Course curriculum

    1. Getting to know you

    2. Introduction to Single-Cell RNA Seq

    3. Experimental Design

    1. Get started with Trailmaker™

    2. Introduction to Trailmaker™

    3. Trailmaker Pipeline Overview

    4. Introduction to FASTQ files

    5. FASTQ files processing - Behind the Scenes of Trailmaker Pipeline

    6. [Optional] FASTQ files processing - Practical Lesson

    7. Trailmaker Insights Overview

    8. File Conversion

    1. Introduction

    2. Step 1 - Classifier Filter

    3. Step 2 - Cell Size Distribution Filter

    4. Step 3 - Mitochondrial Content Filter

    5. Step 4 - Number of genes vs transcripts filter

    6. Step 5 - Doublet Filter

    7. Step 6 & 7 - Data Integration & Configuring Embedding

    8. Real World Examples - Part 1

    9. Real World Examples - Part 2

    10. Real World Examples - Part 3

    1. Data Integration step - part 1 - Normalization

    2. Data Integration step - part 2 - Feature selection

    3. Data Integration step - part 3 - Dimensionality reduction

    4. Data Integration step - part 4 - Data integration

    5. Data Integration step - part 5 - How to assess the quality of data integration

    6. Data Integration step - part 6 - Exclusion of specific gene categories

    7. Data integration step - part 7 - Downsampling

    1. Data Exploration - part 1 - Embedding

    2. Data Exploration - part 2 - Clustering

    3. Data Exploration - part 3 - Cell sets

    4. Data Exploration - part 4 - Cell type annotation

    5. Data Exploration - part 5 - Heatmap and marker genes

    6. Data Exploration - part 6 - Differential expression analysis

    7. Data Exploration - part 7 - Pathway analysis

    1. Plots and Tables - part 1 - Cell sets and Metadata

    2. Plots and Tables - part 2 - Trajectory analysis

    3. Plots and Tables - part 3 - Gene expression

    4. Plots and Tables - part 4 - Differential expression

    5. Plots and Tables - part 5 - Reproducing figures from a paper

About this course

  • 42 lessons
  • 10 hours of video content

Social proof: testimonials

Md Anwarul Karim, MBBS, PhD

Department of Neurology - Baylor College of Medicine - Houston, Texas

This is an amazing course for any researcher entering into the field of single cell transcriptomics data analysis. The contents covered in this course provides a great conceptual understanding for newcomers especially for the people from wet lab background. This will also be useful for people not doing the data analysis themselves but want to have a conceptual understanding of the workflow and what the common figures presented in single cell-related scientific papers means. I would highly recommend it to anyone interested in single cell transcriptomics.

Jawwad Ahmad

Field Application & Sales Specialist at Global Marketing Services

No doubt it is one of the most knowledgeful, easy-to-understand course I have ever done for scRNA seq analysis. Thanks for offering such a wonderful, amazing and resourceful course.

Olga Bianciotto

University of Turin

I would recommend it, the platform seems to be very useful and the course also provides a clear general understanding of the steps of scRNAseq data analysis.

Samadhi Kulasooriya

Creighton University School of Medicine

I would definitely recommend others. It was very easy to understand and all the course material were well put together.

Thai Ha Hoang

Faculty of Medicine, Université Libre de Bruxelles, Belgium

I would recommend the course as it is very well structured and easy to understand for people not familiar with scRNA.

Pricing options

This course is provided to you for free by Parse Biosciences