Sfaira accelerates data and model reuse in single cell genomics

Fischer, David; Dony, Leander; Koenig, Martin; Moeed, Abdul; Zappia, Luke; Tritschler, Sophie; Holmberg, Olle; Aliee, Hananeh; Theis, Fabian J.

doi:10.1101/2020.12.16.419036

Cited by 10 publications

(10 citation statements)

References 63 publications

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“…The HLCA core is publicly available as a data portal and a model repository to explore, download, and use as a reference for new datasets. As the atlassing community has multiple outlets for newly generated data, we made the atlas available in Sfaira 70 , Zenodo 71 , Azimuth 14 , CellTypist 72 , and FASTGenomics 73 .…”

Section: Discussionmentioning

confidence: 99%

An integrated cell atlas of the human lung in health and disease

Luecken

Sikkema

Strobl

et al. 2022

Preprint

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Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

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Section: Discussionmentioning

confidence: 99%

An integrated cell atlas of the human lung in health and disease

Luecken

Sikkema

Strobl

et al. 2022

Preprint

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“…For simplicity, normalized expression data are simply referred to as TPM throughout the manuscript. In addition, SimBu allows selecting annotated scRNA-seq data available through the Sfaira database (Fischer et al ., 2021). Starting from the input scRNA-seq data, an object from the SummarizedExperiment (Morgan et al ., 2021; Huber et al ., 2015) package is built.…”

Section: Methodsmentioning

confidence: 99%

“…1a). ScRNA-seq datasets can be obtained through the Sfaira (Fischer et al ., 2021) database, which provides access to annotated scRNA-seq datasets from various organisms, tissues, and cell types. SimBu samples single cells from a scRNA-seq expression matrix and aggregates their transcriptomes to build pseudo-bulk RNA-seq expression profiles, summarized as gene counts, counts per million (CPM), or transcripts per millions (TPM), depending on the input data (Fig.…”

Section: Introductionmentioning

confidence: 99%

SimBu: Bias-aware simulation of bulk RNA-seq data with variable cell type composition

Dietrich

Sturm

Merotto

et al. 2022

Preprint

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As complex tissues are typically composed of various cell types, deconvolution tools have been developed to computationally infer their cellular composition from bulk RNA sequencing (RNA-seq) data. To comprehensively assess deconvolution performance, gold-standard datasets are indispensable. Goldstandard, experimental techniques like flow cytometry or immunohistochemistry are resource-intensive and cannot be systematically applied to the numerous cell types and tissues profiled with high-throughput transcriptomics. The simulation of 'pseudo-bulk' data, generated by aggregating single-cell RNA-seq (scRNA-seq) expression profiles in pre-defined proportions, offers a scalable and cost-effective alternative. This makes it feasible to create in silico gold standards that allow fine-grained control of cell-type fractions not conceivable in an experimental setup. However, at present, no simulation software for generating pseudo-bulk RNA-seq data exists. We developed SimBu, an R package capable of simulating pseudo-bulk samples based on various simulation scenarios, designed to test specific features of deconvolution methods. A unique feature of SimBu is the modelling of cell-type-specific mRNA bias using experimentally-derived or data-driven scaling factors. Here, we show that SimBu can generate realistic pseudo-bulk data, recapitulating the biological and statistical features of real RNA-seq data. Finally, we illustrate the impact of mRNA bias on the evaluation of deconvolution tools and provide recommendations for the selection of suitable methods for estimating mRNA content. SimBu is a user-friendly and flexible tool for simulating realistic pseudo-bulk RNA-seq datasets serving as in silico gold-standard for assessing cell-type deconvolution methods. SimBu is freely available at https://github.com/omnideconv/SimBu as an R package under the GPL-3 license.

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“…A recent review and repository of single-cell perturbation data for machine learning lists 22 datasets, but supplied cleaned and format-unified data for only 6 (Ji et al, 2021). An existing unified framework for single cell data, called ‘sfaira’, is ideal for model building and memory efficient data loading, but the public ‘data zoo’ does not currently supply perturbation datasets or standardized perturbation annotations (Fischer et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

scPerturb: Harmonized Single-Cell Perturbation Data

Peidli

Green

Shen

et al. 2022

Preprint

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Recent biotechnological advances led to growing numbers of single-cell studies, which reveal molecular and phenotypic responses to large numbers of perturbations. However, analysis across diverse datasets is typically hampered by differences in format, naming conventions, data filtering and normalization. In order to facilitate development and benchmarking of computational methods in systems biology, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform pre-processing and quality control pipelines and harmonize feature annotations. The resulting information resource enables efficient development and testing of computational analysis methods, and facilitates direct comparison and integration across datasets. Using these datasets, we demonstrate the application of E-distance for quantifying perturbation similarity and strength. This work provides an information resource and guide for researchers working with single-cell perturbation data and highlights conceptual considerations for new experiments. The data collection, scPerturb, is publicly available at scperturb.org.

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Sfaira accelerates data and model reuse in single cell genomics

Cited by 10 publications

References 63 publications

An integrated cell atlas of the human lung in health and disease

An integrated cell atlas of the human lung in health and disease

SimBu: Bias-aware simulation of bulk RNA-seq data with variable cell type composition

scPerturb: Harmonized Single-Cell Perturbation Data

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