geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq

Missarova, Alsu; Jain, Jaison; Butler, Andrew; Ghazanfar, Shila; Stuart, T.A.; Brusko, Todd M.; Wasserfall, Clive; Nick, Harry S.; Brusko, Todd M.; Atkinson, Mark A.; Satija, Rahul; Marioni, John C.

doi:10.1186/s13059-021-02548-z

Cited by 20 publications

(12 citation statements)

References 67 publications

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“…The method of choosing marker genes should also be robust. Although here we used simple differential expression analysis for picking marker genes, several recent software packages have been released that enable more nuanced approaches for marker gene panel creation ( Aevermann et al, 2021 ; Missarova et al, 2021 ; Chen et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

et al. 2022

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Spatial transcriptomics extends single-cell RNA sequencing (scRNA-seq) by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as multiplexed error-robust fluorescence in situ hybridization (MERFISH) can achieve single-cell resolution, directly mapping single-cell identities to spatial positions. MERFISH produces a different data type than scRNA-seq, and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on the mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas and from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both the liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that MERFISH provides a quantitatively comparable method for single-cell gene expression and can identify cell types without the need for computational integration with scRNA-seq atlases.

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Section: Discussionmentioning

confidence: 99%

Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

et al. 2022

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“…The method of choosing marker genes should also be robust. Although here we utilized simple differential expression analysis for picking marker genes, several recent software packages have been released that enable more nuanced approaches for marker gene panel creation (Aevermann et al 2021;Missarova et al 2021; X. Chen, Chen, and Thomson 2022).…”

Section: Discussionmentioning

confidence: 99%

Concordance of MERFISH Spatial Transcriptomics with Bulk and Single-cell RNA Sequencing

Liu

Tran

Vemuri

et al. 2022

Preprint

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Spatial transcriptomics extends single cell RNA sequencing (scRNA-seq) technologies by providing spatial context for cell type identification and analysis. In particular, imaging-based spatial technologies such as Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) can achieve single-cell resolution, allowing for the direct mapping of single cell identities to spatial positions. Nevertheless, because MERFISH produces an intrinsically different data type than scRNA-seq methods, a technical comparison between the two modalities is necessary to ascertain how best to integrate them. Here, we used the Vizgen MERSCOPE platform to perform MERFISH on mouse liver and kidney tissues and compared the resulting bulk and single-cell RNA statistics with those from the existing Tabula Muris Senis cell atlas. We found that MERFISH produced measurements that quantitatively reproduced the bulk RNA-seq and scRNA-seq results, with some minor differences in overall gene dropout rates and single-cell transcript count statistics. Finally, we explored the ability of MERFISH to identify cell types, and found that it could independently resolve distinct cell types and spatial structure in both liver and kidney. Computational integration with the Tabula Muris Senis atlas using scVI and scANVI did not noticeably enhance these results. We conclude that compared to scRNA-seq, MERFISH provides a quantitatively comparable method for measuring single-cell gene expression, and that efficient gene panel design allows for robust identification of cell types with intact spatial information without the need for computational integration with scRNA-seq reference atlases.

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“…These methods are designed primarily to reduce computation and inform clustering studies that help determine marker genes, but they are not intended to directly select gene panels; therefore, their performance is not expected to be competitive with methods designed explicitly for selecting small gene sets. Next, we tested the recently proposed GeneBasis method 41 that selects genes using a greedy algorithm to preserve the data manifold. Finally, we considered three methods that aim to differentiate cell types: a method that maximizes the information about cell type labels (MutInfo) 9,42 , a method that identifies key gene predictors using feature importance scores (SMaSH) 43 , and the scGeneFit method 44 that uses linear programming.…”

Section: Selecting Genes Using Deep Learningmentioning

confidence: 99%

Predictive and robust gene selection for spatial transcriptomics

et al. 2023

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A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell’s molecular state. This typically requires targeting an a priori selection of genes, often covering less than 1% of the genome, and a key question is how to optimally determine the small gene panel. We address this challenge by introducing a flexible deep learning framework, PERSIST, to identify informative gene targets for spatial transcriptomics studies by leveraging reference scRNA-seq data. Using datasets spanning different brain regions, species, and scRNA-seq technologies, we show that PERSIST reliably identifies panels that provide more accurate prediction of the genome-wide expression profile, thereby capturing more information with fewer genes. PERSIST can be adapted to specific biological goals, and we demonstrate that PERSIST’s binarization of gene expression levels enables models trained on scRNA-seq data to generalize with to spatial transcriptomics data, despite the complex shift between these technologies.

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geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq

Cited by 20 publications

References 67 publications

Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

Concordance of MERFISH Spatial Transcriptomics with Bulk and Single-cell RNA Sequencing

Predictive and robust gene selection for spatial transcriptomics

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