We are were combining two sets of data, and we wanted to see if we have any batch effect when we estimated cell composition using the function EstimateCellCounts2() . We decided to set meanPlot=T (as recomended), but we have problems with interpretation if actually we have any problem there. Any hint? Thank you.
We are aware of this but we find it confusing "The meanPlot should be used to check for large batch effects in the data, reducing the confidence placed in the composition estimates. This plot depicts the average DNA methylation across the cell-type discrimating probes in both the provided and sorted data. The means from the provided heterogeneous samples should be within the range of the sorted samples. If the sample means fall outside the range of the sorted means, the cell type estimates will inflated to the closest cell type. Note that we quantile normalize the sorted data with the provided data to reduce these batch effects."
We are were combining two sets of data, and we wanted to see if we have any batch effect when we estimated cell composition using the function EstimateCellCounts2() . We decided to set meanPlot=T (as recomended), but we have problems with interpretation if actually we have any problem there. Any hint? Thank you.
We are aware of this but we find it confusing "The meanPlot should be used to check for large batch effects in the data, reducing the confidence placed in the composition estimates. This plot depicts the average DNA methylation across the cell-type discrimating probes in both the provided and sorted data. The means from the provided heterogeneous samples should be within the range of the sorted samples. If the sample means fall outside the range of the sorted means, the cell type estimates will inflated to the closest cell type. Note that we quantile normalize the sorted data with the provided data to reduce these batch effects."