World's Best Scientists 2026 revealed!

D-Index & Metrics

Genetics

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

Christina Kendziorski publication distribution in Genetics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Genetics in 2026. The highlighted bar marks where Christina Kendziorski sits on this spectrum.

45–54 publications: 6 scientists 55–64 publications: 10 scientists 65–74 publications: 35 scientists 75–84 publications: 84 scientists 85–94 publications: 102 scientists 95–104 publications: 151 scientists 105–114 publications: 175 scientists 115–124 publications: 203 scientists 125–134 publications: 217 scientists 135–144 publications: 205 scientists 145–154 publications: 193 scientists 155–164 publications: 188 scientists 165–174 publications: 170 scientists 175–184 publications: 178 scientists 185–194 publications: 164 scientists 195–204 publications: 173 scientists 205–214 publications: 159 scientists 215–224 publications: 134 scientists 225–234 publications: 143 scientists 235–244 publications: 105 scientists 245–254 publications: 114 scientists 255–264 publications: 92 scientists 265–274 publications: 88 scientists 275–284 publications: 87 scientists 285–294 publications: 80 scientists 295–304 publications: 62 scientists 305–314 publications: 75 scientists 315–324 publications: 67 scientists 325–334 publications: 60 scientists 335–344 publications: 52 scientists 345–354 publications: 40 scientists 355–364 publications: 48 scientists 365–374 publications: 47 scientists 375–384 publications: 46 scientists 385–394 publications: 31 scientists 395–404 publications: 27 scientists 405–414 publications: 40 scientists 415–424 publications: 30 scientists 425–434 publications: 43 scientists 435–444 publications: 29 scientists 445–454 publications: 14 scientists 455–464 publications: 28 scientists 465–474 publications: 21 scientists 475–484 publications: 21 scientists 485–494 publications: 22 scientists 495–504 publications: 17 scientists 505–514 publications: 12 scientists 515–524 publications: 11 scientists 525–534 publications: 8 scientists 535–544 publications: 8 scientists 545–554 publications: 14 scientists 555–564 publications: 4 scientists 565–574 publications: 11 scientists 575–584 publications: 5 scientists 585–594 publications: 11 scientists 595–604 publications: 12 scientists 605–614 publications: 7 scientists 615–624 publications: 6 scientists 625–634 publications: 10 scientists 635–644 publications: 9 scientists 645–654 publications: 10 scientists 655–664 publications: 6 scientists 665–674 publications: 6 scientists 675–684 publications: 6 scientists 685–694 publications: 4 scientists 695–702 publications: 6 scientists 703+ publications: 100 scientists
45 publications 703+

This scientist: 134 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 703 publications or more.

Christina Kendziorski D-index placement in Genetics in 2026

The chart shows the D-index (discipline H-index) distribution of Genetics scientists ranked by Research.com in 2026. The highlighted bar marks where Christina Kendziorski sits on this spectrum.

40–41 D-Index: 24 scientists 42–43 D-Index: 52 scientists 44–45 D-Index: 84 scientists 46–47 D-Index: 112 scientists 48–49 D-Index: 118 scientists 50–51 D-Index: 141 scientists 52–53 D-Index: 143 scientists 54–55 D-Index: 145 scientists 56–57 D-Index: 179 scientists 58–59 D-Index: 162 scientists 60–61 D-Index: 175 scientists 62–63 D-Index: 191 scientists 64–65 D-Index: 172 scientists 66–67 D-Index: 184 scientists 68–69 D-Index: 164 scientists 70–71 D-Index: 158 scientists 72–73 D-Index: 150 scientists 74–75 D-Index: 136 scientists 76–77 D-Index: 127 scientists 78–79 D-Index: 127 scientists 80–81 D-Index: 111 scientists 82–83 D-Index: 110 scientists 84–85 D-Index: 110 scientists 86–87 D-Index: 84 scientists 88–89 D-Index: 102 scientists 90–91 D-Index: 66 scientists 92–93 D-Index: 72 scientists 94–95 D-Index: 70 scientists 96–97 D-Index: 54 scientists 98–99 D-Index: 60 scientists 100–101 D-Index: 49 scientists 102–103 D-Index: 55 scientists 104–105 D-Index: 45 scientists 106–107 D-Index: 42 scientists 108–109 D-Index: 28 scientists 110–111 D-Index: 39 scientists 112–113 D-Index: 25 scientists 114–115 D-Index: 31 scientists 116–117 D-Index: 29 scientists 118–119 D-Index: 34 scientists 120–121 D-Index: 29 scientists 122–123 D-Index: 29 scientists 124–125 D-Index: 18 scientists 126–127 D-Index: 27 scientists 128–129 D-Index: 22 scientists 130–131 D-Index: 16 scientists 132–133 D-Index: 11 scientists 134–135 D-Index: 17 scientists 136–137 D-Index: 12 scientists 138–139 D-Index: 21 scientists 140–141 D-Index: 4 scientists 142–143 D-Index: 9 scientists 144–145 D-Index: 14 scientists 146–147 D-Index: 6 scientists 148–149 D-Index: 10 scientists 150–151 D-Index: 7 scientists 152–153 D-Index: 9 scientists 154–155 D-Index: 8 scientists 156–157 D-Index: 8 scientists 158–159 D-Index: 9 scientists 160+ D-Index: 96 scientists
40 D-Index 160+

This scientist: 50 D-Index — 11th percentile

11% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 160 D-Index or more.

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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