Madhavi Bhatia, National Pharmaceutical Education and Research, Guwahati
What is Single-cell RNA-seq?
Single-cell RNA-seq is a method that measures hundreds of gene expressions in thousands of cells that are sampled from a hundred individuals. Single-cell sequencing methods are helpful in solving a variety of biological and medical problems that are present in large-scale data sets and available in research communities. Single-cell RNA-seq analysis also measures the quantity of the gene expression changes from a large number of cells. In case-control studies, knowing differential expressed genes (DEGs) is often used in research and clinical cases. We can use a statistical method for differential expression analysis between different groups of individuals but not the cells.
The method in a nutshell:
A casual gene is a gene that is mainly affected or is being affected by a disease status independent of any confounding variables present in the sample. Many differential expressed genes can lead to disease for most of the late-onset disorders, but aberrant changes on the handful of genes might initiate some disease phenotypes. Confounder adjustment for differential single-cell gene expression analysis (CoCoA-diff) is used to represent the missing parts of the potential outcomes of single-cell profiles then propagates the imputed results to the pseudo-block estimation, and then it decomposes the total pseudo-bulk profiles into 2 parts- confounding and differential effects. Then using a single-cell gene expression matrix, we can estimate 2 types of pseudo-bulk data –the estimated confounders and the residual differential effects. The method is built on the outcome regression which is done by using a matching algorithm. The matching algorithm proceeds as
- Counterfactual measurement of single cell expression is done by matching cells in particular conditions with cells that are opposing conditions and the distance between the cells is measured.
- After pairing of the noticed and counterfactual single-cell data, the average expression of genes shared across 2 opposite conditions is estimated.
Using the CoCoA diff method the effectiveness in the case of the APOE gene is measured in microglia samples. It was observed that there is a significant correlation of APOE gene expression of AD status in patients. Thus it is important to understand and also quantify to what degree the cell-cell matching process can address the intrinsic and the other technical variability present in a single-cell RNA-seq data matrix. In this method, we consider only the average effect within each individual and the model only captures the estimands. We also ignored the concept of zero-inflation since this method treats single-cell data as a counter matrix and not being transformed by logarithm.
Conclusion:
The CoCoA diff model is based on a casual interference method that is used to identify and remove the putative confounding effects from the single-cell RNA –seq data. This results in differential gene expression analysis which is unbiased and also gains more statistical power. The CoCoA diff improves the downstream analysis in extensive simulation experiments. CoCoA diff helped in knowing both well-established and novel causal genes in AD. In the future, many existing interference methods and models can be reformulated in this similar framework.
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Reference: Park, Y. P., & Kellis, M. (2021). CoCoA-diff: Counterfactual inference for single-cell gene expression analysis. Genome Biology, 22(1), 228. https://doi.org/10.1186/s13059-021-02438-4
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About the author: Madhavi Bhatia is currently pursuing a Master of Science in Pharmaceutical Biotechnology from NIPER, Guwahati. Her area of interest lies in understanding the role of gene mutation in the development of various diseases and developing a treatment for such diseases.
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