dataesr/scientific-paragraphs-categorization huggingface dataset:
Combinatorial assessment of datasets according to significance and similarity criteria To find the best combination of datasets defining a consistent and sufficiently large quiescent signature, we ranked them according to their significance. This significance was determined according to an ensemble of criteria. First, the dataset should have a minimum number of DEGs. Our Fetal_NICD[E17. 5/E14. 5] dataset, for instance, had only 250 DEGs (Additional file 5: Table  S1 ), and using it in the analysis resulted in a dramatically reduced number of overlapping DEGs (Additional file 3: Figure  S3 ). A second criterion was the presence of genes known to be differentially expressed between quiescent and activated states from previous studies. In this case, datasets GSE38870 and GSE81096 did not meet this criterion, since they lacked genes known to be associated with or regulating the quiescent state such as Calcr, Notch1, Chrdl2, Lama3, Pax7, and Bmp6 genes (unpublished data, see Fig.
7 ;  [41] [42] [43] ). As a third criterion, we used the dataset similarity, which was assessed using the Jaccard index (JI), and a matrix of the JIs for the up-and down-regulated genes was generated (Figs. 4b, c , respectively). In both matrices, the closest pairs of datasets were GSE47177 at 60 h and GSE47177 at 84 h (JI = 0. 46 and 0. 44 for the up-and down-regulated genes, respectively), followed by the second pair of closest sets Quiescent [high]/D3Activated [high] and Quiescent [low]/ D3Activated [low] (JI = 0. 39 and 0. 33, for up-and downregulated genes, respectively). The observation that the first two closest datasets belonged to studies originating from the same laboratory underscores the impact of technical biases. The hierarchical clustering of the Euclidean distance of the Jaccard indexes shows that for up-and down-regulated genes, the datasets Fetal_-NICD[E17.
5/E14. 5], GSE38870, and GSE81096 had a tendency to not group with the rest of the datasets. In addition to these criteria, others can be used to assess the significance of the datasets. Choosing the datasets according to the activation or extraction method of the cells, for example, would result in a more stringent ensemble of datasets. Taking into account the dataset significance (based on the number of DEGs and presence of some reported quiescent markers) and the low extent of overlap between Fetal_-NICD[E17. 5/E14. 5], GSE38870, and GSE81096 datasets with respect to the remaining datasets, these three datasets were excluded from the multi-dataset analyses. The final ensemble comprised the eight remaining datasets which had 207 and 542 genes commonly up-and down-regulated, respectively (Fig.
This finding was expected given that a quiescent signature is yet to be defined, and thus current gene sets lack such annotations. To facilitate the analysis of transcriptomes as described here, we have developed an online interactive tool called Sherpa (Fig. 6 ). Sherpa allows users to perform analyses on individual and on multiple datasets. Each individual dataset analysis involves the identification of differentially expressed genes; comparison of the expression of selected genes in the quiescent and activated states through tables, heatmaps, and volcano plots; and exploration of the distribution of the samples according to their variability through principal component analysis and cluster analysis. The multiple dataset analysis allows the comparison of selected datasets according to the commonly differentially expressed genes.
All of these analyses are interactive, as they allow the user to select the thresholds of fold change (logFC) and false discovery rate (adj. P value). To assign a global function to the commonly regulated genes, we annotated them using GOSlim terms, which summarize broad terms based on Gene Ontology (GO) terms  [44]. To identify categories of genes, we generated heatmaps of the logFC in the different datasets for a subset of the 207 UP genes belonging to the extracellular matrix, nucleic acid binding activity (+/-cell cycle proliferation), and signal transduction activity (Fig. 5b ). Unexpectedly, some genes associated with cell cycle proliferation, such as c-Fos and c-Jun, were up-regulated in the quiescent cell analyses in all datasets (Fig. 7a ). To verify the transcriptional relevance of these genes in quiescent cells, we used a protocol to isolate satellite cells in which a short fixation (PFA) treatment was performed prior to harvesting the cells to arrest de novo transcription during the isolation protocol (see the "Methods" section).
Edges indicate that genes are shared among the gene sets. The thickness of the edge is proportional to the number of shared genes. Also shown are the heatmaps of logFC for genes belonging to extracellular matrix, nucleic acid binding and cell cycle and proliferation, nucleic acid binding, and signal transduction activity, respectively. Each row corresponds to a gene and each column corresponds to a dataset. Dendrograms show hierarchical clustering using the Euclidean distance mRNA (right panel) nor at the protein (left panel) level (Fig. 7c ). However, these genes were up-regulated using conventional satellite cell isolation protocols that take several hours. As a control, PFA treatment after cell isolation had no effect on this expression pattern (Additional file 4: Figure  S4 ). This rapid up-regulation was then followed by a decline in expression levels of these genes (Fig.
Discussion The transcriptome analysis and pipeline, as well as the Sherpa interface that we describe here, allow multi-scale comparisons across divergent datasets that are heterogeneous in the platform and biological condition. Notably, this pipeline allowed the examination of 11 datasets, including three novel transcriptomes from our work, as well as the identification of a variety of functional gene sets that appear in common with the majority of the datasets. To perform this analysis, it was necessary to standardize every step of the analysis to attenuate the impact of heterogeneity inherent in all of the datasets due to experimental, biological, and technical variations. These varying conditions led us to perform a combinatorial assessment of the individual datasets according to their significance and similarity criteria.
Variations in datasets are not unique to the study of muscle stem cells. Indeed, the last decades have witnessed many efforts to analyze microarray data to provide relevant gene signatures. In cancer biology, for example, gene markers were sought either for prognosis, i. e. , lists of genes able to predict clinical outcome  [45]  or for molecular subtyping, i. e. , list of genes able to classify different subtypes of a disease  [46, 47]. However, even if markers performed well, gene signatures derived from studies on the same treatments and diseases often resulted in gene lists with little overlap  [48]. In other cases, the signatures proved to be unstable, having other gene lists on the same dataset with the same predictive power  [49]. These observations suggest that such signatures may include causally related genes, i.
e. , downstream of the phenotype-causing genes, and that these gene lists may share the same biological pathways  [50]. Gene Set Enrichment Analysis (GSEA) has become an efficient complementary approach for analyzing omic data in general and GEPs in particular  [50] [51] [52]. It shifts the expression analysis from a gene space to a gene set space, where genes are organized into gene sets according to a common feature, such as a functional annotation (e. g. , a Gene Ontology term) or a specific metabolic pathway (e. g. , a KEGG pathway). In this way, it incorporates previously existing biological knowledge to drive and increase interpretation, while offering greater robustness and sensitivity than gene level strategies  [50, 53, 54]. In spite of the heterogeneity in datasets examining quiescent muscle satellite cells, we were able to identify genes that were consistently up-and down-regulated among the different datasets (Additional file 5: Table  S1 ).
The final multi-set analysis comprised eight datasets which had 207 and 542 genes that were commonly up-and downregulated, respectively. Moreover, the gene set enrichment analysis of the individual datasets showed striking similarities on the over-and under-represented gene sets. These gene sets, which summarize and represent well-defined biological states and processes in the cells, were shared among the different datasets. They include an overrepresentation of genes in the TNFα pathway via NFKβ signaling, Il6-Jak-Stat3 signaling, and the apical surface processes, and an under-representation of MYC and E2F targets, and genes associated with the G2 M checkpoint and oxidative phosphorylation. Some markers such as Calcitonin receptor (Calcr), Teneurin4 (Tenm4), and stress pathways identified previously were also present in our analysis  [11, 41, 55]  (Additional file 6: Table  S2 ).
However, we also report that virtually all datasets contained genes that would be expected to be present during activation or cell cycle entry, such as members of the Fos and Jun family previously identified as immediate early stress response genes  [56]. Using a novel isolation protocol based on the notion that tissues that are fixed prior to processing result in stabilized mRNA  [18, 19] , we validated the expression of several genes including Calcr and Teneurin4 (Tenm4) as true quiescent markers. In contrast, we show that Fos and Jun transcripts and Jun family proteins are not present at significant levels in vivo, but are robustly induced within 5 h, the average processing time taken for isolation by FACS of satellite cells. These results are concordant with a recently published paper in which immediate early and heat-shock genes were rapidly up-regulated during the cell isolation procedure  [57].
We propose that these and other stress response genes mitigate the quiescent to activation transition that accompanies the initial steps of exit from G0. Given these unexpected findings, the comparison of transcriptomes of satellite cells from a fixed/in vivo state with those that were described here would be important to delineate homeostatic vs. immediate early response genes. For that purpose, Sherpa allows the integration of datasets from fixed samples, or other methodologies, when they will be available. Beyond the present findings, we propose that all transcriptome data obtained from cells isolated from solid tissues, which require extensive enzymatic digestion and processing before isolation of RNA, need to be re-evaluated to distinguish those genes that are induced by the isolation procedure.
In addition to generating this open access compendium of GEPs, we provide a standardized pipeline that sets the basis for a multi-set analysis for an effective and systematic comparison of individual datasets. Analyzing multiple datasets provides generalized information across different studies  [38, 39]. The cancer field was a pioneer in combining several works  [58, 59]  and other fields, such as neurodegenerative diseases  [60, 61]  and regulatory genomics have successfully adopted this strategy  [62]. The multi-dimensional approach presented here offers increased power, due to the higher sample size and increased robustness, by highlighting variations in individual studies results  [37, 63]. Such variations are the consequence of the high level of noise and artefacts and are typically associated with microarray data  [64].
Conclusions Here, we compile the first comprehensive catalog of gene expression data of myogenic cells across distinct states and conditions, providing a global perspective on quiescence. An extensive comparison of the transcriptomic profiles of mouse skeletal muscle satellite cells in quiescent and activated states resulting from nine datasets revealed common features among the different studies from other features which are more specific to the individual datasets. In spite of heterogeneities across platforms, we were able to identify genes that were consistently up-and down-regulated among the different datasets. By doing so, we developed and made available an open-access interactive exploratory tool called Sherpa (SHiny ExploRation tool for transcriPtomic Analysis) that allows statistically valid analyses and systematic comparisons that cannot be performed directly on the datasets.
Acknowledgements We would like to thank  K. Soni  and  U. Borello  for their assistance in the early stages of this work and  P. Mourikis  and  F. Relaix  for communicating unpublished results and sharing protocols. We also acknowledge the  Flow Cytometry Platform of the Technology Core-Center for Translational Science (CRT) at Institut Pasteur  for the support in conducting this study and the microarray platform at  Institut Cochin.
2%) FACS: FACS: integrin- syndecan-3 alpha7+/Lin- /CD31-/CD45- /CD11b-/Sca1- 12 h or 48 h 72 h (3 d) postinjury Affymetrix Agilent 028005 430_2. 0 SurePrint G3Mouse 8x60k Microarray GSE47177 Liu et GSE3483 GSE15155 al. [9] Fukada et al. [10] Pallafacchina et al. [11] 3 QSC, 3 QSC, 3 ASC 3 QSC, 3 ASC 3 ASC 60 h, 3 ASC 84 h 2013 2007 2010 Hindlimb Hindlimb Diaphragm, pectoralis, abdominal muscles M F M, F 8 w 8-12 w 6 w C57BL/6 (Jackson) C57BL/7 (nihon clea) Pax7 CreER/+ Pax3 GFP/+ (high GFP) R26 eYFP/+ BaCl 2 QSCs in culture Pax3 GFP/+ , Pax3 GFP/ + for 4 d :mdx:mdx Adult; adult mdx; 1 w old; 3 d in culture FACS: Pax7 CreER/+ ; FACS: CD45-Reporter R26 eYFP/+ /SM/C-2. 6+ 36 h (1. 5 d), 60 h 4 d in culture 3 d in culture (2. 5 d), 84 h (3. 5 d) postinjury Affymetrix Mouse Affymetrix 430A Affymetrix 430_2.
Given the importance of meiotic recombination in biology, there is a need to develop robust methods to estimate meiotic recombination rates. A popular approach, called the Marey map approach, relies on comparing genetic and physical maps of a chromosome to estimate local recombination rates. In the past, we have implemented this approach in an R package called MareyMap, which includes many functionalities useful to get reliable recombination rate estimates in a semi-automated way. MareyMap has been used repeatedly in studies looking at the effect of recombination on genome evolution. Here, we propose a simpler user-friendly web service version of MareyMap, called MareyMap Online, which allows a user to get recombination rates from her/his own data or from a publicly available database that we offer in a few clicks.
GBE Genome Biol. Evol. 2506-2509 doi:10. 1093/gbe/evx178 Advance Access publication September 6, 2017 et al. 2004) for example. In these tools, the users cannot work on their own data. The only tool for inferring local recombination rates in any organisms in a semiautomated way is a tool called MareyMap that we developed a few years ago  (Rezvoy et al. 2007 ) and have updated recently  (Siberchicot et al. 2015). MareyMap requires the user to have files for physical and genetic maps for each chromosome in a given species. The files are then uploaded and a number of functionalities allow the user to build a Marey map plot, to clean aberrant points from the map, to infer local estimates using different methods and to export estimates in different formats  (Rezvoy et al. 2007). MareyMap is an R package with a tcl/tk graphical interface, which works well but requires the user to have R (R Core Team 2016) installed and to be familiar with installing R packages.
Despite R being free and open-source, we have noticed that a significant number of users have experienced difficulties in installing the MareyMap R package when they are not frequent R users. Moreover, the many options proposed by MareyMap are useful for experienced users but can be confusing for beginners with the package and the Marey map approach. Here, we describe a web service called MareyMap Online that we have developed as a user-friendly version of MareyMap. MareyMap Online proposes a simplified version of MareyMap with five main steps: data uploading, data cleaning, recombination rate estimation, export and data sharing (see menu bar in fig. 1 ), which are described below. The user is guided through these steps with only essential options proposed and clear guidelines when there are choices to make.
An important feature offered by MareyMap Online is to be able to pick some Marey map data from a publicly accessible database that we provide. This database currently includes 8 organisms: Arabidopsis thaliana, Caenorhabditis elegans, Danio rerio (zebrafish), Drosophila melanogaster, Homo sapiens, Oryza sativa (rice), Oryzias latipes (medaka), and Saccharomyces cerevisiae. MareyMap Online will ask each user the permission to keep her/his curated Marey map data once the analysis is done, which will then be stored in our database. We thus expect the database to include many more species in the future, which can be useful for meta-analysis on recombination and genome evolution for instance. Such studies are currently difficult as this kind of database does not exist and one needs to collect the Marey map data through literature search.
MareyMap Online has been implemented from the MareyMap R package with shiny, an R package which offers a framework to develop web applications in R  (Chang et al. 2016 ). As explained above, the user follows a pipeline step by step and is asked first to pick a Marey map dataset from the database or to upload her/his own dataset. The format required is quite simple and an example is provided. In step 2, a plot comparing physical (X axis) and genetic (Y axis) map can be seen for each chromosome (fig. 1 ). Maps often contain errors. These can be easily spotted on a Marey map as they will clearly disrupt the expected overall monotonously increasing relationship between physical and genetic positions. Such erroneous datapoints need to be removed before further analysis and an interactive cleaning tool is proposed to the user in order to do it.
At step 4, the user will view the estimates and automatically generated warnings will tell her/him when negative values are present among the estimates. Back-and-forth adjustments between steps 3 and 4 may be needed to pick the estimation method most adapted to the user data. Then, export options are proposed. A useful one is the possibility to upload a list of physical positions and retrieve the estimates for all these positions, which is very useful for a downstream analysis of all the genes in a genome for example. The user can also query some positions to get estimates. At step 5, the user will be asked whether she/he is willing to authorise storage of her/his curated Marey map data in our public database. In conclusion, MareyMap Online provides a simplified userfriendly pipeline for getting local recombination rate estimates.
Methods: The criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement. Results: A total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.
A face mask (Hans Rudolf) held in place by a nylon harness covered the participants' nose and mouth. The mask was attached to a bidirectional digital turbine flow meter to measure the volumes of inspired and expired air. Heart rate data were captured using the FT1, a chest-worn sensor (Polar Electro Oy). Once instrumented, participants remained seated for 15 min while physiological data were recorded; the last 5 min of data were averaged to calculate resting EE. Participants then completed four discrete bouts of walking and running on a motorized treadmill (Quasar Med, H/P Cosmos Sports & Medical GmbH). Participants completed 5-min bouts of exercise at light (4 km/h and 6 km/h), moderate (10 km/h), and vigorous intensity levels (≥12 km/h), separated by 3-min bouts of passive recovery. Following laboratory-based activities, participants were instructed to wear the ActiGraph accelerometer (as described above) and carry the smartphone (as they would normally carry their personal phone) for 24 hours during free-living daily activities.
Data Handling Heart rate, calorimeter, and ActiGraph data were downloaded using the manufacturers' software; smartphone data were downloaded using a text reader, exported for manual analysis, and synchronized in postprocessing. Mean values were calculated during the last 2 min of each laboratory-based activity bout, following similar procedures in phase 1 and 2. Free-living accelerometer data were cleaned by removing nonwear time, equivalent to >60 min of continuous zero counts. A minimum of 10 hours of available free-living data were required for the analysis. Data were processed to generate comparable units of EE for analysis  [17].
Statistical Analysis Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 21 for Windows (IBM Corp). To derive estimates of EE from Movn activity counts, multivariate regression was conducted following established methods  [19]  to identify the strongest relationship between Movn activity counts, participant characteristics, and EE measured via indirect calorimetry. Estimates of EE were then used to identify Movn activity count thresholds associated with accepted classifications for light (<3 metabolic equivalent of task [MET]), moderate (3-6 MET), hard (6-9 MET), and very hard (>9 MET) activity intensity levels  [19]. The regression equation was also applied to phase 2 laboratory-based data to cross-validate the accuracy of EE estimation among an independent sample using different smartphone hardware.
Furthermore, individual-level biases were calculated to determine whether group-level agreement was consistent across the sample. To determine the feasibility of using smartphone sensor data to ascertain the geographic location of activity and patterns of human movement, accelerometer and GPS data were combined to provide an indication of the location and intensity of PA. Data were imported into ArcGIS version 10. 2. 2 (Esri) transformed to location points and interpolated into two-dimensional (2D) spatial paths and three-dimensional (3D) spatiotemporal trajectories. Descriptive data are reported as mean (standard deviation); bias data are reported as mean and 95% CI; alpha=. 05 for all hypothesis tests.
Convergent Validity Sensor wear time compliance was lower than anticipated during phase 1 free-living activities. After removing nonwear time, only 8 participants met minimum data requirements. Participants recorded an average of 766 (SD 189) min of activity on valid days, yielding a total free-living convergent reference sample of 6124 min. Table  4  summarizes EE during phase 1 free-living activities, assessed with Movn and ActiGraph devices. Compared with the convergent reference ActiGraph, Movn overestimated EE during free-living activities (Table  4 ); the mean bias was larger than during laboratory-based activity and represented a systematic bias between devices (t 6123 =101. 49, P<. 001). Consistent with laboratory-based activities, a statistically significant effect of activity intensity level was also detected on free-living measurement biases (F 3,6120 =1550.