nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes.

Lukas M Weber, Arkajyoti Saha, Abhirup Datta, Kasper D Hansen, Stephanie C Hicks

July 2023 Nat Commun

Synopsis of Social media discussions

Several discussions reference the article by quoting its title, such as 'nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes,' with little additional commentary, indicating casual recognition rather than in-depth engagement. The tone and context suggest a general acknowledgment rather than significant enthusiasm or critique, which aligns with the moderate interest and impact scores.

A
Agreement
 Neither agree nor disagree

The posts do not show strong disagreement or strong support; they mainly acknowledge the publication without explicit endorsement or criticism.

I
Interest
 Moderate level of interest

The interest score is modest because discussions simply mention the article title without deep analysis or critique.

E
Engagement
 Neutral engagement

The engagement appears superficial, with minimal interaction or elaboration beyond quoting or referencing the publication.

I
Impact
 Moderate level of impact

The perceived impact is moderate, as the posts suggest awareness but do not indicate widespread influence or application.

Social Mentions

YouTube

1 Videos

Facebook

2 Posts

Twitter

3 Posts

Blogs

2 Articles

Metrics

Video Views

8

Total Likes

3

Extended Reach

3,563

Social Features

8

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

scalable identification of spatially variable genes using nnSVG method

scalable identification of spatially variable genes using nnSVG method

nnSVG is a novel method for detecting genes that vary in expression based on spatial location in tissue samples, utilizing nearest-neighbor Gaussian processes for continuous, precise identification. It is efficient and scalable for large spatial datasets, validated through experiments and simulations.

STEM RTCL TV

October 17, 2023

8 views

  • Jungsu Kim
    @jungsukim77 (Twitter)

    [Spatial transcriptomics Pipeline] nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes https://t.co/JIfgVKzSq0
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    July 11, 2023

    2

  • know thyself（子豚のオリバー)
    @thyself_know (Twitter)

    RT @siero5335: 面白そう: "nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes", https:/…
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    July 10, 2023

    2

  • siero
    @siero5335 (Twitter)

    面白そう: "nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes", https://t.co/ZZD20P0Nog
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    July 10, 2023

    1

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Abstract Synopsis

  • nnSVG is a new method for finding genes that change in expression based on their spatial location in tissue samples.
  • It leverages nearest-neighbor Gaussian processes to continuously identify these spatially variable genes and improves precision with gene-specific estimates.
  • The method is efficient and scales well with increasing spatial data, and its effectiveness has been validated through real experiments and simulations.