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NGS for natural scientist
  • 1. Preface
    • How to use this book
    • Motivation
    • Genomic data science as a tool to biologist
    • Next Generation Science (also NGS)
  • 2. Getting started
    • A step by step pipeline tutorial
    • Sequencing chemistry explained by Illumina
    • Joining a course
    • RNA quality and Library prep
    • (optional) My click moment about "Why Linux"
  • 3. Good-to-know beforehand
    • Experiment design
    • Single-end and Paired-end
    • Read per sample and data size
    • Normalization - RPKM/FPKM/TPM
    • Gene annotation
  • 4. Setting up terminal
    • My Linux terminal
    • Linux environment
    • R and RStudio
    • PATH
  • 5. FASTQ and quality control
    • Getting FASTQ files from online database
    • FASTQ quality assessment
  • 6. Mapping/alignment and quantification
    • Salmon
    • DESeq2
  • 7. Visualization
  • 8. Single cell RNA-Seq
  • 9. AWS cloud and Machine Learning
    • Machine Learning in a nutshell
    • R vs Python
    • Setting up ML terminal
    • Data exploration
  • (pending material)
    • graphPad
    • readings for ML
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8. Single cell RNA-Seq

Also known as scRNA-Seq - I really need to show you how not complicated to make a new biotech company nowadays, and you may as well be the next Amgen if you hit the jackpot.

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Last updated 2 months ago

So first of all I didn't start with this but I assessed this is a good starting point (after I think I know what I am doing) -

Totally not going into analytical details but the major revolution of single cells are made around how to form the PCR compartment at nano-scale. We first started with emuPCR like Roche 454, proceed to a so much efficient emulsification device such as 10X chrome controller, while both of them are more or less the same technically. The latest major breakthrough was to amplify the genetic fragement within the cells by detergent permeabilization (yes, the same thing we used for IHC staining since 100 years ago). And then we have slide-seq which adds tags to cells based on their location in the organ by slicing then on a fixed glass slides. Honey comb ideas to separate cells into thousands of compartment for sequencing. But the point is this paragraph has already funded hundreds of start-up no matter the size of improvement. And from this I would say I am looking at another 10 years before scRNA-seq to become a mainstream - cost, benefit, data, informaiton, all comes hand in hand.

Technology aside, the major merit of single cell seq over bulk cell sequencing, at the time of writing, is that it provides more layers in the data analysis. We can bulk seq a kidney, or we can sequence different kidney cell type of the same kidney, to have a deeper and wider picture of what's going on in this particular kidney or this group of kidney that exhibit similar pathology. That said, if we can harness the technical improvement, although tiny and taking baby steps, to increase the layers of the data, e.g. spatial information, cellular subtype, number of different cells between specimens etc. Will that be information overflow in your case, or you cannot proceed without these information, it is all up to you and down to you.

https://hbctraining.github.io/scRNA-seq/