OmicsLogic Transcriptomics Training Program

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