A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei.

Jude M Phillip, Kyu-Sang Han, Wei-Chiang Chen, Denis Wirtz, Pei-Hsun Wu

February 2021 Nat Protoc

Synopsis of Social media discussions

Many discussions revolve around the advanced features of the VAMPIRE algorithm, with some users highlighting its application for spatial transcriptomics, showcasing excitement with terms like 'supercool' and 'must read'. Others simply reiterate the publication's title, indicating varied levels of engagement, yet maintaining a positive tone that emphasizes the algorithm’s transformative potential in cell analysis.

A
Agreement
 Strong agreement

The majority of discussions reflect strong support and enthusiasm for the findings from the publication, indicating a high level of agreement with its implications.

I
Interest
 High level of interest

Participants in the discussions express significant curiosity and relevance, often highlighting the importance of the VAMPIRE algorithm for ongoing research.

E
Engagement
 Moderate level of engagement

While many posts reiterate the title and key concepts, a few engage more deeply by discussing integrations and applications of the method, suggesting moderate to high engagement.

I
Impact
 High level of impact

The consensus is that the work has substantial potential influence on how researchers analyze cellular morphology, indicating a belief in its high impact.

Social Mentions

YouTube

2 Videos

Twitter

65 Posts

Metrics

Video Views

191

Total Likes

253

Extended Reach

247,957

Social Features

67

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

VAMPIRE Analysis: Quantifying Cell Morphological Diversity

VAMPIRE Analysis: Quantifying Cell Morphological Diversity

This video introduces the VAMPIRE analysis pipeline, a robust unsupervised machine learning method that quantifies the morphological diversity of cells and nuclei using fluorescence or brightfield images. It enables rapid analysis of large datasets, identifying distinct shape modes to visualize cellular morphology.

The Nanokid

January 22, 2021

131 views

Leonardo Collado Torres

July 23, 2021

60 views

  • LIBD rstats club
    @LIBDrstats (Twitter)

    RT @MadhaviTippani: Below is my presentation on how VAMPIRE https://t.co/1gVNUrJnx7 works for extracting data about cell morphology to add…
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    August 25, 2021

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  • @lcolladotor (Twitter)

    RT @MadhaviTippani: Below is my presentation on how VAMPIRE https://t.co/1gVNUrJnx7 works for extracting data about cell morphology to add…
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    August 25, 2021

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  • MadhaviTippani
    @MadhaviTippani (Twitter)

    Below is my presentation on how VAMPIRE https://t.co/1gVNUrJnx7 works for extracting data about cell morphology to add additional information for spatial transcriptomics analysis when integrated with #VistoSeg https://t.co/tfA2YzHbFu
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    August 6, 2021

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  • George
    @GeorgeMatheson_ (Twitter)

    RT @vnzloy: "A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei. Phillip JM, Han…
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    March 12, 2021

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  • Volodymyr Nechyporuk-Zloy
    @vnzloy (Twitter)

    "A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei. Phillip JM, Han KS, Chen WC, Wirtz D, Wu PH. Nat Protoc. 2021 Feb;16(2):754-774. doi: https://t.co/5bRObn42NN https://t.co/YJJ5YGezoi
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  • Bree
    @BreeKB_ (Twitter)

    RT @JudeM_Phillip: I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning…
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    January 20, 2021

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  • Giuseppe Ciccone
    @GiusCiccone (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • BreakthroughMedicine&AdvancedResearchTools
    @JayChance12 (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • Jennifer Yokoyama
    @YokoyamaLabUCSF (Twitter)

    RT @karchlab: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei | Nature Protoc…
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    January 12, 2021

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  • GoogleAndi
    @developercs (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • Saubashya Sur, PhD
    @SaubashyaSur (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 12, 2021

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    @Datascience__ (Twitter)

    A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei https://t.co/Q2JO1Fqnte #MachineLearning
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    January 12, 2021

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  • Alexis Lomakin, Ph.D. (אל) אַלעקסיס לאָמאַקין
    @Alexis_Lomakin (Twitter)

    RT @JudeM_Phillip: I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning…
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    January 12, 2021

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  • T Tian
    @gdsttian (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 12, 2021

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  • Cristian Cardona
    @adircinho (Twitter)

    RT @machinelearnflx: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei https://…
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    January 11, 2021

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  • Kaustav Bera
    @KaustavBera11 (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 11, 2021

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  • David Rosenthal MD
    @davidrosenthal (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 11, 2021

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  • Winston Timp
    @timp0 (Twitter)

    RT @JudeM_Phillip: I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning…
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    January 11, 2021

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  • Janitza Montalvo-Ortiz
    @JanitzaMontalvo (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 11, 2021

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  • Michal Tal, PhD
    @ImmunoFever (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 11, 2021

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  • Celeste Karch
    @karchlab (Twitter)

    A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei | Nature Protocols https://t.co/ruaO1ImcBi
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    January 11, 2021

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  • Sergio Sánchez S
    @sergio_sstban (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • Prit Benny Malgulwar
    @PritBenny (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • @MelStuart9 (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 11, 2021

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  • Raleigh M. Linville
    @LinvilleRaleigh (Twitter)

    RT @JudeM_Phillip: I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning…
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    January 11, 2021

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  • Quinton Smith
    @telomerase2 (Twitter)

    RT @JudeM_Phillip: I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning…
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    January 11, 2021

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  • Warren Grayson
    @GraysonLab (Twitter)

    RT @JudeM_Phillip: I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning…
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    January 11, 2021

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  • Mark Allenby
    @MCAllenby (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 11, 2021

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  • Raúl Jiménez Castaño
    @ruljc (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 11, 2021

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  • Liudmila Sosulina
    @LSNN13 (Twitter)

    RT @CarmeloFerrai: Supercool must read! VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity…
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    January 11, 2021

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  • amelie bonnet
    @amelie_bonnet (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • raghav1vij
    @raghav1vij (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 11, 2021

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  • Glenn Bantug
    @gbantug2000 (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 11, 2021

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  • Nexample
    @Nexample_Twits (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 11, 2021

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  • Jorge Marchand
    @SyntBio (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 11, 2021

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  • Vicky R
    @vic_rev (Twitter)

    RT @JudeM_Phillip: I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning…
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    January 11, 2021

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  • Mostafa Aakhte
    @aakhte (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    January 11, 2021

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  • Natalie Stanley
    @natstann (Twitter)

    RT @JudeM_Phillip: I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning…
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    January 11, 2021

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  • David Van Valen
    @davidvanvalen (Twitter)

    RT @JudeM_Phillip: I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning…
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    January 11, 2021

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    RT @JudeM_Phillip: I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning…
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    RT @JudeM_Phillip: I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning…
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  • Pat Malkòm
    @JediMasterWho (Twitter)

    RT @JudeM_Phillip: I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning…
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    January 11, 2021

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  • Jude Phillip
    @JudeM_Phillip (Twitter)

    I'm excited to share our latest work published in @NatureProtocols on developing a robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei. Please do check it out
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    January 11, 2021

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  • Jon Humphries
    @JDHL18 (Twitter)

    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • Jude Phillip
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    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • Alexey Gavrikov
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    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • Shao-Tuan Chen
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    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • Vittorio Stumpo
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    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • Paul Macklin
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    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • Alexis Lomakin, Ph.D. (אל) אַלעקסיס לאָמאַקין
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    RT @deniswirtz: VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei ht…
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  • Carmelo Ferrai
    @CarmeloFerrai (Twitter)

    Supercool must read! VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei https://t.co/6fS19rZLft
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    January 11, 2021

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  • The Johns Hopkins Institute for NanoBioTechnology
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  • Rita Strack
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  • Denis Wirtz
    @deniswirtz (Twitter)

    VAMPIRE: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei https://t.co/G8lnkquwL2 https://t.co/kliUKnGPCj
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Abstract Synopsis

  • The study introduces a new unsupervised machine learning method called the VAMPIRE algorithm for quantifying the morphological diversity of cells and their nuclei using fluorescence or brightfield images.
  • This algorithm classifies cells by identifying distinct shape modes based on their outer contours, facilitating an advanced visualization of cellular morphology related to different cell types and conditions.
  • The method is highly automated, allowing rapid analysis of large datasets (up to 20,000 cells) in under 60 minutes, while effectively measuring the morphological heterogeneity within cell populations.