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Upcoming Nanocourses - Fall 2022

Please note that all Nanocourses are being offered IN-PERSON only!

Note for UTSW PostDocs: we recommend communicating with the PostDoc Office to ensure your eligibility for academic credit.

Course Name Dates
Data Science using R 9/8/2022 and 9/9/2022
Time Series Analysis 9/23/2022 and 9/30/2022
Computational Image Analysis 10/13-10/14 and 10/17-10/18/2022
Programming for Beginners (with MATLAB) 11/9/2022 and 11/10/2022

Time Series Analysis

September 23rd and 30th, 9 AM to 5 PM; Room G9.102 Directions

This course aims to promote understanding of time-series data and their processing/analysis methods. On the first day, we will provide an introduction to techniques for time-series data processing, analysis, and modeling. On the second day, we will cover various time-series data analysis techniques being used for neural spiking data. Day 1: Time-series signal processing (filtering, imputation, etc.), Feature extraction from time-series signals, Autocorrelation Function (ACF), AR modeling Day 2: Neural spiking data analysis (Spike train statistics, Reverse-correlation to estimate receptive fields, Poisson neuron model, Generalized linear model)

Applications are open to any person at UTSW or in the surrounding community who are interested in learning computational analysis of time series data. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-04 Special Topics - Time Series Analysis, PDRT 5095-01 Special Topics in Bioinformatics - Time Series Analysis

Registration closed. Nanocourse full. Class space is limited. Registration form responses will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email.

Lead Instructor: Jungsik Noh

Other instructors: Jeon Lee, Wenhao Zhang

Computational Image Analysis

October 13-14 and 17-18th, 9 AM to 5 PM; Room G9.250A Directions

This course will cover an introduction to key topics and concepts of computational image analysis, in the form of lectures followed by hands-on exercises. Platforms that will be used: MATLAB, CellProfiler, ImageJ/FIJI. Day 1: Basic image processing, Segmentation Day 2: Segmentation (continued), Diffraction-limited object detection, 3D Day 3: More advanced image analysis such as colocalization and tracking, Feature extraction Day 4: Machine learning/data analysis

Applications are open to any person at UTSW or in the surrounding community who are interested in learning computational analysis of imaging data. For UTSW graduate students and trainees, academic credit (2 credit hours) is available. BME 5096-05 Special Topics - Computational Image Analysis, PDRT 5095-03 Special Topics in Bioinformatics - Computational Image Analysis

Please register using this form Class space is limited. Registration form responses will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email.

Lead Instructor: Khuloud Jaqaman

Other instructors: Andrew Jamieson, Kevin Dean

Programming for Beginners (with MATLAB)

November 9th and 10th, 9 AM to 5 PM; Room G9.250A Directions

This course would be useful for students with an interest in learning the most elementary steps in software programming. The course will use Matlab as the programming platform, but the coding elements taught are fully agnostic to the programming language. The goal of the course is not to teach Matlab, but to break down for the novice the mystery of coding and to illustrate the basic thinking behind structuring a set of instructions to produce something intelligible. Students will learn how to write and read simple codes and how to evaluate the progression of a program sequence, both numerically as well as through graphical representations of intermediate and final results. As a final project students will have to choice of program a classic algorithm for data clustering or a classic algorithm for the simulation of biochemical reactions. Day 1: Elementary set of commands (ops on arrays/matrices; loops; decisions), Programming interface including debugging scripts vs functions; variable name space, Benchmark test: ability to read a piece of code Day 2: Plotting including dynamic plots, Random number generation Example problem: calculate pi using a randomized 'droplet fall' on circular area, Benchmark tests for programming: k-means or Gillespie algorithms

Applications are open to any person at UTSW or in the surrounding community who are interested in basic programming. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-06 Special Topics - Programming for Beginners(with MATLAB), PDRT 5239-01 Special Topics in Bioinformatics - Programming for Beginners(with MATLAB)

Class space is limited. Registration form responses will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email. Registration form coming soon!

Lead Instructor: Gaudenz Danuser

Other instructors: Qiongjing (Jenny) Zou

Data Science using R (Registration closed)

September 8th and 9th 2022, 9 AM to 5 PM; Room NB2.100A

This course would benefit students who pursue avdvanced R programing techniques for data science. We will provide information about key elements for data science and machine learning, including how to properly preprocess data, how to select meaningful features from the data, how to identify data clusters, and how to build a predictive model. We will then cover statistical test basics and provides semi-hands-on sessions on how to utilize the statistics for biomarker discoveries. Day 1: Data preprocessing, Feature selection/dimensionality reduction, Data clustering, Predictive models Day 2: Statistical test basics, Biomarker discovery I: metabolomics/proteomics data, Biomarker discovery II: RNA-seq data

Applications are open to any person at UTSW or in the surrounding community who are interested in learning R for data science. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-02 Special Topics - Data Science using R, PDRT 5095-01 Special Topics in Bioinformatics - Data Science using R

Class space is limited. Registration form responses will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email.

Lead Instructor: Jeon Lee

Other instructors: Yingfei Chen, Zach Connerty-Marin, Austin Marckx


Past nanocourses

Spring-Summer 2022

Course Name Dates
Architectures and Applications of Deep Learning 4/11/2022 and 4/12/2022
Introduction to R for Beginners 5/9/2022 and 5/10/2022
Introduction to NGS Analysis 5/19/2022 and 5/20/2022
Advanced Concepts of Deep Learning 6/13/2022 and 6/16/2022
Advanced NGS Analysis 6/27/2022 and 6/28/2022
Single Cell Genomics 7/18/2022 and 7/19/2022

Architectures and Applications of Deep Learning (Registration closed)

April 11th and 12th 2022, 9 AM to 5 PM; NB2.100A

Explore the driving principles behind state of the art deep neural network architectures for generative modeling with GANS (CGAN, WGAN, InfoGAN, CycleGAN), unsupervised learning with autoencoders (CVAE), image analysis (Vision Transformers and CNNs), learning from limited data (Siamese nets), and sequence learning (LSTMs). Learning objectives for this practical course are: (1) Learn to implement these architectures using leading python frameworks: TensorFlow/Keras and PyTorch, (2) Learn design patterns that increase accuracy and network understanding, (3) Learn best practices to achieve winning performance. Throughout you will learn practical applications of deep learning for prediction from a wide array of domains including tabular data and high dimensional signal, image, and video data.

Applications are open to any person at UTSW or in the surrounding community who are interested in applying deep learning solutions to their machine learning research projects. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-02 Special Topics - Architectures and Applications of Deep Learning, PDRT 5095-01 Special Topics in Bioinformatics - Architectures and Applications of Deep Learning

Class space is limited. Responses to this survey will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email.

Lead Instructor: Albert Montillo

Other instructors: Michael Holcomb, Alex Treacher

Introduction to R for Beginners (Registration closed)

May 9th and 10th 2022, 9 AM to 5 PM; NG3.202

Get introduced to using R as a data analysis language. During this course, the student will be able to install and use R for basic data analyses projects. We will demonstrate how to read, write, and format data for appropriate analysis, testing, and tabulation. This course will be a hands-on experience with problems and a variety of result reproduction exercises in R. Advanced topics and extensions will be taught in a subsequent nanocourse.

Applications are open to any person at UTSW or in the surrounding community who are interested in learning R for their data analysis in research. Class space is limited. Responses to this survey will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-03 Special Topics - Introduction to R for Beginners, PDRT 5139-01 BIOINFORMATICS INTRODUCTION TO R FOR BEGINNERS LEVEL I

Registration closed, nanocourse full.

Lead Instructor: Christopher Chaney

Other instructors: Amit Amritkar, Micah Thornton

Introduction to NGS Analysis (Registration closed)

May 19th and 20th 2022, 9 AM to 5 PM; NB2.100A

This course will cover the basics of next-generation sequencing (NGS) technologies and computational analysis. We will provide an overview of NGS sequencing of DNA, RNA, and ChIP, and explain the FASTQ file format. This course includes hands-on practice for class participants to perform sequence alignment using programs such as BWA, Bowtie, and HISAT, and examine alignment output. We will also explore various aspects such as quality control of sequencing data and key algorithms behind sequence alignment and variant calling programs.

Applications are open to any person at UTSW or in the surrounding community who are interested in using NGS analysis for their sequencing datasets. Class space is limited. Responses to this survey will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-04 Special Topics - Introduction to NGS Analysis, PDRT 5095-02 Special Topics in Bioinformatics - Introduction to NGS Analysis

Registration closed, nanocourse full.

Lead Instructor: Bo Li

Other instructors: Daehwan Kim, Christopher Chaney, Micah Thornton

Advanced Concepts of Deep Learning (Registration closed)

June 13th and June 16th 2022, 9 AM to 5 PM; NB2.100A

This course will provide an introduction to key and emerging concepts and ideas in deep learning. The 1st part of this course will introduce the design and principle behind recent advances in model architecture: transformers (including several efficient transformer designs), graph neural networks, and several other new architectures that utilize attention-like multiplicative updates. The 2nd part of this course will cover mathematics and algorithms of generative probabilistic modeling with deep learning, including energy-based models, variational autoencoder, generative adversarial network, normalizing flow, neural ODE, and diffusion probabilistic models. Conceptual advances will be the focus of this nanocourse. You are encouraged to bring a modeling problem of interest for the group discussion session.

Applications are open to any person at UTSW or in the surrounding community who are interested in learning about advancements in deep learning. Class space is limited. Responses to this survey will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-05 Special Topics - Advanced Concepts of Deep Learning, PDRT 5095-03 Special Topics in Bioinformatics - Advanced Concepts of Deep Learning (Note: this course is advanced and requires some basic knowledge of machine learning, deep learning, and programming)

Registration closed, Nanocourse full

Instructor: Jian Zhou

Advanced NGS Analysis (Registration closed)

June 27th and 28th 2022, 9 AM to 5 PM; NB2.100A

This course covers a few advanced yet useful topics regarding next-generation sequencing (NGS) computational analysis. The course includes hands-on practice for class participants to perform HLA gene typing and haplotype-resolved assembly, gene expression quantification using Kallisto and Salmon, and differential gene expression analysis using DESeq2. (Particular subtopics are subject to change as we refine the course.) Along with this practice, the course will explore core algorithms and statistical models utilized in the programs featured in the course.

Applications are open to any person at UTSW or in the surrounding community who are interested in learning about advanced NGS analysis. Class space is limited. Responses to this survey will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-06 Special Topics - Advanced NGS Analysis, PDRT 5095-04 Special Topics in Bioinformatics - Advanced NGS Analysis (Note: this course is advanced and requires prior knowledge of NGS analysis methods and applications in general).

Registration closed. Nanocourse full.

Lead Instructor: Daehwan Kim

Other instructors: Bo Li, Micah Thornton

Single Cell Genomics (Registration closed)

July 18th and 19th 2022, 9 AM to 5 PM; location NB2.100A

This course covers the basics of single-cell technologies and computational analysis. We will provide overviews and key algorithms for single-cell RNA-Seq, single-cell ATAC-Seq, and multiome analysis. This course includes hands-on practice to perform analyses from raw data to quality control, clustering, visualization, and trajectory inference. It also includes more advanced topics including multiome analysis, spatial transcriptomics, and single-cell perturbation. This course requires proficiency with R and Python.

Applications are open to any person at UTSW or in the surrounding community who are interested in learning about advanced NGS analysis. Class space is limited. Responses to this survey will be reviewed and acceptance in the course will be determined from the best match between participant experience and benefit derived from the training and the course content. Decisions will be conferred via email. For UTSW graduate students and trainees, academic credit (1 credit hour) is available. BME 5096-07 Special Topics - Single Cell Genomics, PDRT 5095-05 Special Topics in Bioinformatics - Single Cell Genomics

Registration closed, nanocourse full.

Lead Instructor: Genevieve Konopka

Other instructors: Gary Hon, Tao Wang, Ashwinikumar Kulkarni, Emre Caglayan, Yi Han, Yihan Wang, Daniel Armendariz