Hello,

I am working with the Biomark HD Fluidigm high throughput qPCR transcriptomics datasets (consists of 240 target genes and 8 housekeeping genes). The dataset consists of 250 samples processed in 5 different batches (50 samples/batch). Dataset of each batch was normalized to calculate `Delta Ct` (Difference between the Target gene and Geometric mean of 8 housekeeping gene (i.e GM_HKgs)). Then, transformed to `- Delta Ct` (negative delta Ct) because we want to apply downstream analyses like ANOVA to log-scale normalized relative expression values, as just using `-1 * Delta Ct` as readout is quite valid too. I understand from various posts `removebatcheffects` (`limma` package) would be best to use. My question is what data type should be used as input, Delta Ct or Negative Delta Ct (-1*Delta Ct).
You could use negative delta ct, which would be fine, or see https://support.bioconductor.org/p/67032/ for a more sophisticated normalization method that we use for qPCR.

You don't necessarily need `removeBatchEffect` however. The limma DE/anova analysis of qPCR ct values does not use `removeBatchEffects`.