Variation in expression data is driven by wanted and unwanted effects, and both need exploring. Thus, another important step in quality control is to identify variables that have explanatory power over both experimental metadata variables and computed QC metrics.
But we don't get to unwanted effects until later, why mention it here? Any why do we want to identify variables that can explain the metadata or QC metrics? Perhaps a better starting sentence would be
Another important step in quality control is to identify variables (experimental factors or computed QC metrics) that drives variation in expression data across cells.
The variables are ranked by median R2 across features in the plot, providing useful information for identifying variables...
Stating the usefulness of the information is a bit redundant. We obviously wouldn't give people useless information, so perhaps something better might be:
The variables are ranked by median R2 across features in the plot, allowing users to identify variables...
But we don't get to unwanted effects until later, why mention it here? Any why do we want to identify variables that can explain the metadata or QC metrics? Perhaps a better starting sentence would be
Stating the usefulness of the information is a bit redundant. We obviously wouldn't give people useless information, so perhaps something better might be: