Whole genome amplification (WGA) has laid the foundation for investigating complex genomic alteration with single-cell or even single-molecule resolution. Coupled with sequencing-based copy number variation (CNV) analysis, it promotes understanding of the nature of commonly existing genetic heterogeneity by constructing the sequencing profiles for every single cell. However, prevailing methods only provide insights into limited aspects due to their intrinsic technical challenges. Their output data, as a result, fails to render comprehensive information (which is) concerned. Here, we describe the CNV detection analysis based on micro-channel multiple displacement amplification (μcMDA), a protocol able to provide optimized amplification uniformity while inheriting the advantages of MDA chemistry. We demonstrate the analysis of both the normal diploid YH-1 cell line and the aneuploid K562 cancer cell line. In the detection of simulated CNVs ranging from 300 kb to 2 Mb, μcMDA can respectively increase the detection rates of copy number loss and gain by 28.8% and 40.2% on average, using only 0.2× sequencing data. When detecting the inherent CNVs in tumor cells, the resolution of CNV recognition can be improved to 250 kb. Starting from either superabundant template copies or minute single-cell-level input, this easily accessible approach is capable of providing quantitatively reliable coverage as well as more robust GC-content regression for CNV detection.