The OmicsLogic Transcriptomics program will introduce real-world applications of RNA-seq and provide participants with hands-on skills and logical background to the full RNA-seq analysis approach. We will review methods of quantitative and qualitative analysis of mRNA expression in a sample. Other session will focus on how data is generated using Next Generation Sequencing. Practical sessions will guide participants to use the methods we review on several project datasets to practice generating a table of expression from raw FASTq files and perform subsequent analysis of this table of gene and isoform expression.
Overview of the entire course, Overview of the tools and resources for this program (edu.t-bio.info courses, projects and datasets, and the T-BioInfo Analytics Platform),...
Role of pre-processing in standard RNA-seq pipelines (Trimmomatic and PCR-clean), Mapping techniques: mapping on the transcriptome, mapping on the genome and combined strategies (Bowtie, BWA,...
Quantification and Generating a table of expression: RSEM, HTSeq, and Sailfish Case study & Hands-on using Cell line project Case study & Hands-on using...
Filtering, removing noise and Normalization Techniques Correlation – detecting correlation of features and factors Regression, factors, and features – Factor Regression Analysis overview
Unsupervised machine learning (PCA, H-Clust, K-means) Clustering of samples using gene expression profiles Clustering of genes by expression profiles across samples
Supervised Machine learning techniques Factor Regression Analysis, Decision Trees, Random Forest Challenges associated with different kinds of machine learning
Discriminant Analysis and Support Vector Machines Feature Selection and expanding the list of features Data Visualization
Annotation using Gene Ontology Human GAGE: Gene Set Enrichment Analysis Statistical Significance and Reproducibility
Single-cell transcriptomics (SCT) Introduction to SCT, History of SCT, NGS Techniques, Capture techniques, Quantification, scRNA-seq data preparation & Counts
ScRNA data analysis, Publication & projects: Drop-seq data from Ye et al., 2017, Results & Interpretation.
Planning your project, 2 Q&A sessions, 1 Presentation Case Studies & Publications Datasets