Cyclical Regression Covariates remove the major confounding effect of cyclical developmental gene expression with strain-specific drug response in the malaria parasite Plasmodium falciparum

BackgroundThe cyclical nature of parasite gene expression in the intraerythrocytic development cycle (IDC) in human blood confounds the accurate detection of specific transcriptional differences due to drug resistance in Plasmodium falciparum. Here, we propose the use of cyclical regression covariates to eliminate the major confounding of developmentally driven transcriptional changes with changes due to drug response. We show that elimination of this confounding can reduce both Type I and Type II errors, and demonstrate the effect of approach on real data. ResultsWe apply this method to two publicly available datasets, and demonstrate its ability to reduce the potential confounding of differences in expression due the species-specific intraerythrocytic development cycle from strain-specific differences in drug response. We show that the application of cyclical regression covariates has minimal impact on the pool of transcripts identified as significantly different in a dataset generated from single timepoint clinical blood samples with low variance for developmental stage and a profound impact on another clinical data set with more variance among the samples for developmental stage. ConclusionsCyclical regression covariates have immediate application to studies where in-vitro synchronization of all samples to the same developmental timepoint is not feasible, primarily parasite transcriptome sequencing direct from clinical blood samples, a widely used approach to frontline detection of emerging drug resistance.