dataesr/scientific-paragraphs-categorization huggingface dataset:
t-SNE method with perplexity = 50 was used to make 2d embedding. Differential gene expression between molar and distal incisor mesenchymal cells was estimated as fold change between cluster-specific expression levels, estimated as sum of gene reads to total reads in a cluster. Significance of expression changes was estimated as p value of t-test comparing group means between normalized expression levels of molar and distal incisor clusters. Joint analysis of human datasets. Two growing apical papilla and five adult molar 10× Chromium datasets of human teeth, comprising totally 41673 cells, were analysed using single-cell variational inference (scVI) deep learning framework  54. Gene space was subsampled to 3000 genes using scVI subsample_genes routine following by setting up parameters of variational autoencoder using VAE() routine with parameters (n_hidden = 128, n_latent = 30, n_layers = 2 and dispersion = 'gene') and training it using UnsupervisedTrainer() routine with n_epochs = 150.
Batch effect correction was by default performed using harmonization approach  55. The resulting 30-dimensional reduced scVI space was used to make 2d embedding of the datasets using UMAP() routine with parameter spread = 1. K-nearest neighbour cell graph (k = 30) was constructed using cosinebased cell-cell distances estimated in scVI space and then used to make leiden clustering (resolution = 1). To explore behaviour of apical and distal mouse-incisor gene modules across human mesenchyme populations, we chose apical-specific and distal-specific sets of genes and assessed their averaged expression in human mesenchyme clusters. In details, apical and distal-specific genes were defined as those having at least threefold change between apical and distal incisor states and p value < 10 -10 in both 10 Chromium and Smart-seq2 mouse-incisor datasets.
Acknowledgements I. A. was supported by an  ERC   consolidator grant  ( STEMMING-FROM-NERVE ,  647844 ),  EMBO young investigator program ,  Bertil Hallsten Research Foundation , Vetenskapsradet Project Grant and  Ake Wiberg Foundation. J. K. was supported by the  Grant Agency of Masaryk University , project  MUNI/H/1615/201  and by funds from the  Faculty of Medicine MU  to junior researcher. T. H. was supported by an  ERC  consolidator grant ( 2015-AdG-695136 ). P. M. and O. D. K. were supported by  NIH/NIDCR   R35-DE026602. P. V. K. and R. A. S. were supported by  NIH   R01HL131768. We acknowledge the core facility CELLIM of  CEITEC  supported by the  Czech-BioImaging large   RI  project ( LM2018129  funded by  MEYS CR)  for their support with obtaining scientific data presented in this paper.
- 30 Fig. 5 305 Fig. 5 Developmental dynamics of dental mesenchyme. a Analysis of mouse-incisor dental mesenchymal cells isolated for separate analysis from general dataset. Colors show unbiased clusters. The principal tree correctly captures positions of mature mesenchymal derivative and progenitor populations. b Analysis of RNA velocity shows major directions of cell progression in the transcriptional space. The arrow start-and endpoints indicate current and predicted future cell states. c Model of stem-cell dynamics in mesenchymal compartment with relation to dental follicle. d Prediction and validation of spatially restricted Foxd1 + stem cells. The Foxd1-associated axis was selected for validation, and is shown on t-SNE (genes with the strongest positive and negative associations are shown in red and blue respectively).
Hence the generative model must be able to use the information contained in the knock-out gene signature to generate inhibitor-like molecules. For this, we trained the conditional stacked GAN using all 31,821 compound-induced gene expression profiles and their corresponding compound structures. Then, we generated 1000 molecular representations for each of 148 gene expression signatures induced by the knock-outs of ten protein targets of pharmaceutical interest. As before, there is more than one gene expression signature for each knock-out protein target due to the use of different single guide RNA (sgRNA) for CRISPR knock-outing  25. All generated molecular representations were filtered to keep only those corresponding to valid and synthesizable molecules. The similarity between the resulting molecules and their nearest neighbor inhibitor contained in the ExCAPE database  48  was evaluated for each target.
4)  50. It is worth mentioning that 24% of the generated molecules presented a MACCS similarity above 0. 7 (but only ~1% above 0. 8) to a known inhibitor. Figure  3b  shows examples of generated molecules and their closest known active molecules for each of the ten targets. It is surprising to see that in many cases the generated molecule shares functional groups and even a similar molecular scaffold with the active molecule. As seen from these examples, the knock-out gene expression signature of the target was able to direct the molecular generation to specific areas of the chemical space associated with active molecules. We performed the scaffold analysis to evaluate the potential of the model to generate molecules with known active scaffolds. Only a few scaffolds from the generated molecules (0-14 scaffolds or 5-54 generic scaffolds depending on the target) were also present in active molecules from the ExCAPE database  48  (Supplementary Table  1 ).
Nevertheless, a high percentage of these (>55% for scaffolds and >65% for generic scaffolds) were not part of the training compounds that are known to be active for these specific targets based on information from the Drug Repurposing Hub  51. In this sense, the model is doing what it is meant to do: connecting chemistry and biology through gene expression without the need of previous activity labels. Optimizing scaffolds towards a gene expression signature. Although that stage II of the stack GAN was designed to refine results from Stage I, it can also be used to refine any other molecule or scaffold. As a proof of concept, we evaluated if the Stage II of our approach was able to optimize the benzene ring (the most common scaffold in the dataset) towards active-like compounds for different targets.
For this, we encoded the SMILES of the benzene ring into a latent space representation using the encoder of the SMILES-to-grammar model which then was fed into the Stage II generator (G 1 ) together with the desired gene expression signature (Fig. 4a ). As a result, the model generated an optimized molecule for every scaffold-gene expression signature combination. We repeated this procedure for each of the 148 gene expression signatures corresponding to the ten protein targets previously mentioned. Figure  4b  shows some examples of the molecules optimized toward a specific target and their closest active nearest neighbor in the ExCAPE database (not used in the training data). Interestingly, 46% of the resulting molecules kept a benzene ring with the appropriate side chains added by the generative model.
Then, we evaluated the maximum structural similarity between each of the 148 selected molecules and a set of >1000 active molecules for the specific target extracted from the ExCAPE database  48. At the same time, 1000 molecular representations were produced with the generative model for each of the 148 target knock-out gene expression signatures. After decoding the molecular representations into SMILES and filtering them by validity and synthetic accessibility we chose the most structurally similar generated molecule to one of the >1000 active molecules. Figure  5a  shows the distribution of structural similarity between generated molecules or compounds selected from the training set using similarity search (Euclidean or cosine distance) and their closest active molecule in ExCAPE database  48.
4 million compounds from ChEMBL database  55  ) and produced a higher number of valid molecules (97. 7%), most of them had a low classification score (median value below 0. 61) for each of the ten knock-out gene expression signatures (Fig. 5b  and Supplementary Table  2 ). This is not surprising since non-conditioned generative models, like this LSTM network, are trained to produce new data that present a distribution similar to that of the training set. This also explains the wide range of classification scores, where some of them could be higher than 0. 8 due to the presence of inhibitors for each of the ten targets in the training set. Similar results were observed when molecules ware generated using a non-conditioned GAN and the L1000 dataset. In contrast, molecular representations obtained from the conditioned GAN showed significantly higher classification scores than the non-conditioned LSTM network for all target knock-outs (p-value < 0.
Discussion In conclusion, we reported a method based on conditional generative adversarial networks that proposes new molecules from a particular gene expression signature. Our method offers some advantages over current molecular generative approaches as well as an alternative way to exploit all the information contained in compound-induced gene expression data, in particular the one in L1000 database. Firstly, this method allows the generation of active-like molecules using just the gene expression signature of the target knock-out. Since no previous measurement of biological activity or target annotations are needed for the training data, this approach could potentially be applied to any target, as we have shown herein. Further work is still needed to assess the optimal biological models to generate the gene expression signatures, especially in the light of the variability of drug responses in cell lines as recently reported  56.
Secondly, we can use the model to modify a chemical scaffold (or other molecule) in order to generate active-like molecules, which is particularly useful for scaffold hopping or compound optimization. Finally, we showed that our method generates molecules more similar to known active compounds than the ones that can be found by a similarity search, which is currently the state-of-the-art method to navigate the L1000 data. The fact that our method does not rely on target annotations or activity data makes it very useful in cases where such information is not available such as in target deorphanization projects. It must be said that there is still room for improve this method, for example evaluating if it can be applied lead optimization or finding ways to generate compounds with known structural features associated with activity on specific drug targets.
Methods Data set. In this study we used the L1000 CMap database recently reported  25. This database contains induced gene expression profiles of more than 25,200 perturbagens of which ~19,800 are small molecules, 314 biologics and ~5075 genes with altered function by shRNA, cDNA, and/or CRISPR. These perturbagens were assayed in different cell lines to produce around 1. 3 million of individual gene expression profiles corresponding to ~473,000 gene expression signatures. Each profile/signature reports the expression of 978 genes (referred as Landmark genes) which can be used to infer the expression of another ~12,000 genes in order to have a better picture of the full transcriptome (see original paper for more details). The complete L1000 CMap data can be downloaded from Gene Expression Omnibus (GEO IDs GSE92742 and GSE70138).
As a validation procedure we challenged the model to generate molecules from a gene expression signature coming from a knocked-out target protein. For this, we used 705 gene expression signatures from the L1000 database  25  corresponding to the knock-outs of 53 target proteins in MCF7 generated by CRISPR technology. Known active molecules for these targets were extracted from the Excape database  48  , where 28 of the 53 protein targets were present and only ten had more than 1000 active molecules. For these ten targets we generated 1000 molecular representation for each gene expression signature (148 in total) and evaluated the model by comparing the generated molecules to the known inhibitors of these targets. We evaluated the similarity between the generated compounds and the known active molecules using Fraggle similarity and Tanimoto similarity using MACCS keys and Morgan Fingerprints (radius = 3, 1024 bits) with RDKIT  63.
In Stage II, the generator (G 1 in blue) takes as input the desired gene expression signature together with a molecular representation (e. g. , the one produced by G 0 ) to repeat the process. The general pipeline is represented in c where the generative adversarial network is trained with ~20 K compounds from the L1000 dataset 25  (see Methods for details) to be able to generate compounds from a desired gene expression signature during the prediction phase.
These datasets are part of a larger project that integrates other phenotypic data collected on this trial. They include the development of harvested plants and of course agronomic and yield traits at the plot level. They are available upon request from the authors according intellectual property limitations. The raw data associated with this article can be found at https://doi. org/10. 15454/HESVA0. (Charente-Maritime, France) field station. The field station is divided into 540 plots including 475 plots with hybrids obtained by crossing 36 sterile (CMS PET1) to 36 restorer lines as previously described  (Bonnafous et al. , 2018). The other plots (around 11% of the field) contained one of the four check-hybrids (Extrasol, ES Akustic, NK Kondi and LG5450HO). These four hybrids are used as environmental control.
The harvest was performed on July 22nd 2015 between 11:00 and 12:30 local time (sunrise at 6:36 and sun zenith at 14:00). This harvest was performed seven days after anthesis as the studied hybrids flowered on July 15th on average ±3 days (standard deviation). On four plants per plot, we collected one leaf per plant positioned at nÀ4 rank (with n the total number of leaves detached of the flower head) for this molecular analysis. Leaves were cut without their petiole and immediately frozen in dry ice and then, transferred to À80 °C freezer before grinding. Field arrangement (file. MAP. pdf), technical management (file. ITK. pdf), meteorological (file. METEO. xls) and soil humidity (files. SOIL. pdf and. METEO. xls) data can be found on the SUNRISE Phenotype Archive at the following link https://sunrise-archive.
Primers design Specific primer pairs for qPCR were designed according to the sunflower genome  (Badouin et al. , 2017)  using Primer3web software (v. 0. 4. 0)  (Untergasser et al. , 2012). The following parameters were used: product size between 50 and 100 bp; primer size between 18-22 bp; primer Tm between 58-62 °C. The specificity of each pair of primers was verified by BLAST on the whole sunflower genome and tested with the LightCycler 480 Real-Time PCR Instrument (Roche Diagnostics, Mannheim, Germany) on a pool of cDNA representative of the genetic diversity of our samples. Finally, the primers were selected according to their amplification and melting curves, their cycle detection thresholds, and after verification of their specificity (absence of dimer).
(2012). Results of qRT-PCR are analyzed following the 2 À DDCt method described by  Livak and Schmittgen (2001). Threshold cycles (Ct) (number of cycles performed when the signal reaches the detection threshold) were calculated using Fluidigm Biomark software with the following parameters: quality threshold = 0. 65, baseline correction = linear (derivative), Ct threshold method = auto detector, peak sensitivity = 10, peak ratio threshold = 0. 8. In the end, only four reaction chambers (of 92160) were empty due to defective valves on the Fluidigm plates: batch 1, plate 3, hybrid SF160_SF292 versus gene HanXRQChr05g0137501; batch 1, plate 3, hybrid SF160_SF292 versus gene HanXRQChr05g0131121; batch 1, plate 3, hybrid CSF2_SF278 versus gene HanXRQChr11g0336031; batch 2, plate 1, sample pool dilution 0,25 versus gene HanXRQChr12g0369571.
Amplification efficiency The amplification efficiency of each gene was estimated from its values on the range of dilution, using the robustfit function in Matlab (version 7. 11) Statistics Toolbox (version 7. 4), as follows: X i t ¼ X i 0 Â 1 þ Ef f i ð Þ Ct ; ð1Þ where Effi is the amplification efficiency of gene i, X i 0 is the initial amount of target molecules, X i t is the amount of target molecules when the threshold cycle Ct is reached. For 32 genes, efficiency was manually curated. The aberrant values deleted were determined as plus or minus three times the standard deviation.
Missing values: curation and imputation Among the 66,951 points measured using Fluidigm, 684 had NA value, which corresponds to 1% of missing data on the experiment. For the hybrids, 259 of them have one or more missing value. The average number of missing values per genotype is 1. 8. Three genotypes had more than 10% of value missing (Tab. 2), we decided to remove those genotypes. Over the 180 studied genes, 110 genes had missing values. The average number of missing values is four per gene. Five genes had 10% or more of missing values (see Tab. 3). We decided to keep those genes for future analysis. Missing values were imputed gene per gene. For a gene i, its missing value on a hybrid genotype h is replaced by the mean of the expression level of gene i on the other hybrids, as follows: Ct h i ¼ 1 S X S s¼1 Ct s i ; ð3Þ where S is the total number of hybrid genotype, for which expression level is available for the gene i.
Acknowledgements. The authors are grateful to  Sébastien Carrère  and  Ludovic Legrand  for the storage help. These data were produced with the funding of the  French National Research Agency  ( ANR SUNRISE ANR-11-BTBR-0005 ). This work was part of the  "Laboratoire d'Excellence (LABEX) "  TULIP  ( ANR-10-LABX-41 ).
Context. Recent work has used spectra of ∼5000 stars in NGC 6397 that were extracted from a MUSE mosaic to determine the atmospheric parameters for these stars by fitting the spectra against the Göttingen Spectral Library. A significant change in metallicity between the turn off and the red giant branch was found and was discussed as a possible manifestation of predicted effects of atomic diffusion. However, the small amplitude of the effect and inconsistency with earlier measurements call for more attention before this result is interpreted. Systematic effects due to the interpolation or to the synthetic spectra cannot be ruled out at this level of precision. Aims. We reanalyze the data with : the ELODIE and MILES reference libraries in order to assess the robustness of the result. These empirical libraries have a finer metallicity coverage down to approximately the cluster metalicity turn-off.
Data and earlier analysis The observations of a 5 x 5 mosaic 1  reaching out to a distance of ∼ 3. 5 arcmin from the cluster center were made during the MUSE commissioning, between 2014 July 26 to August 3 (ESO program identifier 60. A-9100(C)). They consisted of 127 pointings of exposure time of not more than 60 s to avoid saturation of the bright giant stars, for a total integration time of 95 min. Each pointing covers a field of 1 x 1 arcmin with a spatial step of 0. 2 arcmin, and the seeing was in the range 0. 6" to 1". The data were reduced by the MUSE consortium using the official pipeline, and the extraction of the individual stellar spectra was described in H16, with a deblending approach presented in  Kamann et al. (2013, implemented  in the PampelMuse software, publicly available).
The line spread function (LSF) varies with the wavelength from a fullwidth at half-maximum (FWHM) of about 2. 82 Å at λ = 4750Å (equivalent to an instrumental velocity dispersion, σ ins = 80 km s -1 ) to about 2. 54 Å at λ = 7000Å (σ ins = 46 km s -1 ), and stays approximately constant further in the red. The actual LSF also varies across the 24 spectrographs forming MUSE by roughly 0. 1 Å. Out of the 18 932 extracted spectra, 14 271, with an SNR greater than 5 are distributed on the MUSE website 2  (the raw data are available in the ESO archive 3  ). To determine the atmospheric parameters, H16 proceeded in three steps. First they obtained T eff and log g by fitting the HST photometry to an isochrone. They then used these parameters to generate interpolated models that they cross-correlated with the observations to derive the radial velocity (v rad ).
The ELODIE interpolator is described in  Wu et al. (2011) , and for MILES, we used the interpolator described in  Sharma et al. (2016). The quality of the interpolated spectra naturally depends on the distribution of the stars in the parameter space: the denser the distribution, the better the interpolation. The quality also depends on the accuracy of the input A&A proofs: manuscript no. paper_engcorr stellar parameters that were used when the interpolators were computed, and on the intrinsic accuracy of the interpolators. At the margin of the parameter space, the interpolators use synthetic spectra to extend the validity range, but with a lower quality. The MS of the cluster is at the edge of the validity region of the interpolators. However, using the procedure described below, we successfully fit synthetic spectra from  Coelho et al.
2. This sample contains 1 587 spectra of 1 063 stars. The atmospheric parameters were determined with the ULySS package  (Koleva et al. 2009 ) by minimizing the squared residuals between the MUSE observation and an interpolated spectrum, S (λ) = P n (λ) × G(v rad , σ rel ) ⊗ TGM(T eff , logg, [Fe/H], λ), (1) where P n (λ) is a series of Legendre polynomial up to degree n, meant to absorb the instrumental spectral response and line-ofsight extinction; we used n = 20. G(v rad , σ rel ) is a Gaussian function centered at v rad and with standard deviation σ rel. The spectra were binned in logarithmic wavelength, so that the Doppler shift can be expressed by a convolution. TGM is the spectral interpolator. The free parameters are T eff , [Fe/H], v rad , σ rel and the coefficients of P n (λ). See  Arentsen et al.
Carretta et al. (2009a)  measured 13 stars at high spectral resolution, with the UVES spectrograph attached to the VLT, and  Carretta et al. (2009b)  measured 144 stars at intermediate resolution, with GIRAFFE on the same telescope, all giants with T eff < 5400 K. Lovisi et al. (2012)  measured 146 stars, but those in common with the MUSE sample are mostly blue stragglers, which are not discussed here. Finally,  Korn et al. (2007) , and  Lind et al. (2008)  observed stars with the UVES (18 stars) and GIRAFFE (116 stars) spectrographs, respectively, but in more peripheral fields that lie outside the MUSE pointings. The comparisons with the two first datasets are very similar, so that we can merge them and report only the total comparison. We find T eff (lit) -T eff (ELODIE) = 15 K with a dispersion of 30 K, and T eff (lit) -T eff (MILES) = 48 K with a dispersion of 25 K.
Results Our measurements of the atmospheric parameters for the 1 587 spectra are available in electronic form in Vizier. Figure  2  presents the distribution of the measurements in the log g versus T eff plane. The vertical axis is the photometric gravity, and the horizontal axis is the T eff from H16, ELODIE, and MILES, for the left, central, and right panel, respectively. The data points are color-coded according to the metallicity. This can be compared with the Figure  8  of H16. The RGB harbors an unphysical break in the H16 data near T eff = 5000 K. We cannot provide a final explanation for this break, but it may cor-respond to a discontinuity of the opacities used for GSL, at a threshold temperature where some molecular bands are considered. This RGB is generally hotter than in our analysis, reflecting the systematic difference quantified in Sect.