scHiCcompare - differential analysis of single-cell Hi-C data
Title: scHiCcompare - differential analysis of single-cell Hi-C data
Presenter: My Nguyen
Advisor: Mikhail Dozmorov
Abstract:
Changes in the three-dimensional (3D) structure of the genome are an
established hallmark of cancer and developmental disorders. To comprehend
these global 3D structures, techniques such as chromatin conformation
capture (Hi-C) have been devised. A typical Hi-C experiment requires millions
of cells and ultra-high sequencing depth, on the order of 1 billion reads per
sample (bulk Hi-C). In contrast, single-cell Hi-C technologies allow for
capturing 3D structures in individual cells, albeit with the trade-off of
encountering high data sparsity (characterized as high proportion of zeros).
Despite numerous methods for differential 3D analysis in bulk Hi-C data,
analyzing single-cell Hi-C data differentials remains underdeveloped. We
propose to adapt our HiCcompare method for scHi-C data normalization and
differential analysis. Briefly, clusters of scHi-C data will be imputed
univariately by random forest, converted to pseudo-bulk Hi-C datasets, and
analyzed as we described previously. The single cells datasets of 14 different
cell types, covering each chromosome in the human prefrontal cortex, will be
used to apply our method and detect chromosome regions differences
between pairs of cell types. We will present the results scHiCcompare and
benchmark methods (Negative Binomial model, T.test, Kolmogorov–Smirnov
test, Wilcoxon signed-rank test, SnapHiC-D, and scHiCDiff) in terms of
controlling false positives under the null hypothesis and detecting simulated
differential chromatin contacts. Notably, scHiCcompare outperforms those
methods across various measurement metrics, including the Matthews
Correlation Coefficient (MCC), F1 score, sensitivity, and specificity, with a
smaller single-cell data sample, enhancing detection power.