Fall 2022

Data Science using R

September 8 & 9
9 AM to 5 PM
NB2.100A

This course would benefit students who pursue advanced 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.
Academic credit (1 credit hour) is available. UTSW graduate students use BME 5096-02 Special Topics - Data Science using R and UTSW PostDocs use PDRT 5095-01 Special Topics in Bioinformatics - Data Science using R.

Registration closed. Nanocourse full.

Lead Instructor: Jeon Lee

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

Time Series Analysis

September 23 & 30
9 AM to 5 PM
G9.102

This course aims to promote understanding of time-series data and their processing/analysis methods. Starting with an introduction to techniques for time-series data processing, we will cover analysis, modeling, and 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.
Academic credit (1 credit hour) is available. UTSW graduate students use BME 5096-04 Special Topics - Time Series Analysis and UTSW PostDocs use PDRT 5095-01 Special Topics in Bioinformatics - Time Series Analysis

Registration closed. Nanocourse full.

Lead Instructor: Jungsik Noh

Other instructors: Jeon Lee, Wenhao Zhang

Computational Image Analysis

October 13-14 & 17-18
9 AM to 5 PM
G9.250A

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.
Academic credit (2 credit hours) is available. UTSW Graduate Students use BME 5096-05 Special Topics - Computational Image Analysis and PostDocs use 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

ANNOUNCEMENT
The Programming for Beginners (using MATLAB) nanocourse will be offered on March 8 and 9, 2023 instead of November 2022. Check out the Spring 2023 page under Nanocourses tab.