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Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational… Click to show full abstract
Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets.
Keywords:
single cell;
driven comparison;
data driven;
cell expression
Journal Title: Journal of human genetics
Year Published: 2021
Link to full text (if available)
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Journal Title: Journal of human genetics
Year Published: 2021
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!