The scverse project provides a computational ecosystem for single-cell omics data analysis

Virshup, Isaac; Bredikhin, Danila; Heumos, Lukas; Palla, Giovanni; Sturm, Gregor; Gayoso, Adam; Kats, Ilia; Koutrouli, Mikaela; Angerer, Philipp; Bergen, Volker; Boyeau, Pierre; Büttner, Maren; Eraslan, Gökçen; Fischer, David; Frank, Max; Hong, Justin; Klein, Michal; Lange, Marius; Lopez, Romain; Lotfollahi, Mohammad; Luecken, Malte D.; Ramírez, Fidel; Regier, Jeffrey; Rybakov, Sergei; Schaar, Anna C.; Amiri, Valeh Valiollah Pour; Weiler, Philipp; Xing, Galen; Berger, Bonnie; Pe’er, Dana; Regev, Aviv; Teichmann, Sarah A.; Finotello, Francesca; Wolf, F. Alexander; Yosef, Nir; Stegle, Oliver; Theis, Fabian J.

doi:10.1038/s41587-023-01733-8

Cited by 124 publications

(50 citation statements)

References 24 publications

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“…To allow VData objects to be used directly by tools of the scverse [23] without first converting VData objects to AnnData and thus giving up on the efficient memory usage, VData objects can be wrapped into an ‘AnnDataProxy’. AnnDataProxies inherit from AnnData and parse AnnData operations or method calls to produce valid VData operations.…”

Section: Resultsmentioning

confidence: 99%

VData: Temporally annotated data manipulation and storage

Bouvier,

Bonnaffoux

2023

Preprint

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BackgroundRecent advances in both single-cell sequencing technologies and gene expression simulation algorithms have led to the production of increasingly large datasets. Larger datasets (tens or hundreds of Gigabytes) can no longer fit on regular computers’ RAM and thus pose important challenges for storage and manipulation. Existing solutions offer partial solutions but do not explicitly handle the temporal dimension of simulated data and still require large amounts of RAM to run.ResultsVData is a Python extension to the widely used AnnData format that solves these issues by extending 2D dataframes to 3 dimensions (cells, genes and time). VData is built on top of Ch5mpy, a custom built Python library for easily working with hdf5 files and which allows to reduce the memory footprint to the minimum.ConclusionsVData allows to store and manipulate very large datasets of (empirical or simulated) time-stamped data. Since it follows the original Ann-Data format, it is compatible with the scverse tools and AnnData users will find it easy to use.

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Section: Resultsmentioning

confidence: 99%

VData: Temporally annotated data manipulation and storage

Bouvier,

Bonnaffoux

2023

Preprint

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“…Next the model was fine-tuned with scANVI 41 using ‘Subregion’ and ‘CellClass’ as cell type labels with a batch size of 1024 for a maximum of 100 epochs with early stopping criterion and n_samples_per_label=100. To project the organoids atlas to the primary atlas, we used the scArches 54 implementation provided by scvi-tools 58,59 . The query model was finetuned with a batch size of 1024 for a maximum of 100 epochs with early stopping criterion and weight_decay=0.0.…”

Section: Methodsmentioning

confidence: 99%

An integrated transcriptomic cell atlas of human neural organoids

He,

Dony,

Fleck

et al. 2023

Preprint

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Neural tissues generated from human pluripotent stem cells in vitro (known as neural organoids) are becoming useful tools to study human brain development, evolution and disease. The characterization of neural organoids using single-cell genomic methods has revealed a large diversity of neural cell types with molecular signatures similar to those observed in primary human brain tissue. However, it is unclear which domains of the human nervous system are covered by existing protocols. It is also difficult to quantitatively assess variation between protocols and the specific cell states in organoids as compared to primary counterparts. Single-cell transcriptome data from primary tissue and neural organoids derived with guided or un-guided approaches and under diverse conditions combined with large-scale integrative analyses make it now possible to address these challenges. Recent advances in computational methodology enable the generation of integrated atlases across many data sets. Here, we integrated 36 single-cell transcriptomics data sets spanning 26 protocols into one integrated human neural organoid cell atlas (HNOCA) totaling over 1.7 million cells. We harmonize cell type annotations by incorporating reference data sets from the developing human brain. By mapping to the developing human brain reference, we reveal which primary cell states have been generated in vitro, and which are under-represented. We further compare transcriptomic profiles of neuronal populations in organoids to their counterparts in the developing human brain. To support rapid organoid phenotyping and quantitative assessment of new protocols, we provide a programmatic interface to browse the atlas and query new data sets, and showcase the power of the atlas to annotate new query data sets and evaluate new organoid protocols. Taken together, the HNOCA will be useful to assess the fidelity of organoids, characterize perturbed and diseased states and facilitate protocol development in the future.

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“…Future engineering work will focus on leveraging the AnnData [59] Python package for handling large datasets that cannot be fit in RAM. Furthermore, using AnnData within scBoolSeq will allow its integration in the scverse [60] computational ecosystem for single-cell omics data analysis.…”

Section: Implementation and Usagementioning

confidence: 99%

scBoolSeq: Linking scRNA-Seq Statistics and Boolean Dynamics

Maganã López,

Calzone,

Zinovyev

et al. 2023

Preprint

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Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expressions in cells, as scRNA-Seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-Seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-Seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-Seq datasets, including dropout events, with Boolean states is a challenging task. We present scBoolSeq, a method for the bidirectional linking of scRNA-Seq data and Boolean activation state of genes. Given a reference scRNA-Seq dataset, scBoolSeq computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, scBoolSeq can perform both binarisation of scRNA-Seq datasets, and generate synthetic scRNA-Seq datasets from Boolean trajectories, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of scBoolSeqs binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-Seq data generated by scBoolSeq with BoolODE from the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-Seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in a two-dimensional projection of the data.

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The scverse project provides a computational ecosystem for single-cell omics data analysis

Cited by 124 publications

References 24 publications

VData: Temporally annotated data manipulation and storage

VData: Temporally annotated data manipulation and storage

An integrated transcriptomic cell atlas of human neural organoids

scBoolSeq: Linking scRNA-Seq Statistics and Boolean Dynamics

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