What you will learn:
In this course, we will talk about single cell RNA-seq: how it is generated, how to analyze it and what specific challenges need to be considered. Single cell RNA-seq or “scRNA-seq” has been demonstrated as a powerful technique for classification of tissue-specific cells and is used to study cell differentiation using time-course experiments. However, specialized data preparation techniques and high noise-signal ratio of this type of data require specialized approaches to its analysis. In addition, resulting expression tables contain sparse data that need to be prepared for downstream analysis with various normalization and imputation techniques.
In this course, you will learn:
- Techniques used to prepare scRNA-seq data
- Major analytical steps for processing raw data and extracting gene expression information for each cell
- Commonly used analytical techniques to visualize and discriminate the data into groups for annotation
Drop-seq, Single cell RNA-seq, Single Cell Transcriptomics (SCT), Unique Molecular Identifier (UMI), Single-cell Transcriptomes Attached to MicroParticles (STAMP),
- Lectures 7
- Quizzes 0
- Duration 50 hours
- Skill level All levels
- Language English
- Students 180
- Certificate Yes
- Assessments Yes
All the key takeaways from the Transcriptomics 1 up until this last module made it easy to cross the t's and dot the i's in understanding the Transcriptomics 4. I found it as the last bridge to put all the transcriptomics modules into perspectives.