description>
<generator>Source Themes Academic (https://sourcethemes.com/academic/)</generator><language>en-us</language><copyright>© 2024</copyright><lastBuildDate>Sun, 08 Oct 2023 00:00:00 +0000</lastBuildDate>
<image>
<url>http://yu-tong-wang.github.io/images/icon_hu0b7a4cb9992c9ac0e91bd28ffd38dd00_9727_512x512_fill_lanczos_center_2.png</url>
<title>Yutong Wang</title>
<link>http://yu-tong-wang.github.io/</link>
</image>
<item>
<title>CMPBIO 293: Doctoral seminar in computational biology (Fall 2023)</title>
<link>http://yu-tong-wang.github.io/teaching/courses/compbio293/</link>
<pubDate>Sun, 08 Oct 2023 00:00:00 +0000</pubDate>
<guid>http://yu-tong-wang.github.io/teaching/courses/compbio293/</guid>
<description><h2 id="course-information">Course Information</h2>
<ul>
<li><em>Professor</em>: Yun S. Song</li>
<li><em>Lecture</em>: Thursday 1-3pm, 39 Evans</li>
<li><em>Office Hours</em>: Tuesday 5:10-6pm, 378 Stanley Hall</li>
<li><em>Content</em>: This doctoral level course covers a broad spectrum of advanced topics including dimensionality reduction, clustering, RNA-seq analysis, multi-omic and spatial transcriptomics, CRISPR technologies, deep mutation scanning, and predictive models for variant effects and polygenic risk scores. The interactive seminar builds skills, knowledge and community in computational biology for first year PhD and second year Designated Emphasis students.</li>
</ul>
</description>
</item>
<item>
<title>CMPBIO 290: Algorithms for single-cell genomics (Fall 2021)</title>
<link>http://yu-tong-wang.github.io/teaching/courses/compbio290/</link>
<pubDate>Fri, 08 Oct 2021 00:00:00 +0000</pubDate>
<guid>http://yu-tong-wang.github.io/teaching/courses/compbio290/</guid>
<description><h2 id="course-information">Course Information</h2>
<ul>
<li><em>Professor</em>: Yun S. Song</li>
<li><em>Lecture</em>: Monday 3-5pm, 177 Stanley Hall</li>
<li><em>Office Hours</em>: Thursday 4-5pm on Zoom</li>
<li><em>Content</em>: This graduate course will cover a range of current topics in single-cell genomics (broadly defined), with an emphasis on algorithms and statistical methods. Three main themes of the course will be spatial transcriptomics, integrative analysis of multi-omics data, and immune receptor-antigen interactions. In addition to reading a number of recent research articles in these areas, students will be provided with an opportunity to gain hands-on experience in analyzing real data.</li>
</ul>
<p>Check out my
<a href="https://docs.google.com/presentation/d/1zqfWGPs8OOUo1K-umfocRuxCAYNC1INJNKljyvwY4Dc/edit?usp=sharing" target="_blank" rel="noopener">lecture slides</a> about spatial transcriptomics, and two problem sets I created about
<a href="https://drive.google.com/file/d/1pWhgq5sLg0kS9g14oLrGw-e30VkXGImj/view?usp=sharing" target="_blank" rel="noopener">spatial transcriptomics data analysis</a> as well as
<a href="https://drive.google.com/file/d/19h4wuA0DWmzS_oAxlcvut3ZrWmS2lGB5/view?usp=sharing" target="_blank" rel="noopener">multi-omics integration analysis</a> respectively. Have fun! :)</p>
</description>
</item>
<item>
<title>Statistics 135: Concepts of Statistics (Spring 2020)</title>
<link>http://yu-tong-wang.github.io/teaching/courses/stat135/</link>
<pubDate>Fri, 22 May 2020 00:00:00 +0000</pubDate>
<guid>http://yu-tong-wang.github.io/teaching/courses/stat135/</guid>
<description><h2 id="course-information">Course Information</h2>
<ul>
<li><em>Professor</em>: Adam Lucas</li>
<li><em>Office Hours</em>: W 12-1pm, Th 1-3pm, F 9:30-10:30am</li>
<li><em>Lab Sections</em>: F 12-2pm, F 4-6pm</li>
<li><em>Content</em>: STAT 135 is Berkeley&rsquo;s core upper-division course on statistical theory and methodology for undergraduates in statistics and related fields. Topics include parameter estimation, hypothesis testing, statistical tests (parametric and non parametric) and linear regression (single and multiple).</li>
</ul>
<p>Please refer to Lab 7 slides for a thorough review of part 1 &amp; 2 (i.e., parametric estimation and hypothesis testing). The following teaching materials were made by me, and the problem sets in the slides were from previous iterations of the class.</p>
<h2 id="materials">Materials</h2>
<ul>
<li>Lab 1: R setup and practice <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab1.pdf">[Slides]</a></li>
</ul>
<p><strong>Part 1: Parametric Estimation</strong></p>
<ul>
<li>
<p>Lab 2: MoM, nonparametric bootstrap, SE calculation <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab2.pdf">[Slides]</a></p>
</li>
<li>
<p>Lab 3: MLE, Delta method <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab3.pdf">[Slides]</a></p>
</li>
<li>
<p>Lab 4: Quiz 1 recap, Fisher information, CRLB, MSE, ggplot <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab4.pdf">[Slides]</a></p>
</li>
<li>
<p>Lab 5: Sufficiency, minimal sufficiency, Rao-Blackwell theorem <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab5.pdf">[Slides]</a></p>
</li>
</ul>
<p><strong>Part 2: Hypothesis Testing</strong></p>
<ul>
<li>
<p>Lab 6: Hypothesis testing, Neyman-Pearson lemma, LRT, UMP test, GLRT, p value <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab6.pdf">[Slides]</a></p>
</li>
<li>
<p>Lab 7: Midterm Review <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab7.pdf">[Slides]</a></p>
</li>
</ul>
<p><strong>Part 3: Statistical Tests (parametric &amp; nonparametric)</strong></p>
<ul>
<li>
<p>Lab 8: 2-sample t-test, chi-squared test <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab8.pdf">[Slides]</a></p>
</li>
<li>
<p>Lab 9: TOH, TOI, Mann-Whitney test <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab9.pdf">[Slides]</a></p>
</li>
<li>
<p>Lab 10: Wilcoxon signed rank test, ANOVA, Bonferroni correction <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab10.pdf">[Slides]</a></p>
</li>
</ul>
<p><strong>Part 4: Linear Regression (single &amp; multiple)</strong></p>
<ul>
<li>
<p>Lab 11: Bonferroni t-test, Kruskal Willis test, review of statistical tests, linear regression with its R implimentation <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab11.pdf">[Slides]</a>
<a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab11-Demo.html">[R demo]</a></p>
</li>
<li>
<p>Lab 12: Statistical properties for least squares estimation <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab12.pdf">[Slides]</a>
<a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab12.html">[R soln]</a></p>
</li>
<li>
<p>Lab 13: Multiple linear regression, prediction interval, Bayesian statistics <a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab13.pdf">[Slides]</a>
<a href="http://yu-tong-wang.github.io/teaching/stat135_S20/Stat135-Lab13.html">[R demo]</a></p>
</li>
</ul>
</description>
</item>
<item>
<title>Public Health 292: Biostatistics Seminar for Graduate Students (2022-2024)</title>
<link>http://yu-tong-wang.github.io/teaching/courses/ph292/</link>
<pubDate>Sun, 01 May 2022 23:25:36 -0700</pubDate>
<guid>http://yu-tong-wang.github.io/teaching/courses/ph292/</guid>
<description><h2 id="course-information">Course Information</h2>
<ul>
<li>Professor: Corinne Riddell</li>
<li>Content: PH292 is a required seminar course for master students in Biostatistics. Topics include thesis writing, peer review of academic writing, statistical software development, principles of open and reproducible science, collaboration etiquette, and Diversity, Equity, and Inclusion (DEI).</li>
</ul>
<p>You can find my lecture slides about these DEI topics:</p>
<ul>
<li>
<p>
<a href="https://docs.google.com/presentation/d/1L0gr33iS55iPdZcyJR49hMK5YuMcE1URHVzgmUfNASw/edit?usp=sharing" target="_blank" rel="noopener">Eugenics and statistics</a>, i.e., how racism and other -isms are part of the history of statistics and play out in modern statistics.</p>
</li>
<li>
<p>
<a href="https://docs.google.com/presentation/d/1-B4Xq_bl-q_e-VYZWN8hPJRp9f30NRUlRgXbYjcm9cI/edit?usp=sharing" target="_blank" rel="noopener">Ethics and algorithmic fairness in health care</a>, including</p>
<ul>
<li>
<p>ethical concerns in the development of genetics,</p>
</li>
<li>
<p>representation disparity in genetic studies and clinical trials,</p>
</li>
<li>
<p>algorithmic fairness in machine learning with case studies.</p>
</li>
</ul>
</li>
</ul>
</description>
</item>
<item>
<title>Python Bootcamp (Spring, Summer 2022)</title>
<link>http://yu-tong-wang.github.io/teaching/courses/python_bootcamp/</link>
<pubDate>Sun, 01 May 2022 00:02:03 -0700</pubDate>
<guid>http://yu-tong-wang.github.io/teaching/courses/python_bootcamp/</guid>
<description><h2 id="course-information">Course Information</h2>
<ul>
<li>Content: The Center for Computational Biology offers a 5-day “
<a href="https://ccb.berkeley.edu/outreach/workshops-bootcamps/" target="_blank" rel="noopener">Introduction to Programming for Bioinformatics</a>” bootcamp. The goals of this course are to introduce students to Python, a simple and powerful programming language that is used for many applications, and to expose them to the practical bioinformatic utility of Python and programming in general. Specific topics include control flow, logic, data structures, data visualization and manipulations.</li>
</ul>
</description>
</item>
<item>
<title>Statistics 2: Intro to Statistics (Fall 2018, Spring 2019)</title>
<link>http://yu-tong-wang.github.io/teaching/courses/stat2_s19/</link>
<pubDate>Sat, 01 Jun 2019 22:12:15 -0700</pubDate>
<guid>http://yu-tong-wang.github.io/teaching/courses/stat2_s19/</guid>
<description><h2 id="course-information">Course Information</h2>
<ul>
<li>Professor: Cari Kaufman</li>
<li>Office Hours: W, F 9-11am @ 426 Evans</li>
<li>Lab Sections: M, W 12-1pm @285 Cory; M, W 1-2pm @332 Evans</li>
<li>Content: STAT 2 is Berkeley&rsquo;s introductory-level course on statistics for undergraduates. Topics include descriptive statistics, probability and inference.</li>
</ul>
</description>
</item>
<item>
<title>Single-cell and spatial transcriptomics data analysis with Seurat in R</title>
<link>http://yu-tong-wang.github.io/talk/sc_st_data_analysis_r/</link>
<pubDate>Wed, 10 Nov 2021 12:00:00 -0800</pubDate>
<guid>http://yu-tong-wang.github.io/talk/sc_st_data_analysis_r/</guid>
<description><h2 id="materials">Materials:</h2>
<p>The tutorial can be downloaded in both <a href="http://yu-tong-wang.github.io/talk/sc_st_data_analysis_R.html">[html]</a> and <a href="http://yu-tong-wang.github.io/talk/sc_st_data_analysis_R.Rmd">[Rmd]</a> formats.</p>
<h2 id="topics">Topics:</h2>
<p>A thorough walk-through is provided to perform computation and data analysis on single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics data using Seurat and other packages in R. Other topics include the explanation of a general Seurat object, and the conversion of sequencing data formats between R and Python.</p>
<h2 id="acknowledgements">Acknowledgements:</h2>
<p>The tutorial was inspired by the computational assignments I created together with Dr. Yun S. Song in a graduate course (CMPBIO 290: Algorithms for single-cell genomics) at University of California, Berkeley in Fall 2021. The tutorial material was largely based on many open-source resources, especially the
<a href="https://satijalab.org/seurat/" target="_blank" rel="noopener">Seurat tutorials</a> from the Satija Lab. I would also like to thank Salwan Butrus for helpful feedback and suggestions.</p>
</description>
</item>
<item>
<title>XYZeq: Spatially-resolved single-cell RNA-sequencing reveals expression heterogeneity in the tumor microenvironment</title>
<link>http://yu-tong-wang.github.io/publication/xyzeq/</link>
<pubDate>Mon, 15 Mar 2021 21:08:09 -0800</pubDate>
<guid>http://yu-tong-wang.github.io/publication/xyzeq/</guid>
<description><p><span>*</span> indicates equal contribution, <span>†</span> indicates co-corresponding authors.</p>
</description>
</item>
<item>
<title>Decoding cell-cell communication through deep learning reveals novel ligand-receptor pairs from spatial transcriptomics</title>
<link>http://yu-tong-wang.github.io/publication/ggpair/</link>
<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
<guid>http://yu-tong-wang.github.io/publication/ggpair/</guid>
<description><p><span>*</span> indicates equal contribution, <span>†</span> indicates co-corresponding authors.</p>
</description>
</item>
<item>
<title>Teaching</title>
<link>http://yu-tong-wang.github.io/teaching/index_/</link>
<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
<guid>http://yu-tong-wang.github.io/teaching/index_/</guid>
<description></description>
</item>
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