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Fig. 5 | BMC Cancer

Fig. 5

From: Precision oncology using a limited number of cells: optimization of whole genome amplification products for sequencing applications

Fig. 5

NGS application using 1, 5 and 10-cell WGA products and batch gDNA. a Lorenz curve illustrating the amplification bias in read coverage. Lorenz curve provides information on the uniformity of the sequencing reads distribution. Perfect coverage results in a straight line with slope of 1 (y = x) as shown by the dotted line. The wider the curve below the line of y = x, the lower the coverage uniformity and greater the amplification bias. Single-cell WGA product passing the QC-step (QC-pass) is observed to have less amplification bias compared to the single-cell WGA product that failed the QC-step (QC-fail). As the number of starting cellular input increases, the degree of amplification bias becomes even less pronounced, approaching that of unamplified batch gDNA by 10-cells. b Histogram of read count distribution. Wider range of distribution without a distinct peak (as seen for single-cell WGA: QC-fail) signifies worse coverage dispersion. c Genomic profiles generated from NGS sequencing data. Significantly less “noise” is observed with single-cell QC-pass when compared to single-cell QC-fail. Increasing the number of starting cellular input further improves the data quality, as reflected in the decreasing index of dispersion

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