C
Christoph Hafemeister
Researcher at New York University
Publications - 30
Citations - 16050
Christoph Hafemeister is an academic researcher from New York University. The author has contributed to research in topics: Gene regulatory network & Gene. The author has an hindex of 16, co-authored 27 publications receiving 7388 citations. Previous affiliations of Christoph Hafemeister include National Institutes of Health & Max Planck Society.
Papers
More filters
Journal ArticleDOI
Comprehensive Integration of Single-Cell Data.
Tim Stuart,Andrew Butler,Paul J. Hoffman,Christoph Hafemeister,Efthymia Papalexi,William M. Mauck,Yuhan Hao,Marlon Stoeckius,Peter Smibert,Rahul Satija +9 more
TL;DR: A strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.
Posted ContentDOI
Comprehensive integration of single cell data
Tim Stuart,Andrew Butler,Paul J. Hoffman,Christoph Hafemeister,Efthymia Papalexi,William M. Mauck,Marlon Stoeckius,Peter Smibert,Rahul Satija +8 more
TL;DR: This work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets, and demonstrates how anchoring can harmonize in-situ gene expression and scRNA-seq datasets.
Journal ArticleDOI
Simultaneous epitope and transcriptome measurement in single cells.
Marlon Stoeckius,Christoph Hafemeister,William Stephenson,Brian Houck-Loomis,Pratip K. Chattopadhyay,Harold Swerdlow,Rahul Satija,Peter Smibert +7 more
TL;DR: In this article, a method called cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is proposed, in which oligonucleotide-labeled antibodies are used to integrate cellular protein and transcriptome measurements into an efficient, single-cell readout.
Journal ArticleDOI
Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
TL;DR: It is proposed that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity.
Posted ContentDOI
Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
TL;DR: It is proposed that the Pearson residuals from ’regularized negative binomial regression’, where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity.