The Human Cell Atlas

Regev, Aviv; Teichmann, Sarah A.; Lander, Eric S.; Amit, Ido; Benoist, Christophe; Birney, Ewan; Bodenmiller, Bernd; Campbell, Peter J.; Carninci, Piero; Clatworthy, Menna R.; Clevers, Hans; Deplancke, Bart; Dunham, Ian; Eberwine, James; Eils, Roland; Enard, Wolfgang; Farmer, Andrew; Fugger, Lars; Göttgens, Berthold; Hacohen, Nir; Haniffa, Muzlifah; Hemberg, Martin; Kim, Seung K.; Klenerman, Paul; Kriegstein, Arnold R.; Lein, Ed; Linnarsson, Sten; Lundeberg, Joakim; Majumder, Partha P.; Marioni, John C.; Mérad, Miriam; Mhlanga, Musa M.; Nawijn, Martijn C.; Netea, Mihai G.; Nolan, Garry P.; Pe’er, Dana; Philipakis, Anthony; Ponting, Chris P.; Quake, Stephen R.; Reik, Wolf; Rozenblatt-Rosen, Orit; Sanes, Joshua R.; Satija, Rahul; Shumacher, Ton; Shalek, Alex K.; Shapiro, Ehud; Sharma, Padmanee; Shin, Jay W.; Stegle, Oliver; Stratton, Michael; Stubbington, Michael J T; Oudenaarden, Alexander van; Wagner, Allon; Watt, Fiona M.; Weissman, Jonathan S.; Wold, B; Xavier, Ramnik J.; Yosef, Nir; Participants, Human Cell Atlas Meeting

doi:10.1101/121202

Cited by 277 publications

(304 citation statements)

References 191 publications

(224 reference statements)

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“…Finally, besides the technical challenges posed by multicollinearity, deep deconvolution is further hampered by the intrinsically plastic and dynamic nature of the immune system, which results in the co-existence of a continuum of immune phenotypes and prevents a clear distinction between the concepts of cell type and cell state [59]. …”

Section: Challenges In the Quantification Of Tumor-infiltrating Immunmentioning

confidence: 99%

Quantifying tumor-infiltrating immune cells from transcriptomics data

Finotello

Trajanoski

2018

Cancer Immunol Immunother

329

274

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By exerting pro- and anti-tumorigenic actions, tumor-infiltrating immune cells can profoundly influence tumor progression, as well as the success of anti-cancer therapies. Therefore, the quantification of tumor-infiltrating immune cells holds the promise to unveil the multi-faceted role of the immune system in human cancers and its involvement in tumor escape mechanisms and response to therapy. Tumor-infiltrating immune cells can be quantified from RNA sequencing data of human tumors using bioinformatics approaches. In this review, we describe state-of-the-art computational methods for the quantification of immune cells from transcriptomics data and discuss the open challenges that must be addressed to accurately quantify immune infiltrates from RNA sequencing data of human bulk tumors.Electronic supplementary materialThe online version of this article (10.1007/s00262-018-2150-z) contains supplementary material, which is available to authorized users.

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Section: Challenges In the Quantification Of Tumor-infiltrating Immunmentioning

confidence: 99%

Quantifying tumor-infiltrating immune cells from transcriptomics data

Finotello

Trajanoski

2018

Cancer Immunol Immunother

329

274

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“…http://dx.doi.org/10.1101/206573 doi: bioRxiv preprint first posted online Oct. 20, 2017; technology continues to improve analysis tools will have to adapt to significantly larger datasets (in the millions of cells) which may require specialised data structures and algorithms. Methods for combining multiple scRNA-seq datasets as well as integration of scRNA-seq data with other single-cell data types, such as DNA-seq, ATAC-seq or methylation, with be another area of growth and projects such as the Human Cell Atlas 48 will provide comprehensive cell type references which will open up new avenues for analysis.…”

Section: Resultsmentioning

confidence: 99%

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

Zappia

Phipson

Oshlack

2017

Preprint

101

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As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers.In order to better facilitate selection of appropriate analysis tools we have been cataloguing and curating new analysis tools, as they become available, in the scRNAtools database (www.scRNA-tools.org). Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools are developed to perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and as a record of the growth of the field over time.

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“…Long-read [8] or targeted RNA sequencing [9] often reveals new or erroneous transcript models, even in wellannotated loci, and predictions of enhancers produce largely discordant results [10]. The value of annotations generated by large-scale big data 'omics projects', such as ENCODE [4], FANTOM5 [3] and the Human Cell Atlas [11], is not an immediate gain of new biological knowledge. Instead, their value is technical, by providing new standards, analytical approaches and reagents, as well as data that is accessible, standardised, reusable and extensive that can be exploited by anyone in order to frame new mechanistic hypotheses.…”

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confidence: 99%

Big knowledge from big data in functional genomics

Ponting

2017

Emerging Topics in Life Sciences

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With so much genomics data being produced, it might be wise to pause and consider what purpose this data can or should serve. Some improve annotations, others predict molecular interactions, but few add directly to existing knowledge. This is because sequence annotations do not always implicate function, and molecular interactions are often irrelevant to a cell's or organism's survival or propagation. Merely correlative relationships found in big data fail to provide answers to the Why questions of human biology. Instead, those answers are expected from methods that causally link DNA changes to downstream effects without being confounded by reverse causation. These approaches require the controlled measurement of the consequences of DNA variants, for example, either those introduced in single cells using CRISPR/Cas9 genome editing or that are already present across the human population. Inferred causal relationships between genetic variation and cellular phenotypes or disease show promise to rapidly grow and underpin our knowledge base.Single-gene studies in model or cellular systems have substantially advanced knowledge in the life sciences. Progress has relied on scientific acumen and on technological advances that provide detailed insights into processes at the atomic, molecular, multisubunit complex, cellular and sometimes organismal levels. These many successes, however, should not blind us as to how our knowledge is incomplete and error-prone. Virtually all (99.85%) protein sequences have no associated experimental evidence at the protein level and for 52% their annotations are flagged as containing possible errors (www.ebi.ac.uk/uniprot/TrEMBLstats). Furthermore, scientific knowledge from targeted studies has been gained unevenly: of all human brain-expressed genes for example, science has focused on very few, with the top 5% of such genes being the subject of 70% of the literature [1].Whole-genome experiments seek to address these deficiencies of uneven coverage and incompleteness. These are aided by technological innovations that inexorably generate ever larger data sets. Critically, however, big data analysis per se reveals not mechanistic causes, but rather correlations and patterns, and leaves questions starting Why unanswered [2]. Even when subsequent experiments address more narrowly defined hypotheses while exploiting this data, these also often fail to determine causality. Correlations and patterns may describe the data set well, but they need to be supplemented by causal inferences in order to predict phenomena reliably. The transformation of large, unstructured data sets to insights (Figure 1) and predictive biology is challenging and rarely attained.In human genomics, data and annotations have grown rapidly. The 3.2 billion base reference genome is partitioned currently into 20 338 protein-coding and 22 521 non-protein-coding gene annotations that are transcribed into 200 310 transcripts (www.ensembl.org/Homo_sapiens/Info/ Annotation) that start from 308 214 locations [3]. Binding sites, often...

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The Human Cell Atlas

Cited by 277 publications

References 191 publications

Quantifying tumor-infiltrating immune cells from transcriptomics data

Quantifying tumor-infiltrating immune cells from transcriptomics data

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

Big knowledge from big data in functional genomics

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