Stemformatics: visualize and download curated stem cell data

Choi, Jarny; Pacheco, Chris M.; Mosbergen, Rowland; Korn, Othmar; Chen, Tyrone; Nagpal, Isha; Englart, Steve; Angel, Paul W.; Wells, Christine A.

doi:10.1093/nar/gky1064

Cited by 35 publications

(37 citation statements)

References 14 publications

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“…Here, we use the Stemformatics catalog [20], which has curated hundreds of studies, to assess the extent that platform impacts on expression variance for each gene. This challenged our initial assumption that accounting for batch necessitates an adjustment to every gene expression value.…”

Section: Plos Computational Biologymentioning

confidence: 99%

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A simple, scalable approach to building a cross-platform transcriptome atlas

et al. 2020

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Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows.

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Section: Plos Computational Biologymentioning

confidence: 99%

“…Datasets were excluded if they showed evidence of over-amplification, incomplete data availability in the public databases GEO or ArrayExpress, incomplete sample metadata or identified sample-swaps, or where experimental design was confounded. Details of the Stemformatics data curation pipeline are available [20,21].…”

Section: Data Curationmentioning

confidence: 99%

A simple, scalable approach to building a cross-platform transcriptome atlas

et al. 2020

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“…Mapping, and analysis of microarray and RNA sequencing datasets were conducted in the Stemformatics platform. Scripts are available for download from the Stemformatics BitBucket (Choi et al, 2019a)). All datasets and relevant samples ( Supplementary Table 6) passed stringent quality control checks required for hosting on the stemformatics platform.…”

Section: Supplemental Videomentioning

confidence: 99%

“…Samples were curated with respect to phenotype, source and isolation method. Datasets were processed through the Stemformatics pipeline which includes a stringent set of quality control requirements for hosting on the Stemformatics.org portal (Choi et al, 2019a). We constructed the atlas by implementing a two-step process (Angel et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

An integrated analysis of myeloid cells identifies gaps in in vitro models of in vivo biology

Rajab

Angel

Deng

et al. 2019

Preprint

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SummaryThe Stemformatics myeloid atlas is an integrated transcriptome atlas of human macrophages and dendritic cells that systematically compares freshly isolated tissue-resident, cultured, and stem-cell derived myeloid cell types. We identified two broad classes of tissue-resident macrophages with lung, gut and tumour-associated macrophages most similar to monocytes. Microglia, Kupffer cells and synovial macrophages shared similar profiles with each other, and with cultured macrophages. Pluripotent stem cell-derived macrophages were not reminiscent of fetal-derived cells. Instead, they were characterized by atypical expression of collagen and a highly efferocytotic phenotype. Likewise, Flt3L-derived cord blood dendritic cells were distinct from conventional dendritic cell subsets isolated from primary tissues and lacked expression of key pattern recognition receptors. Myeloid subsets were reproducible across different experimental series, showing the resource is a robust reference for new data. External users can annotate and benchmark their own samples, including annotation of myeloid single cell data at www.stemformatics.org/atlas/myeloid/.

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“…Existing tools for user-friendly data exploration and visualization include Stemformatics [21], Metascape [22], and mixOmics [23]. Stemformatics is an online portal that assembles gene expression data from stem cell datasets.…”

Section: Introductionmentioning

confidence: 99%

ShinyOmics: collaborative exploration of omics-data

Surujon

Opijnen

2020

BMC Bioinformatics

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Background: Omics-profiling is a collection of increasingly prominent approaches that result in large-scale biological datasets, for instance capturing an organism's behavior and response in an environment. It can be daunting to manually analyze and interpret such large datasets without some programming experience. Additionally, with increasing amounts of data; management, storage and sharing challenges arise. Results: Here, we present ShinyOmics, a web-based application that allows rapid collaborative exploration of omics-data. By using Tn-Seq, RNA-Seq, microarray and proteomics datasets from two human pathogens, we exemplify several conclusions that can be drawn from a rich dataset. We identify a protease and several chaperone proteins upregulated under aminoglycoside stress, show that antibiotics with the same mechanism of action trigger similar transcriptomic responses, point out the dissimilarity in different omics-profiles, and overlay the transcriptional response on a metabolic network. Conclusions: ShinyOmics is easy to set up and customize, and can utilize user supplied metadata. It offers several visualization and comparison options that are designed to assist in novel hypothesis generation, as well as data management, online sharing and exploration. Moreover, ShinyOmics can be used as an interactive supplement accompanying research articles or presentations.

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Stemformatics: visualize and download curated stem cell data

Cited by 35 publications

References 14 publications

A simple, scalable approach to building a cross-platform transcriptome atlas

A simple, scalable approach to building a cross-platform transcriptome atlas

An integrated analysis of myeloid cells identifies gaps in in vitro models of in vivo biology

ShinyOmics: collaborative exploration of omics-data

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