Hi, I have single cells data from two different experiments and I would like to remove the batch effects from the data in order to cluster correctly the cells. I would like to try to see what are the differences removing the batch effects due to housekeeping genes and also to try to remove the batch effects that are not specifically due to the housekeeping genes.
I think that you can do it for the bulk RNA seq using RUV but I would like to know if I can apply RUV using single cell data or if I have to use another method.
I would say that RUV is not the appropriate tool here. RUV(seq) is designed for detecting unwanted factors of variation. But in this case, you know the factor of variation - the batch/experiment in which each cell was processed. There's not much point running RUVseq to recover something that you already know.
Moreover, if you treat cells from the same batch as "replicates" and run RUV on the expression data within each batch, it will identify the population structure as unwanted factors and remove it. This will remove differences associated with cell types and states, which are presumably what you're actually interested in.
If your two experiments are replicates of each other (i.e., generated from the same tissue/cell line/condition), you can just run removeBatchEffect (from limma) to eliminate the batch effect prior to clustering. If your experiments are from entirely different conditions, the technical and biological effects are confounded with each other, and there isn't much you can do to remove the former without also discarding the latter.
I didn't understand your point about house-keeping genes, but it doesn't seem particularly relevant.
Sorry, I have bulk RNA-seq and single cell RNA-seq (iCELL8) on the same organism, in 9 time points. so how I can remove batch effect between to datasets because PCA analysis shows two strata of groups each correspond to bulk or single cell seq
Sorry, I have bulk RNA-seq and single cell RNA-seq (iCELL8) on the same organism, in 9 time points. so how I can remove batch effect between to datasets because PCA analysis shows two strata of groups each correspond to bulk or single cell seq
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