Single-cell RNA-seq (scRNA-seq) is emerging as a promising technology to characterize and dissect the cell-to-cell variability. However, the mixture of technical noise and intrinsic biological variability makes separating technical artifacts from real biological variation cells particularly challenging. Proper detection and filtering out technical artifacts before downstream analysis are critical. Here, we present a protocol that integrates both gene expression patterns and data quality to detect technical artifacts in scRNA-seq samples.