Our study have scRNA + scATAC sequencing on same sample, however, there are more than 40% mito (mitochondrial ) genes in about 50% cells. After repeat with different experiment methods, this percentage can not be decreased.
Could you kindly suggest which bioinformatic method to deal with such problems?
Dear Wang Lu ,
There are a few bioinformatic methods that can be used to deal with high percentages of mitochondrial genes in single cell RNA-seq (scRNA-seq) data. One approach is to use a mitochondrial gene filtering tool, such as the one provided in the Cell Ranger software package, to remove mitochondrial genes before downstream analysis. Another approach is to perform dimensionality reduction, such as Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE), and visually inspect the data to identify and exclude cells with high mitochondrial gene expression. Additionally, some studies have used a method called "mito-correction" that normalizes the mitochondrial gene expression levels across all cells.
Another method is to use a mitochondrial gene filtering tool, such as the one provided in the Cell Ranger software package, to remove mitochondrial genes before downstream analysis. This can be done by using the mitochondrial gene expression as a cell quality filter, removing cells with high mitochondrial gene expression, or only considering cells with low mitochondrial gene expression for downstream analysis.
Additionally, some studies have used a method called "mito-correction" that normalizes the mitochondrial gene expression levels across all cells. The main idea behind this method is to use the mitochondrial gene expression as a reference to normalize the expression of all the other genes in the dataset. This can be done by using the mitochondrial gene expression as a cell quality filter, removing cells with high mitochondrial gene expression, or only considering cells with low mitochondrial gene expression for downstream analysis.
It is worth noting that while these methods can help to reduce the impact of high mitochondrial gene expression on downstream analysis, they may also remove valuable information about the mitochondrial physiology of the cells in question. Therefore, it is important to carefully consider the goals of the analysis and the potential impact of these methods before applying them.
Nicolò M. Villa Thank you for your detailed reply.
Since the leader of our lab has great expectation for this study, he wants to know whether the single-cell dataset with such severe degradation can be accepted by journals with >10 impact factor.
Could you kindly suggest which study have such severe cell degradation and published in a top journal?
Cell degradation is a common issue in single-cell sequencing, including scRNA-seq and scATAC-seq. Degradation can result in the loss of RNA or chromatin accessibility, leading to incomplete or biased data. Here are a few approaches to deal with the problem of cell degradation in scRNA + scATAC single-cell sequencing:
Overall, the key to dealing with the problem of cell degradation in scRNA + scATAC single cell sequencing is to optimize sample preparation, perform quality control measures, and use appropriate normalization and analysis techniques.
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