SEMITONES: Single-cEll Marker IdentificaTiON by Enrichment Scoring

Vlot, Anna Hendrika Cornelia; Ohler, Uwe

doi:10.1101/2020.11.17.386664

Cited by 3 publications

(3 citation statements)

References 51 publications

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“…We first mapped cells to 3D root geometry locations ( Schmidt et al, 2014 ) using novoSpaRc ( Nitzan et al, 2019 ), an algorithm that reconstructs the locations of single cells in space based on scRNA-seq data ( Dataset S1 ; STAR Methods ). Secondly, we used SEMITONES ( Vlot et al, 2020 ), an algorithm that identifies enriched features in single cell data without prior clustering, to estimate the enrichment of marker gene expression in cell neighborhoods. Third, we calculated the correlation coefficient of each cell’s expression profile to published gene expression profiles of root cell types isolated with fluorescent reporters ( Brady et al, 2007a ; Li et al, 2016 ).…”

Section: Resultsmentioning

confidence: 99%

“…The enrichment scores of known cell type-specific markers ( De Rybel et al, 2013 ; Schürholz et al, 2018 ; Muñiz et al, 2008 ; Menand et al, 2007 ; Bonke et al, 2003 ; Clay and Nelson, 2005 ; Lee and Schiefelbein, 2002 ; Brady et al, 2007b ; Kamiya et al, 2016 ; Huang et al, 2017 ; Miyashima et al, 2019 ; Matsuzaki et al, 2010 ; Wallner et al, 2017 ; Ishida et al, 2009 ; Taniguchi et al, 2017 ; Kamiya et al, 2015 ; Aida et al, 2004 ; Kubo et al, 2005 ; Lee and Schiefelbein, 1999 ) ( Dataset S1 ) were calculated for each cell in the atlas using SEMITONES ( Vlot et al, 2020 ; github.com/ohlerlab/SEMITONES ). SEMITONES uses cluster/reference-free, rank based statistics to calculate the significance of local enrichment of gene expression based on a distance between cells.…”

Section: Star Methodsmentioning

confidence: 99%

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A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

et al. 2022

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Section: Resultsmentioning

confidence: 99%

Section: Star Methodsmentioning

confidence: 99%

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

et al. 2022

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“…Several of these approaches have also been applied to probe set selection 3,4,6 . Dedicated probe set selection approaches that use a reference scRNA-seq dataset to optimize for cell type classification [20][21][22][23][24][25][26][27][28][29][30] , or the capture of transcriptional variation [31][32][33][34] have also been proposed. However, few approaches account for both cell-type and gene variation, and none of the above methods include technical constraints in their selection procedure.…”

Section: Introductionmentioning

confidence: 99%

Probe set selection for targeted spatial transcriptomics

Kuemmerle

Luecken

Firsova

et al. 2022

Preprint

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Targeted spatial transcriptomics methods capture the topology of cell types and states in tissues at single cell- and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing and interpreting the spatial signals present in a tissue. However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or novel states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both probe set specificity for cell type identification and within-cell-type expression variation to resolve spatially distinct populations while taking into account prior knowledge, as well as probe design and expression constraints. To facilitate data analysis and interpretation, Spapros also provides rules for cell type identification. We evaluated Spapros by selecting probes on 6 different data sets and built an evaluation pipeline with 12 quality metrics to find that Spapros outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a SCRINSHOT experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types. Spapros enables optimal probe set selection, probe set evaluation, and probe design, as a freely available Python package.

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Assisted design of data science pipelines

Redyuk,

Kaoudi,

Schelter

et al. 2024

The VLDB Journal

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When designing data science (DS) pipelines, end-users can get overwhelmed by the large and growing set of available data preprocessing and modeling techniques. Intelligent discovery assistants (IDAs) and automated machine learning (AutoML) solutions aim to facilitate end-users by (semi-)automating the process. However, they are expensive to compute and yield limited applicability for a wide range of real-world use cases and application domains. This is due to (a) their need to execute thousands of pipelines to get the optimal one, (b) their limited support of DS tasks, e.g., supervised classification or regression only, and a small, static set of available data preprocessing and ML algorithms; and (c) their restriction to quantifiable evaluation processes and metrics, e.g., tenfold cross-validation using the ROC AUC score for classification. To overcome these limitations, we propose a human-in-the-loop approach for the assisteddesignofdatasciencepipelines using previously executed pipelines. Based on a user query, i.e., data and a DS task, our framework outputs a ranked list of pipeline candidates from which the user can choose to execute or modify in real time. To recommend pipelines, it first identifies relevant datasets and pipelines utilizing efficient similarity search. It then ranks the candidate pipelines using multi-objective sorting and takes user interactions into account to improve suggestions over time. In our experimental evaluation, the proposed framework significantly outperforms the state-of-the-art IDA tool and achieves similar predictive performance with state-of-the-art long-running AutoML solutions while being real-time, generic to any evaluation processes and DS tasks, and extensible to new operators.

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SEMITONES: Single-cEll Marker IdentificaTiON by Enrichment Scoring

Cited by 3 publications

References 51 publications

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

Probe set selection for targeted spatial transcriptomics

Assisted design of data science pipelines

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