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Lei Qu

Researcher at Anhui University

Publications -  18
Citations -  1946

Lei Qu is an academic researcher from Anhui University. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 9, co-authored 13 publications receiving 1406 citations. Previous affiliations of Lei Qu include Southeast University & Howard Hughes Medical Institute.

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A multimodal cell census and atlas of the mammalian primary motor cortex

Ricky S. Adkins, +247 more
- 07 Oct 2021 - 
TL;DR: This study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps, and establishes a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
Journal ArticleDOI

BrainAligner: 3D registration atlases of Drosophila brains

TL;DR: Analyzing Drosophila melanogaster neural expression patterns in thousands of three-dimensional image stacks of individual brains requires registering them into a canonical framework based on a fiducial reference of neuropil morphology, which permits assessment of stereotypy, potential connectivity and functional mapping of the adult fruit fly brain.
Journal ArticleDOI

Morphological diversity of single neurons in molecularly defined cell types

TL;DR: In this paper, the authors systematically examined complete single-neuron morphologies on a brain-wide scale, established a pipeline encompassing sparse labeling, whole-brain imaging, reconstruction, registration and analysis, and identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities.
Journal ArticleDOI

BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies

TL;DR: The BlastNeuron approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.