dataesr/scientific-paragraphs-categorization huggingface dataset:
The five clusters are represented by five CPUs and the channels between AVATAR tasks are implemented as software channels mapped to on-chip RAM. Some properties pertaining to mapping must be explicitly captured in the Deployment Diagram, such as CPUs, memories and their parameters, while others, such as simulation infrastructure and interrupt management, are added transparently to the top cell during the transformation to SoCLib. Figure  4  shows the Deployment Diagram of the software components of the active braking application mapped on five processors and five memory elements. From the Deployment Diagram, a SoCLib prototype is then generated. This prototype consists of a SystemC top cell, the embedded software in the form of POSIX threads compiled for the target processors, and the embedded operating system (Figure  9 ).
Capturing Performance Information We now present how performance information can be obtained from the use case simulated with SoCLib. In the experiments shown here, we use PowerPC cores. The cycle accurate bit accurate (CABA)-level simulation allows measurement of cache miss rates, la- tency of any transaction on the interconnect, taking/releasing of locks, etc. Since SoCLib hardware models are much more precise than the ones used at the design space exploration level, precise timing and hardware mechanisms can be evaluated. However, these evaluations take considerable time compared to high-level simulation/evaluation. We restrict ourselves to using only the hardware counters available in the SoCLib cache module. We start by an overview of performance problems. For this, we use an overall metric summing up all phenomena that slow down execution of instructions by the processor, such as memory access latency, interconnect contention, overhead due to context switching etc.
During vertebrate neurulation, the embryonic ectoderm is patterned into lineage progenitors for neural plate, neural crest, placodes and epidermis. Here, we use Xenopus laevis embryos to analyze the spatial and temporal transcriptome of distinct ectodermal domains in the course of neurulation, during the establishment of cell lineages. In order to define the transcriptome of small groups of cells from a single germ layer and to retain spatial information, dorsal and ventral ectoderm was subdivided along the anterior-posterior and mediallateral axes by microdissections. Principal component analysis on the transcriptomes of these ectoderm fragments primarily identifies embryonic axes and temporal dynamics. This provides a genetic code to define positional information of any ectoderm sample along the anterior-posterior and dorsal-ventral axes directly from its transcriptome.
In parallel, we use nonnegative matrix factorization to predict enhanced gene expression maps onto early and mid-neurula embryos, and specific signatures for each ectoderm area. The clustering of spatial and temporal datasets allowed detection of multiple biologically relevant groups (e. g. , Wnt signaling, neural crest development, sensory placode specification, ciliogenesis, germ layer specification). We provide an interactive network interface, EctoMap, for exploring synexpression relationships among genes expressed in the neurula, and suggest several strategies to use this comprehensive dataset to address questions in developmental biology as well as stem cell or cancer research.
EctoMap is a molecular atlas defining the regional code of ectoderm development and a searchable interface to compare gene expression levels, synexpression groups, and relationships between genes. This tool will be broadly useful in embryos and evo-devo analyses, for example, to analyze and compare gene regulatory networks (GRNs) in the evolution of NC and placodes or to study gene subfunctionalization after genome duplication, as in the X. laevis genome. EctoMap and the tools provided here constitute a spatial reference for single cell transcriptome analysis and for comparing gene expression in embryos with other contexts, such as cancer cells, stem cells, or in congenital diseases.
, Nieuwkoop and Faber stage; tSNE, t-distributed stochastic neighbor embedding; WE, whole embryo; WGCNA, weighted gene correlation network analysis; WNT, Wingless-Int. rhombencephalon and spinal cord. The ANF, corresponding to telencephalon, was dissected together with PPE. Moreover, as the NB subdivisions are better characterized later on, when premigratory NC and placodes are segregated and fully specified  [24, 25] , we also dissected the premigratory NC and the adjacent lateral preplacodal domain in late neurula St. 17, according to published protocols  [26, 27]. These additional dissected regions allowed us to link gene expression in progenitor cells from St. 14 early neurula to the transcriptome of specified St. 17 NC and placodes. To incorporate further temporal information into our dataset, we also included WEs collected during late gastrulation and throughout neurulation: St.
11-11. 5, 12, 12. 5, 13, 14-15,  [16] [17] [18] [19] S1 Table). More than 300 dissected samples were quality checked by quantitative PCR analysis (qPCR) using diagnostic markers for NP (sox2), NNE (epidermal keratin, xk81a1), NB (pax3), NC (snai2), and paraxial mesoderm (myod1) (S2 Table ). Despite the utmost care in dissections, a few attached mesoderm cells occasionally contaminated the posterior ectoderm samples; samples with the lowest myod1 expression were selected. After this quality check, whenever possible, adjacent ectoderm pieces, i. e. , dissected out from a single embryo, were chosen (details available in Gene Expression Omnibus [GEO] data repository, GSE103240). Finally, we selected samples with a yield above 40 ng of total RNA. According to standards in the field, 3 biological replicates were collected per dissected region, at St.
12. 5 and St. 14. Each replicate originated from different embryos and different clutches to buffer interindividual variations. For NB ectoderm regions, which are our main research focus of interest, we were able to add a few more replicates, which would increase statistical power of the analyses in this region (S1 Table ). For premigratory NC and PPE from the late neurula, between 2 and 4 biological replicates were dissected at St. 17. A total of 79 samples were finally used for polyadenylated RNA selection and cDNA library preparation. Each was then subjected to a classical pipeline of downstream data analysis using paired-end sequencing on a HiSeq 2000 (Illumina) (Fig 1D The RNA sequencing from polyadenylated transcripts yielded at least 14 million reads per sample. About 88% of the reads mapped to unique loci in the X.
laevis genome assembly (v9. 1)  [16]. In addition, we predicted novel transcripts, which were included in Gene Model Annotation 1. 8. 3 of the X. laevis 9. 1 Genome Build (http://www. xenbase. org/entry/doNewsRead. do? id=223). In the following sections, we focus on spatial and temporal analyses at early and midneurula stages. Then, we describe how we use the whole dataset (tissue dissections and time series) to build a gene co-expression network. Finally, we present EctoMap, a user-friendly interactive tool that aims to provide insight on the study of temporal and spatial relationships of genes using X. laevis neurula as a prototypical vertebrate model. violet), and nonneural ectoderm (NNE, green, yellow-brown). NP, NB (at St. 12. 5 and St. 14), and NNE (at St. 14 only) were further subdivided into 2 or 3 pieces: anterior (a, darker color) and posterior (p, lighter color) fragments, as indicated.
For St. 12. 5 NB only, a more lateral fragment was cut (l, beige), (lateral refers to medial-lateral coordinates on the dorsal view or corresponds to more ventral if using dorsal-ventral whole embryo coordinates). Each sample is associated to 1 color throughout this study (color code reference in S1 Table ). (B) This dissection pattern (dotted lines) matches with reference genes expression patterns in the developing neurula ectoderm as indicated for pax3 (dorsal-lateral NP and NB), sox2 (NP and St. 14 PPE), and snail2 (premigratory neural crest within the NB). The dissection line is indicated outside the embryo whenever the dissection encompasses tissues located more ventrally than seen in dorsal view (e. g. , for PPE and NNE). Similarly, the blastopore (white circle) can be indicated shifted outside the embryo if located more ventrally.
14, see Materials and methods). This list of genes was used in the subsequent unsupervised approaches (PCA, tSNE, and in expression pattern predictions calculated using a nonnegative matrix factorization [NMF] algorithm, see below). All the transcripts excluded from the initial analyses were reintroduced later in the study, including in NMF analysis, to document their spatial distribution and time course. PCA and tSNE capture the major differences in gene expression between samples, with the aim to describe a complex dataset (consisting of a large number of samples, each defined by thousands of gene expression values) and represent this dataset in a space with fewer dimensions. While these algorithms are often used merely to check that replicate samples group together and that experimental conditions (here, dissected regions) are well separated, the mathematical definition of PCA potentially allows exploring further the structure of the dataset.
In the case of PCA, dimensions are defined to capture the highest amount of variation (percentage of the variance) from the high-dimensional gene expression space. By identifying the biological meaning of each dimension (called "component"), one could get insight into the most important factors influencing gene expression in the dataset. We first analyzed each stage separately, using data from genes selected at the corresponding stage (Fig  2 ). For PCA, samples were plotted along the first 2 components and in 2 dimensions for tSNE (Fig 2A  , 2B , 2C and 2D). With either method, we observed a good separation of most samples according to the dissected regions and adequate grouping of the samples representing replicates for the same dissected region (Fig  2 ). To further visualize the outcome of the PCA/ tSNE plots, we used Voronoi diagrams.
In this case, tSNE would be preferable to PCA. From simple usage of unsupervised dimension reduction algorithms, the first conclusion was that the replicate samples grouped as expected using thousands of genes with high variation in expression. This meant that for each neurula stage, ectoderm dissected regions could be recognized based on a large part of their transcriptome. Moreover, exploiting PCA mathematical properties, we could retrieve interesting biological information from the structure of the dataset, as explained below. This deeper analysis was possible in the case of our experimental design, because a few PCA dimensions (named At both stages, St. 12. 5 and St. 14, the first PCA components, which capture the highest percentage of variance, were identified as significant at the 1% level: components 1 to 3 at St.
Most correlated genes maintained a pattern restricted along D-V or A-P axes at mid-neurula stage (S3 Fig,  S4 Table). We concluded that the expression between parentheses. Sample color code is identical to Fig 1A. Lighter background colors are Voronoi diagram areas. These areas are drawn using the barycenter of samples belonging to the same dissected region. Each area indicates the regions of the PCA plane, where all points are closer to the barycenter of this dissected region than to any other barycenter. Arrows indicate anterior neural border samples falling slightly outside their intended dissected region (see text for details). (C, D) At St. 12. 5 (C) and St. 14 (D), t-distributed stochastic neighbor embedding (tSNE) groups samples according to dissected regions. (E, F) At St. 12. 5 (E) and St.
For example, one could hypothesize that the NB transcriptome could either be unique compared to the other ectoderm regions or could result from a combination of NP transcriptome and NNE transcriptome. The algorithm of choice should identify how many prototypical transcriptomes could be defined from the dataset and reconstruct how the actual transcriptome of each dissected region corresponds to a combination of the prototypical transcriptomes. The NMF algorithm reconstructs a sample expression matrix (our complex transcriptome dataset) as a product of 2 matrices: the prototypical tissue expression matrix, which describes gene expression in the reconstructed tissues (named below as "NMF-tissue" or "NMF-predicted"), and the mixing matrix, which describes how NMF reconstructs dissected regions from NMF-tissue with the constraint that the transcriptome expression and the proportions matrices are nonnegative.
When running the algorithm, one looks for the number of prototypical tissues that produce a robust solution (convergence on a single solution from multiple random initializations, see Materials and methods). When applied to our dataset, the NMF algorithm robustly recovered 5 prototypical NMF-tissues at each neurula stage ( The hypothesis of 5 prototypical NMF-tissues results in the mixing matrix shown in Fig 3A  and 3B. The embryo map shows how these 5 prototypical tissues relate to our 7 dissected ectoderm regions (Fig 3A  and 3B ). First, the main contributions were considered in the mixing matrix (indicated in red). At St. 12. 5, 3 of the 5 domains identified by NMF matched directly with the dissected region pattern: the ANB/PPE (violet), the NPa (navy blue), and the ventral NNE (green) (Fig  3A ).
This signature consisted of the genes with the highest Gini index (0. 8 for our dataset) or close to the maximum (0. 7-0. 8 on a scale of 0 to 0. 8), as listed in S5 Table. At early neurula stage, this is the first genomic annotation of specific ectoderm spatial domains. In sum, we have described a genome-scale optimized pattern prediction using NMF, accompanied by an ectodermal enrichment score. Using the Gini index, we have defined the first tissue-specific gene signature for each ectoderm region. In order to represent gene expression levels on the map of the developing ectoderm, we used 2 approaches: averaging the expression values for each of 7 domains or using NMF deconvolution predictions in 5 domains. We found those 2 approaches complementary. Average patterns directly describe raw data but are sensitive to shifts in dissection.
Clustering of spatial and temporal expression data identified synexpression groups associated to important cell and developmental biology processes In eukaryotes, genes involved in related biological processes are often co-regulated and share similar regulatory inputs that coordinate their expression, temporally and spatially  [43, 44]. To gain insights into the temporal and spatial dynamics of the genes expressed at neurulation, we used weighted gene co-expression network analysis (WGCNA), a data-mining method widely used in biology to identify groups that share similar expression profiles from large gene expression datasets  [45]. For this part of analysis, we used 10,073 transcripts, expressed above a background threshold level (see Materials and methods), using the whole dataset, i. e.
, 79 samples, including the dissected samples at 3 neurula stages (St. 12. 5, St. 14, and St. 17 In order to identify biological functions associated to each WGCNA generated gene module, we used an unsupervised functional enrichment test using gene ontology (GO). We found that 64/141 groups were significantly enriched for important biological functions (see Materials and methods, S6A and S6B Table ). We next tested if this enrichment was significant over a randomized result. Overall, these overrepresented functions were more significant and more sensitive than random datasets (Fig 5B  and 5C ). We challenged the biological significance of WGCNA synexpression groups by exploring the distribution of 2 distinct sets of genes across the WGCNA modules: genes related to canonical Wnt signaling and genes involved in NC and placode GRNs.
Wnt signaling is essential in embryonic development and cancer  [46, 47]. In development, canonical Wnt signaling acts during gastrulation and neurulation to induce posterior ectoderm structures and NC  [48]. To examine how WGCNA would cluster genes implicated in Wnt signaling, we retrieved a list of 97 X. laevis transcripts responsive to Wnt signaling inhibition in gastrulas (S7B Table)  [49]. From this initial set, 67 transcripts (70%) were expressed in our neurula-stage dataset above the minimal threshold. We found that half of these Wntresponding transcripts  (33/67)    In particular, G-23 contained 62% of genes previously associated with Wnt signaling in cell cycle, cancer, or patterning including many transcription factors expressed posteriorly and known to directly respond to canonical Wnt signaling and participate in posterior embryo patterning, such as meis3, cdx2/4, hes5, hoxa1, and znf703 (S7A-B  [49, 50] ).
These observations and the GO functions associated to this group (S6A and S7 Tables) suggest that G-23 gathered known and novel genes responding positively to canonical Wnt signaling and involved in posterior patterning or skeletal morphogenesis. G-31 contained genes expressed in the endoderm and mesoderm, which directly and positively respond to Wnt signaling (msgn1, myf5, t, vegT)  [49]. G-41 did not associate to a particular GO term but contained genes encoding putative components of Wnt signaling pathway (fdz10, tcf7, xarp). Finally, the transcripts negatively regulated by Wnt signaling were significantly enriched in G-92 (no GO association) (Fig  6A). This first example showed that WGCNA synexpression grouping is biologically meaningful: it groups together, based on their spatial and temporal synexpression, genes with related biological functions.
This could prompt further research on the novel genes clustered in G-23 with  ). We next investigated the efficiency of WGCNA clustering on a list gathering genes expressed in progenitors of neural tube, NC-GRN, and placodes (PPE-GRN) (S7C Table,  [10, 24, 48, [51] [52] [53] [54] ]. The regional and temporal segregation of these progenitors during neurulation should be retrieved in our dissections as shown by PCA or NMF analyses. In total, we and cilium morphogenesis in green (#22, green). Refer to S6 and S6B Table for the full list of functions associated with each group. (B) Comparison between groups generated by WGCNA (blue) and those created by random assignment of transcripts (red). About 45% of the WGCNA groups associate to at least 1 biological function, which is significantly more than the average obtained for the random dataset groups (4%; the 5 random datasets of 141 groups).
The distribution of adjusted p-values and sensitivities (1-β) of overrepresented terms show that, overall, WGCNA groups are more likely to contain a relevant biological function than groups assigned randomly. (C) Specificity versus sensitivity plot, in which each of the data points represents a single GO function enriched in a group. Black circles represent all 80 biological functions enriched in the 5 random datasets (total of 705 groups). The Wnt responding genes (Groups #23, blue), neural crest (#51, purple), forebrain (#38, orange) are highlighted. For further description of the function, refer to S6B  The histogram shows how many transcripts were found in each synexpression group and if this represented a significant enrichment compared to uniform distribution in all groups; see S11 Table for numerical data.
). G-38 contained 43% of input placodal transcripts and other genes required for forebrain and anterior development (fzd, lhx2, rax, fezf2, S4 Table)  [55] [56] [57] [58]. This grouping was thus similar to NMF deconvolution for ANF/PPE and matched with forebrain and placodes development. G-51 gathered 70% of input NC genes (Fig 6B, S7 and S10 Tables). We then looked for the spatial expression of G-51 genes not present in the input list. Published in situ hybridization experiments confirmed that 87% of the G-51 annotated transcripts were expressed in the pre-migratory NC (S7D Table ). Moreover, the NMF analysis predicted that the remaining uncharacterized G-51 genes were expressed in NC progenitors at St. Finally, we cloned a PCR fragment of sh3pxd2a, one of the uncharacterized genes in G-51, and confirmed its NC expression in neurula-stage WEs, as predicted by both WGCNA and NMF (Fig  7 ).
Interestingly, this gene was expressed at very low levels (mean of 20 RPKM in NBa, compared to 1,000 RPKM for snail2 in the same tissue, Figs  4  and 7 ); it would probably not have been picked up by an approach based on in situ hybridization in vivo, showing how our large-scale analysis followed by pattern predictions could help in identifying novel genes of interest. Also, in this case, due to low expression levels, the average pattern was much less clear cut than the NMF-deconvoluted pattern (Fig  7). All together, these results show how a list of genes, taken either from a broadly used pathway (Wnt) or merged from diverse pathways and GRNs (neural, NC, placodes, mesoderm patterning, and various signaling), was distributed across the WGCNA groups. As a whole, this grouping displayed biologically relevant co-expression groups from the temporal and spatial profiles extracted from our neurula dataset.
A gene co-expression network enhanced clustering and highlighted potential functional relationships between clusters In an effort to expand and customize the previous analysis, we implemented a web interface to query and visualize the gene co-expression network from a user-defined list of seed transcripts. This retrieved the co-expression relationships between the genes of interest (seed) and all transcripts in the dataset that shared strong correlations (positive or negative). In particular, this would allow visualizing the relationships between genes from different co-expression groups. To create the network, the user simply specifies the list of gene names and a minimal p-value cutoff that will be used to determine the number of transcripts (nodes) connected with each of the inputs. The large nodes in the network represented each of the input transcripts.
The lines (edges) connected 2 correlated transcripts: a thick line for a negative correlation and thin line for a positive correlation. The color of each node corresponded to the gene co-expression group determined by WGCNA in the previous section. With such network, one could explore the hierarchical temporal and spatial relationships between the factors involved in neurula ectodermal patterning, as exemplified below. We challenged the gene co-expression network by entering a large list of genes, mixing several distinct biological processes, and testing how they would be subgrouped by unsupervised clustering: we included 54 genes (large nodes) associated with various signaling pathways (bone morphogenetic protein [BMP], Wingless-Int [WNT], and fibroblast growth factors [FGF] pathways), neural patterning, placode GRN, NC-GRN, mesoderm patterning, and endoderm formation (S10 Table ).
Comparing human cancer-related genes to developmental gene coexpression facilitates data mining of large-scale datasets Finally, we evaluated whether this neurula embryo dataset, with its accompanying data-mining tools, could shed interesting light on other types of genome-scale approaches, either in cell biology or human pathologies. Genome-wide association studies (GWASs) aim to identify genes correlated with a specific phenotype or pathology, with the goal of identifying diagnosis markers  [64]. One of the biggest challenges of a GWAS is to identify the genes primarily driving the pathology, among thousands of altered genes, and to understand how these contribute to disease development. We propose that the GWAS could be coupled with the developmental co-expression networks to get a better understanding of the etiology of complex traits.