Comparative Analysis of Packages and Algorithms for the Analysis of Spatially Resolved Transcriptomics Data

Natalie Charitakis; Mirana Ramialison; Hieu T. Nim

doi:10.1007/978-3-030-87821-4_7

Comparative Analysis of Packages and Algorithms for the Analysis of Spatially Resolved Transcriptomics Data

Natalie Charitakis, Mirana Ramialison, Hieu T. Nim

Australian Regenerative Medicine Institute

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Research › peer-review

3 Citations (Scopus)

Abstract

The technology to generate Spatially Resolved Transcriptomics (SRT) data is rapidly being improved and applied to investigate a variety of biological tissues. The ability to interrogate how spatially localised gene expression can lend new insight to different tissue development is critical, but the appropriate tools to analyse this data are still emerging. This chapter reviews available packages and pipelines for the analysis of different SRT datasets with a focus on identifying spatially variable genes (SVGs) alongside other aims, while discussing the importance of and challenges in establishing a standardised ‘ground truth’ in the biological data for benchmarking.

Original language	English
Title of host publication	Transcriptomics in Health and Disease
Editors	Geraldo A. Passos
Place of Publication	Switzerland
Publisher	Springer
Chapter	7
Pages	165-186
Number of pages	22
Edition	2nd
ISBN (Electronic)	9783030878214
ISBN (Print)	9783030878207
DOIs	https://doi.org/10.1007/978-3-030-87821-4_7
Publication status	Published - 2022

Keywords

Biological tissues
Gene regulatory networks
Organ development
Pipeline
ScRNA-Seq
Spatial biology
Spatial expression patterns
Spatial transcriptomics
SpatialDE
Spatially localised gene expression
Spatially resolved transcriptomics
Transcriptomic approaches

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10.1007/978-3-030-87821-4_7

Cite this

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abstract = "The technology to generate Spatially Resolved Transcriptomics (SRT) data is rapidly being improved and applied to investigate a variety of biological tissues. The ability to interrogate how spatially localised gene expression can lend new insight to different tissue development is critical, but the appropriate tools to analyse this data are still emerging. This chapter reviews available packages and pipelines for the analysis of different SRT datasets with a focus on identifying spatially variable genes (SVGs) alongside other aims, while discussing the importance of and challenges in establishing a standardised {\textquoteleft}ground truth{\textquoteright} in the biological data for benchmarking.",

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Comparative Analysis of Packages and Algorithms for the Analysis of Spatially Resolved Transcriptomics Data. / Charitakis, Natalie; Ramialison, Mirana ; Nim, Hieu T.
Transcriptomics in Health and Disease. ed. / Geraldo A. Passos. 2nd. ed. Switzerland: Springer, 2022. p. 165-186.

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Research › peer-review

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T1 - Comparative Analysis of Packages and Algorithms for the Analysis of Spatially Resolved Transcriptomics Data

AU - Charitakis, Natalie

AU - Ramialison, Mirana

AU - Nim, Hieu T.

N1 - Publisher Copyright: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2014, 2022.

PY - 2022

Y1 - 2022

N2 - The technology to generate Spatially Resolved Transcriptomics (SRT) data is rapidly being improved and applied to investigate a variety of biological tissues. The ability to interrogate how spatially localised gene expression can lend new insight to different tissue development is critical, but the appropriate tools to analyse this data are still emerging. This chapter reviews available packages and pipelines for the analysis of different SRT datasets with a focus on identifying spatially variable genes (SVGs) alongside other aims, while discussing the importance of and challenges in establishing a standardised ‘ground truth’ in the biological data for benchmarking.

AB - The technology to generate Spatially Resolved Transcriptomics (SRT) data is rapidly being improved and applied to investigate a variety of biological tissues. The ability to interrogate how spatially localised gene expression can lend new insight to different tissue development is critical, but the appropriate tools to analyse this data are still emerging. This chapter reviews available packages and pipelines for the analysis of different SRT datasets with a focus on identifying spatially variable genes (SVGs) alongside other aims, while discussing the importance of and challenges in establishing a standardised ‘ground truth’ in the biological data for benchmarking.

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KW - Gene regulatory networks

KW - Organ development

KW - Pipeline

KW - ScRNA-Seq

KW - Spatial biology

KW - Spatial expression patterns

KW - Spatial transcriptomics

KW - SpatialDE

KW - Spatially localised gene expression

KW - Spatially resolved transcriptomics

KW - Transcriptomic approaches

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SN - 9783030878207

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EP - 186

BT - Transcriptomics in Health and Disease

A2 - Passos, Geraldo A.

PB - Springer

CY - Switzerland

ER -

Comparative Analysis of Packages and Algorithms for the Analysis of Spatially Resolved Transcriptomics Data

Abstract

Keywords

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