First of all, thanks for creating this great package.
I was wondering how the AveExpr in the output of mmDS is calculated?
And how can I calculate back the original aggregated expression from AveExpr?
I am concerned about missing some filtering steps performed by mmDS, that's why I want to calculate back the original mean expression based on AveExpr.
Here some more details and code:
For my cluster_id1, the AveExpr of the DEGs in the mmDS output ranges from 18.00536 to 24.08153
mm_dream <- mmDS(sce, method = "dream", coef = "group_idEndo",
n_cells = 50, n_samples = 3, covs = "batch")
The corresponding gene 'mean' ranges from about 0.008 to 59.3, calculated as following:
sce_mean <- aggregateData(sce, assay=NULL, by=c("cluster_id"), fun=c("mean"), scale=FALSE, verbose=TRUE)
sce_mean <- sce_mean@assays@data@listData[[1]]
Dear MUSCAT team,
First of all, thanks for creating this great package.
I was wondering how the AveExpr in the output of mmDS is calculated? And how can I calculate back the original aggregated expression from AveExpr? I am concerned about missing some filtering steps performed by mmDS, that's why I want to calculate back the original mean expression based on AveExpr.
Here some more details and code: For my cluster_id1, the AveExpr of the DEGs in the mmDS output ranges from 18.00536 to 24.08153 mm_dream <- mmDS(sce, method = "dream", coef = "group_idEndo", n_cells = 50, n_samples = 3, covs = "batch") The corresponding gene 'mean' ranges from about 0.008 to 59.3, calculated as following: sce_mean <- aggregateData(sce, assay=NULL, by=c("cluster_id"), fun=c("mean"), scale=FALSE, verbose=TRUE) sce_mean <- sce_mean@assays@data@listData[[1]]
Thanks a lot in advance for your answer!