  Intercellular heterogeneity serves as both a confounding factor in studying
individual clones and an information source in characterizing any heterogeneous
tissues, such as blood, tumor systems. Due to inevitable sequencing errors and
other sample preparation artifacts such as PCR errors, systematic efforts to
characterize intercellular genomic heterogeneity must effectively distinguish
genuine clonal sequences from fake derivatives. We developed a novel approach
(SIGH) for identifying significant genuine clonal sequences directly from mixed
sequencing reads that can improve genomic analyses in many biological contexts.
This method offers several attractive features: (1) it automatically estimates
the error rate from raw sequence reads and identifies genuine clonal sequences;
(2) it is robust to the large variety of error rate due to the various
experimental conditions; (3) it is supported by a well grounded statistical
framework that exploits probabilistic characteristics of sequencing errors; (4)
its unbiased strategy allows detecting rare clone(s) despite that clone
relative abundance; and (5) it estimates constituent proportions in each
sample. Extensive realistic simulation studies show that our method can
reliably estimate the error rates and faithfully distinguish the genuine clones
from fake derivatives, paving the way for follow up analysis that is otherwise
ruined by the often dominant fake clones.
