Question: Strange MT% in 10X scRNA-seq data analysis
gravatar for maria2019
13 months ago by
maria2019100 wrote:

I have 10X scRNA-seq data for a hESC and after the alignment using cellrenger, I used seurat V3 to cluster cells. Since this is a normal cell line, I expected not to see any clusters of cells. However, the mt% in seurat is VERY strange

enter image description here

and has 2 clusters which finally results in getting 2 separate clusters in the final scRNA-seq analysis (I only keeo mt%< 6). Is such mt% normal? removing 2-6 mt% will reduce the reads from 500,000 to 15,000 and does not seem right! removing 0-2% mt and keeping 2-6 %mt also does not look right to me.

Any suggestions why there is such mt%? Is it only experiment problem? if not, how should I treat it so that i will not lose a lot of reads.

seurat mt% 10x scrna-seq • 1.7k views
ADD COMMENTlink modified 13 months ago by ATpoint34k • written 13 months ago by maria2019100

Please read and follow How to add images to a Biostars post. I did the changes in the toplevel question for you this time.

ADD REPLYlink written 13 months ago by ATpoint34k
gravatar for Arup Ghosh
13 months ago by
Arup Ghosh2.4k
Arup Ghosh2.4k wrote:

Can you plot the MT percent graph with y.max =20 ? There can be several reasons for high mitochondrial content like

1) An issue during the library preparation.

2) A higher number of dead and dying cells.

3) MT content will vary among different cell types.

According to most of the sources up to 5-10% mitochondrial content is fine to continue with. Just filter out the cells with more than 10% or 5% depending on the number of cells.

Ref: Biostars.

Ref: 10xportal

Ref: Seurat Example

ADD COMMENTlink written 13 months ago by Arup Ghosh2.4k

Thanks for your comment and the provided links. Below is the link to the Ymax=20

The problem that I have is that my mt% is not consistent and it is separated to 2 parts. There are also low% MTs with high read counts. I do not have high mt% in particular, but I have very strange mt% and I am not sure if that is the result of the experiment or the nature of the human embryonic stem cell it self. Does it make sense to remove cells that have below 1.75% mt and only keep those with 1.75-6% mt%?

ADD REPLYlink modified 13 months ago • written 13 months ago by maria2019100

Not sure if this is applicable in your case but something to consider: Mitochondrial Gene percentage threshold in single cell RNA-Seq

ADD REPLYlink written 13 months ago by genomax83k

Maybe there are two metabolically distinct subpopulations is present in the dataset. Also, the UMAP shows two distinct population of cells. I think there will not be any major issue if you consider the cells having <2% MT content, maybe these cells are differentiated and became metabolically less active. It will be best to check top 10/20 features from the different clusters to identify the cell types.

ADD REPLYlink modified 13 months ago • written 13 months ago by Arup Ghosh2.4k
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