Searching journal content for articles similar to Auerbach et al. 31 (10): 1728.

Displaying results 1-10 of 27
For checked items
  1. Method: Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach
    • Fabien Wehbe,
    • Levi Adams,
    • Jordan Babadoudou,
    • Samantha Yuen,
    • Yoon-Seong Kim,
    • and Yoshiaki Tanaka
    Genome Res. January 2025 35: 135-146; Published in Advance December 2, 2024, doi:10.1101/gr.278812.123
    ...demonstrate that pretrained models by scIDST are applicable to multiple independent data resources and are advantageous to infer cells related to certain disease risks and comorbidities. Taken together, scIDST offers a new strategy of single-cell sequencing analysis to identify bona fide disease...
    OPEN ACCESS ARTICLE
  2. Method: Harnessing agent-based frameworks in CellAgentChat to unravel cell–cell interactions from single-cell and spatial transcriptomics
    • Vishvak Raghavan,
    • Yumin Zheng,
    • Yue Li,
    • and Jun Ding
    Genome Res. July 2025 35: 1646-1663; Published in Advance May 2, 2025, doi:10.1101/gr.279771.124
    ..., focusing on cell population levels and losing single-cell resolution. Scriabin (Wilk et al. 2024) analyzes LR interactions at the single-cell level but primarily relies on unimodal scRNA-seq data, limiting CCI inference ability owing to lack of spatial information. Because CCIs in tissue environments...
    OPEN ACCESS ARTICLE
  3. Editorial: New frontiers in single-cell genomics
    • Nicholas E. Navin,
    • Orit Rozenblatt-Rosen,
    • and Nancy R. Zhang
    Genome Res. October 2021 31: ix-x; doi:10.1101/gr.276129.121
    ...MP , Li M . 2021 . Applications of single-cell genomics and computational strategies to study common disease and population-level variation . Genome Res (this issue) 31 : 1728 – 1741 . doi: 10.1101/gr.275430.121 ↵ Baker BM , Mokashi SS , Shankar V , Hatfield JS , Hannah RC , Mackay TFC , Anholt RRH...
    OPEN ACCESS ARTICLE
  4. Method: Alignment of single-cell RNA-seq samples without overcorrection using kernel density matching
    • Mengjie Chen,
    • Qi Zhan,
    • Zepeng Mu,
    • Lili Wang,
    • Zhaohui Zheng,
    • Jinlin Miao,
    • Ping Zhu,
    • and Yang I. Li
    Genome Res. April 2021 31: 698-712; Published in Advance March 19, 2021, doi:10.1101/gr.261115.120
    ...with modest differences in cell type compositions, nearly all existing methods also removed true biological variation between cells from the different biopsy sites.Alignment improves power of eQTL mapping from population-level single-cell RNA-seq dataPCA is often used to estimate and correct batch effects...
    OPEN ACCESS ARTICLE
  5. Method: bigSCale: an analytical framework for big-scale single-cell data
    • Giovanni Iacono,
    • Elisabetta Mereu,
    • Amy Guillaumet-Adkins,
    • Roser Corominas,
    • Ivon Cuscó,
    • Gustavo Rodríguez-Esteban,
    • Marta Gut,
    • Luis Alberto Pérez-Jurado,
    • Ivo Gut,
    • and Holger Heyn
    Genome Res. June 2018 28: 878-890; Published in Advance May 3, 2018, doi:10.1101/gr.230771.117
    .... Further, we foresee a potential application of the convolution strategy in other large data types, such as single-cell mass cytometry data, where iCells could improve resolution and decrease computation time (van Unen et al. 2016; Bendall et al. 2014). With decreasing expenses for library preparation...
    OPEN ACCESS ARTICLE
  6. Research: Comparative genomics of Cryptosporidium parvum reveals the emergence of an outbreak-associated population in Europe and its spread to the United States
    • Greta Bellinzona,
    • Tiago Nardi,
    • Michele Castelli,
    • Gherard Batisti Biffignandi,
    • Karim Adjou,
    • Martha Betson,
    • Yannick Blanchard,
    • Ioana Bujila,
    • Rachel Chalmers,
    • Rebecca Davidson,
    • Nicoletta D'Avino,
    • Tuulia Enbom,
    • Jacinto Gomes,
    • Gregory Karadjian,
    • Christian Klotz,
    • Emma Östlund,
    • Judith Plutzer,
    • Ruska Rimhanen-Finne,
    • Guy Robinson,
    • Anna Rosa Sannella,
    • Jacek Sroka,
    • Christen Rune Stensvold,
    • Karin Troell,
    • Paolo Vatta,
    • Barbora Zalewska,
    • Claudio Bandi,
    • Davide Sassera,
    • and Simone M. Cacciò
    Genome Res. June 2024 34: 877-887; Published in Advance July 8, 2024, doi:10.1101/gr.278830.123
    ...uncharacterized proteins, a protein with putative membrane domain, and a protein with AP2/ERF domain.Genomic variability at the population levelWe observed that out of the 28,047 SNPs identified in the entire data set of 140 s, only 1243 (4.4%) were shared by the three populations, whereas the majority...
    OPEN ACCESS ARTICLE
  7. Method: Characterizing polymorphic inversions in human genomes by single-cell sequencing
    • Ashley D. Sanders,
    • Mark Hills,
    • David Porubský,
    • Victor Guryev,
    • Ester Falconer,
    • and Peter M. Lansdorp
    Genome Res. November 2016 26: 1575-1587; Published in Advance July 29, 2016, doi:10.1101/gr.201160.115
    ...their distinct inversion profiles and built a nonredundant global reference of structural rearrangements in the human genome. The work described here provides a powerful new framework to study structural variation and genomic heterogeneity in single-cell samples, whether from individuals for population studies...
  8. Research: Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data
    • Aaron T.L. Lun,
    • Fernando J. Calero-Nieto,
    • Liora Haim-Vilmovsky,
    • Berthold Göttgens,
    • and John C. Marioni
    Genome Res. November 2017 27: 1795-1806; Published in Advance October 13, 2017, doi:10.1101/gr.222877.117
    ...of individual cells, single-cell RNA sequencing provides unparalleled resolution to study cellular heterogeneity. However, this comes at the cost of high technical noise, including cell-specific biases in capture efficiency and library generation. One strategy for removing these biases is to add a constant...
    OPEN ACCESS ARTICLE
  9. Research: Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons
    • Jiaxu Wang,
    • Piroon Jenjaroenpun,
    • Akshay Bhinge,
    • Vladimir Espinosa Angarica,
    • Antonio Del Sol,
    • Intawat Nookaew,
    • Vladimir A. Kuznetsov,
    • and Lawrence W. Stanton
    Genome Res. November 2017 27: 1783-1794; Published in Advance October 13, 2017, doi:10.1101/gr.223313.117
    ...to 10,000. Single-cell analysis of neurogenesis Genome Research 1785 www..org Supplemental Table S6). Similarly, there are 129, 135, 128, and 48 commonDEGbetween subpopulations of days 1 and 5, days 5 and 7, days 7 and 10, and days 10 and 30, respectively (Supplemental Table S6). A heatmapwas generated...
  10. Method: Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq
    • Saiful Islam,
    • Una Kjällquist,
    • Annalena Moliner,
    • Pawel Zajac,
    • Jian-Bing Fan,
    • Peter Lönnerberg,
    • and Sten Linnarsson
    Genome Res. July 2011 21: 1160-1167; Published in Advance May 4, 2011, doi:10.1101/gr.110882.110
    ...both at the single-cell level and the population level, without the need for known markers or even a prior knowledge that a certain type of cell exists. We propose that this general strategy can be extended to study all kinds of mixed samples, such as specific progenitors active during organogenesis...
For checked items

Search Results

Next 10 » New Search

Modify Results

Citation format:
Results / page:
Results order:

This search

  • Alert me when new articles matching this search are published
  • Download all citations on this page to my citation manager

Preprint Server



Current Issue

  1. April 2026, 36 (4)
  1. Current Issue
  1. Alert me to new issues of Genome Research