The OmicsLogic Transcriptomics 2020 online program is a great introduction to the real-world applications of RNA-seq analysis. This 3-month long program will start January 2020 and will provide participants with clear examples and hands-on practical skills in RNA-seq. The program will follow the logical steps needed to prepare, process, quantify and analyze RNA-seq data, detailing the full process of RNA-seq analysis.
As RNA-seq becomes more popular and utilized in a variety of research areas, deep understanding of analysis methods, their configuration options and typical challenges are critical. That is why in this program, we will spend considerable time discussing how transcriptomic data is generated using Next Generation Sequencing and how it is analyzed using methods of quantitative and qualitative analysis of mRNA expression. The online sessions will also focus on practical hands-on examples – in these sessions, we will guide participants to apply the methods we review to a variety of projects. The participants will then learn how to prepare and analyze data from public domain datasets. These practice examples will focus on generating a table of expression from raw FASTq files and performing downstream analysis of the table of gene and isoform expression.
Here are some of the topics we will cover in this program:
- Processing of Gene expression NGS data
- T-Test, False Discovery Rate and Differential Gene Expression
- Data ScienceBasics: Exploratory Data Analysis
- Statistical Analysis of Gene Expression
- Data Mining using the Multi-Dimensional Expression Data
- Using Machine Learning for Expression Data
- Biological Interpretation
- Single-Cell Transcriptomics
- ScRNA Transcriptomics Data Analysis
In addition to weekly meetings with our trainers, participants will benefit from an in-depth review of these topics offered in our online courses. These include all the relevant terminology and references one will benefit from going into the program.
These courses are bundled together in the online Transcriptomics series. Here is what these courses cover:
Transcriptomics 1: Introduction
This course is an introduction to Next Generation Sequencing for the study of RNA expression. Topics include quantitative and qualitative analysis of RNA in a sample, data preparation using Next Generation Sequencing and preparation of a table of expression from raw FASTq files. Visualization of high-dimensional data using Principal Component Analysis (PCA).
- Mapping raw reads to reference genome and transcriptome
- Detecting junctions and assembly of isoforms
- Quantification of mRNA: Gene, isoform and exon expression table
Transcriptomics 2: Differential Gene Expression
Transcriptomics 2 is a continuation of the Transcriptomics 1 and it focuses on finding differences in gene expression. We will start with differential gene expression and continue to look at typical challenges that can be resolved using other methods. We will look at t-test, then use DESeq2 to run a differential gene expression pipeline and then use Factor Regression Analysis. As a result, you will learn to detect obvious differences between pre-set groups as well as expand that idea to more subtle differences represented by factors that might interact with each other.
- DIfferential gene and isoform expression
- Hypothesis testing, p-values, and normalization
- Multivariate analysis using regression
Transcriptomics 3: Machine Learning for RNA-seq data
Transcriptomics 3 is a course on advanced analytical approaches for RNA-seq data. It reviews supervised and unsupervised machine learning methods for data exploration and classification. Using an example from precision medicine, the methods are demonstrated to work together to understand cancer subtypes and the use of this information to determine how a new sample can be classified.
- Data exploration using dimensionality reduction and clustering
- Classification and discriminant analysis for labeled datasets
- Cancer subtypes based on gene expression (breast cancer classification)
Transcriptomics 4: Single cell RNA-seq
This course is a review of single cell RNA-seq. scRNA-seq has been demonstrated as a powerful technique for classification of tissue-specific cells and the study of time-course data for thousands of single-cell samples. Data preparation techniques and sparse properties of such data require a new set of methods for its analysis.
- Techniques and protocols for scRNA-seq data preparation
- Major analytical steps for processing raw single cell sequencing data
- Analytical techniques to visualize and cluster scRNA-seq data
The Transcriptomics 2020 will be an online program that participants can attend from anywhere in the world using ZOOM. The program sessions will start at 9:30 a.m. CST and last until 10:30 a.m. CST for planned dates. For more details about the program curriculum, planned dates and registration details, please check out the program Organization page where we share updates about the program and you can communicate with the mentors and see activity of fellow participants.
Program Dates – 14th January – 14th March Registration Price – $135 USD*
*we offer an early-bird discount of 20% until December 31, 2019 – the discount code will be emailed to you upon filling out the form.