L1000 dataset contains numerous cellular expression data induced by large sets of perturbagens. The existing peak deconvolution algorithms cannot recover the accurate expression level of genes in many cases, inducing severe noise in the signature detection and limiting its applications in biomedical studies. Here, we present a novel Bayesian analysis-based peak deconvolution algorithm that gives unbiased likelihood estimations for peak locations and characterize the peaks with probability-based z-scores.