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D-Index & Metrics

Genetics

D-Index
50
Citations
13003
World Ranking
3907
National Ranking
1685

Research.com Recognitions

  • 2018 - Fellow of the American Statistical Association (ASA)

Overview

Christina Kendziorski is affiliated with the University of Wisconsin-Madison in the United States. Their research spans several areas within biochemistry, genetics, molecular biology, and medicine, with a particular focus on molecular biology and related subfields. Kendziorski's work is prominently situated in the study of single-cell and spatial transcriptomics, gene expression and cancer classification, and cancer genomics and diagnostics.

The scientist has contributed extensively to topics including:

  • Single-cell and spatial transcriptomics
  • Gene expression and cancer classification
  • Cancer genomics and diagnostics
  • Brain metastases and treatment
  • Immune cells in cancer
  • Glioma diagnosis and treatment
  • Lung cancer research studies

Kendziorski has authored multiple papers across reputable publication venues. Frequent publication venues include:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Cancer Research
  • Journal of Investigative Dermatology
  • SSRN Electronic Journal
  • Nature Communications

Among recent papers are the following notable works:

  • SpotClean adjusts for spot swapping in spatial transcriptomics data, 2022, Nature Communications
  • Interspecies chimeric conditions affect the developmental rate of human pluripotent stem cells, 2021, PLoS Computational Biology
  • Identification of direct transcriptional targets of NFATC2 that promote β cell proliferation, 2021, Journal of Clinical Investigation
  • CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data, 2020, Genome Biology
  • Normalization by distributional resampling of high throughput single-cell RNA-sequencing data, 2021, Bioinformatics

Kendziorski collaborates frequently with several researchers, including:

  • Chitrasen Mohanty
  • Zijian Ni
  • Jared Brown
  • Gopal Iyer
  • Jack Shireman

The scientist's research fields emphasize molecular biology and immunology with extensions into pulmonary and respiratory medicine, oncology, and cancer research.

Recognition of their contributions includes the designation as a Fellow of the American Statistical Association in 2018.

Best Publications

  • EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments.

    Ning Leng;John A. Dawson;James A. Thomson;Victor Ruotti

  • Loss of stearoyl–CoA desaturase-1 function protects mice against adiposity

    James M. Ntambi;Makoto Miyazaki;Jonathan P. Stoehr;Hong Lan

  • The Collaborative Cross, a community resource for the genetic analysis of complex traits

    Gary A. Churchill;David C. Airey;Hooman Allayee;Joe M. Angel

  • On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data.

    M. A. Newton;C. M. Kendziorski;C. S. Richmond;Frederick R. Blattner

  • On the utility of pooling biological samples in microarray experiments.

    C. Kendziorski;R. A. Irizarry;K.-S. Chen;J. D. Haag

  • Design and computational analysis of single-cell RNA-sequencing experiments

    Rhonda Bacher;Christina Kendziorski

  • On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles

    C. M. Kendziorski;M. A. Newton;H. Lan;M. N. Gould

  • Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm

    Li-Fang Chu;Ning Leng;Ning Leng;Jue Zhang;Zhonggang Hou;Zhonggang Hou

  • A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility

    Mark P Keller;YounJeong Choi;Ping Wang;Dawn Belt Davis

  • The efficiency of pooling mRNA in microarray experiments.

    C. M. Kendziorski;Y. Zhang;H. Lan;A. D. Attie

  • SCnorm: robust normalization of single-cell RNA-seq data

    Rhonda Bacher;Li-Fang Chu;Ning Leng;Audrey P Gasch

  • A PtdIns4,5P2-regulated nuclear poly(A) polymerase controls expression of select mRNAs.

    David L. Mellman;Michael L. Gonzales;Chunhua Song;Christy A. Barlow

  • A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.

    Keegan D. Korthauer;Li-Fang Chu;Michael A. Newton;Yuan Li

  • Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling

    Christine T. Ferrara;Christine T. Ferrara;Ping Wang;Elias Chaibub Neto;Robert D. Stevens

  • The IRP1-HIF-2α Axis Coordinates Iron and Oxygen Sensing with Erythropoiesis and Iron Absorption

    Sheila A. Anderson;Christopher P. Nizzi;Yuan I. Chang;Kathryn M. Deck

  • Statistical methods for expression quantitative trait loci (eQTL) mapping.

    C. M. Kendziorski;M. Chen;M. Yuan;H. Lan

  • Gene expression profiling of aging reveals activation of a p53-mediated transcriptional program

    Michael G Edwards;Rozalyn M Anderson;Ming Yuan;Christina M Kendziorski

  • Liver and Adipose Expression Associated SNPs Are Enriched for Association to Type 2 Diabetes

    Hua Zhong;John Beaulaurier;Pek Yee Lum;Cliona Molony

  • Combined expression trait correlations and expression quantitative trait locus mapping.

    Hong Lan;Meng Chen;Jessica B Flowers;Brian S Yandell

  • Statistical methods for gene set co-expression analysis

    YounJeong Choi;Christina Kendziorski

Frequent Co-Authors

James A. Thomson
James A. Thomson University of California, Santa Barbara
Alan D. Attie
Alan D. Attie University of Wisconsin–Madison
Ron Stewart
Ron Stewart Morgridge Institute for Research
Brian S. Yandell
Brian S. Yandell University of Wisconsin–Madison
Karl W. Broman
Karl W. Broman University of Wisconsin–Madison
Eric E. Schadt
Eric E. Schadt Icahn School of Medicine at Mount Sinai
Michael N. Gould
Michael N. Gould University of Wisconsin–Madison
James M. Ntambi
James M. Ntambi University of Wisconsin–Madison
Audrey P. Gasch
Audrey P. Gasch University of Wisconsin–Madison
Ming Yuan
Ming Yuan Columbia University

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