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John Ngai

Researcher at University of California, Berkeley

Publications -  78
Citations -  19929

John Ngai is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Olfactory system & Olfactory bulb. The author has an hindex of 41, co-authored 78 publications receiving 16586 citations. Previous affiliations of John Ngai include Helen Wills Neuroscience Institute & National Institutes of Health.

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Comprehensive genomic characterization defines human glioblastoma genes and core pathways

Roger E. McLendon, +233 more
- 23 Oct 2008 - 
TL;DR: The interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated gliobeasts, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.
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Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation

TL;DR: This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments.
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Normalization of RNA-seq data using factor analysis of control genes or samples

TL;DR: This work proposes a normalization strategy, called remove unwanted variation (RUV), that adjusts for nuisance technical effects by performing factor analysis on suitable sets of control genes or samples and leads to more accurate estimates of expression fold-changes and tests of differential expression compared to state-of-the-art normalization methods.
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Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

TL;DR: Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories and infers more accurate pseudotimes than other leading methods.