Complete single-cell RNAseq analysis walkthrough | Advanced introduction

This is a comprehensive introduction into single-cell analysis in python. I recreate the main single cell analyses from a recent Nature publication. I explain the basics of single-cell sequencing analysis and also introduce more advanced topics. I cover doublet removal, preprocessing, integration, clustering, cell identification, differential expression, gene-set enrichment, non-parametric statistical testing, single-cell gene signature scoring, plotting, and more. This tutorial is suitable for both advance and new single-cell users. I use the scanpy and SCVI packages heavily.

Notebook:

Reference:

0:00 intro
1:18 data
6:35 doublet removal
13:03 preprocessing
23:12 Clustering
27:42 Integration
39:56 label cell types
58:28 Analysis

[daffy_duck_phd]

This video is so helpful, I'm just starting a PhD in bioinformatics and solid resources are scarce, so thank you! The only thing i would say is that a little more explanation as to why you are doing some of the things you are doing would be super helpful for a beginner like me. but honestly this is a great video and looking forward to binge watching the rest of your videos

[mocabeentrill]

Shout out to you bro! After years of wet lab practice, I'm transitioning to bioinformatics and you're one of my inspirations.

[hrisivanov]

Man, you absolutely saved me! Suddenly, everything makes sense now. Subscribed!

[remia]

I'm glad you followed through with your promise to make this long video. It's already received thousands of view in just a few months. Cool! Please, keep them coming!

[hyang]

This is the best tutorial of scRNA-seq analysis with python I've ever seen!

[lukesimpson]

Thanks for the great video, I was wondering though, why "collapse" clusters? if the clustering algorithm has determined that there is multiple macrophage clusters and we then lump them as one doesn't that negate the clustering? Doesn't it mean there is more diversity within macrophages than between monocytes and DCs for example? I am curious what your thoughts are here as I have been pondering this for some time. Thank you.

[young-kookkim]

Thank you so much! This video is perfect for those who want to analyze scRNA-seq data!

[saraalidadiani]

thank you for the nice video, regarding to the part for making the cell typ fraction plot (form this part of the code till end of this part: may you also please explain how to do it in R with the Seurat objecet? thanks

[sergestsofack]

nice video, where can I found these codes so I can just copy and paste ?

[Alano-mgqh]

Great tutorial. Will definitively be shared in a LinkedIn post. Thank you for the hard work and good documentation.

[shaolinma]

Hello, when you run the analysis of counting cells, where is the doublet from? I thought the doublets were filtered in the preprocessing steps. Thanks

[SanzidaAnee]

Hi, my doublet distribution graph is different than your one. I did not get any value >1. Why?

[梁一鹏-vn]

It was 11: 00 p.m. in China when I clicked on this video, and it was already 2: 00 a.m.,
This is the most exciting single cell tutorial i' ve ever seen
you are so good！！！

[AthensNwo]

Is there a reason one uses filtered feature matrix vs raw feature matrix? what I understand the filtered feature file is already quality controlled data done by the cell ranger software, wouldn't it be better to use the raw feature matrix to do quality control?

[ismailgumustop]

Thank you so much for the tutorial! It's quite useful for hands-on learning of scRNA Seq. I have some questions related to "integration" part. While I was working on my dataset, I realized the number of cells (observations) on some samples was quite different. For example:
Sample 1 - 1600 cells
Sample 2 - 560 cells
Sample 3 - 3000 cells
1. I would like to know does this affects my analysis.
2. Do I need to apply something like scaling or oversampling or else?

I would be grateful if you can help.

Best

[YC-utff]

Thank you for this helpful video!!! I found that the loss of model(37:17) is actually quite high. Would this influence the performace of this model?

[emanueleraggi]

Thank you for this amazing video. Is there any possibility for a PCA and ANOVA analysis for the next tutorials? Thanks for sharing your knowledge!

[GiwonCho-pw]

The video is soooo helpful. you are my life saver. However, I wanted to try to using diffxpy especially 'wald test'. the error 'ZeroDivisionError: float division by zero' is happending when I use this code:

res = de.test.wald(data = subset,
formula_loc= '~ 1 + cell_type',
factor_loc_totest='cell_type'
)

I am using MacOS. and in Github, some people is undergoing same problem, guessing the problem occurs when we use MacOS.
do you have any other solution?

[repliedfob]

i downloaded the tutorial from your github and am running it line by line but have ran into an issue. when running line 84 sc.tl.umap(adata)
i get TypeError: unhashable type: 'list'
i havent modified the code in any way. adata is of type "<class

[katarinavalentincic]

great video, invaluable for beginners with coding like myself! What if you have adata which hasn't been filtered yet and you want to filter it to retain only cells that are present in another Anndata object previous_adata, because they have already been filtered with QC and to do that based on indices of those cells in previous_adata?