Debiased Projected Two-Sample Comparisonscfor Single-Cell Expression Data
arxiv(2024)
摘要
We study several variants of the high-dimensional mean inference problem
motivated by modern single-cell genomics data. By taking advantage of
low-dimensional and localized signal structures commonly seen in such data, our
proposed methods not only have the usual frequentist validity but also provide
useful information on the potential locations of the signal if the null
hypothesis is rejected. Our method adaptively projects the high-dimensional
vector onto a low-dimensional space, followed by a debiasing step using the
semiparametric double-machine learning framework. Our analysis shows that
debiasing is unnecessary under the global null, but necessary under a
“projected null” that is of scientific interest. We also propose an
“anchored projection” to maximize the power while avoiding the degeneracy
issue under the null. Experiments on synthetic data and a real single-cell
sequencing dataset demonstrate the effectiveness and interpretability of our
methods.
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