dataesr/scientific-paragraphs-categorization huggingface dataset:
Horizontal Dataset Results Similarly to the Vertical configuration, the absorption, and reduced scattering coefficients of white paper in the Horizontal configuration were computed and are registered in Table  6. The Horizontal configuration corresponds to the case for which the sensor is aligned with the orientation of paper sheets. Surprisingly, one can observe similar results for both µ a and µ s values with respect to the Vertical dataset. Indeed, the reduced scattering values have the same tendency regarding their behavior towards the wavelengths of the light source used. Values for the red channel are higher than the blue values and then the green values. The quality of these estimates can also be evaluated by using them to fit the diffusion model and compare it to the data, as shown on Figure  8.
Similar to the Vertical dataset, the R 2 scores are a bit higher for this type of paper, but the same challenge appears when considering the steepest part of the curve. Although the diffusion model seems adequate for the configuration chosen, it might generate some underfitting for this data. The second class of highly diffuse material we tested is white paper substrate. As previously mentioned, we experimented on two types of configurations for the paper and it resulted in various values of µ a and µ s for each paper substrate of different weight. Table  7  displays the computed relative difference of the reduced scattering coefficient for paper between the two configurations when taking the Vertical dataset as a reference. All of the relative differences are below 5% in absolute value. One can thus conclude the orientation of the paper substrate may not play a significant role for the scattering of the light when considering the diffusion approximation.
Even though the two indices are equivalent, one seems to give better results of linear correlation for µ a and µ s values. One can notice the R 2 scores and associated p-values for the red channel are good across the two datasets for each of the optical properties and whiteness indices. Regarding the scores of the green channel for the two WI, they are low, and the fact that p-values are high tends to mean the linear correlation between whiteness index and absorption or scattering properties of paper is insignificant for this specific wavelength. The results of the blue channel are mixed. The scores for µ a of the Horizontal dataset are low for both WI, but the others for (µ a , µ s ) of the Vertical dataset and µ s for the Horizontal dataset are high, and this seems to show the existence of a linear correlation with the whiteness indices.
Results Results of the inversion method applied to the reflectance profiles are displayed in Table  10. µ s estimated values are larger than µ a values, which is expected from a diffuse material with high scattering properties. However, one may notice that absorption values for the first samples with 2 and 4 drops of white paint are extremely low, to the point their absorptive properties could be nullified. Due to the high initial volume of water in the glass, these concentrations of paint may not have been enough to change the absorption properties of the resulted mixture. The mixture was not diffuse enough, which could be visually checked, and thus some absorptive properties of the water may have stayed dominant. As a consequence, we chose to keep these points in the dataset for the results to be displayed and discussed, but we discard them for studying the correlation between the concentration of white paint and the estimated optical properties.
Methods The numerical code LaMEM 34 employs a finite difference marker-in-cell technique to solve the mass, momentum and energy conservation equations on a fully staggered grid. In our models, the Cartesian model domain has dimensions of 178. 0×118. 2×28. 4 km in x, y, z, respectively, using a variable grid consisting of 384×192× 64 cells. The highest resolution, represented by a minimum grid size of 0. 333×0. 4×0. 333 km, is within 30 km of each ridge segment in the spreading direction (x), within 20 km of the transform in the y-direction, and down to a depth of 11 km below the model seafloor. The model has two layers: a 4 km-thick ocean layer overlying a lithosphere-asthenosphere layer 24. 4 km in thickness. The ocean is simulated as a fluid of relatively low density (1000 kg/m 3 ) and viscosity (5×10 18 Pa s) so as to simulate a quasi-stress-free lithospheric surface following the "sticky-air" approach  56, 57.
Acknowledgements This project was supported by  NSF -OCE # 19-28804  to G. I. and J. C. S. and  NSF -OCE # 19-28776  to M. D. B. and X. T. J. E. was supported by  CNRS (France)  and B. J. P. K. by  ERC CoG MAGMA , # 771143. Any use of trade, prod- uct, or company names is for descriptive purposes of the methodology used only and does not imply endorsement by NOAA or the  US Government. This is SOEST contribution # 11676. The authors would like to thank the editor team as well as  Laetitia Le Pourhiet  and a second anonymous reviewer for their work in improving this manuscript.
Data availability The open-source code LaMEM by  34  used for the numerical models in this study is available at https:// bitbu cket. org/ bkaus/ lamem/ src/ master/. All newly coded features are embedded in the master version of LaMEM. A "Readme" and input files for models shown in figures 3,4 and 6 in the main text as well as for figures in the supplement can be found at https:// bitbu cket. org/ JanaNa/ lamem/ downl oads/. A movie showing the evolution of the model in figure  6  can be found at the same address https:// bitbu cket. org/ JanaNa/ lamem/ downl oads/ and is described in the supplementary material.
While the amount of studies involving single-cell or single-nucleus RNa-sequencing technologies grows exponentially within the biomedical research area, the kidney field requires reference transcriptomic signatures to allocate each cluster its matching cell type. The present meta-analysis of 39 previously published datasets, from 7 independent studies, involving healthy human adult kidney samples, offers a set of 24 distinct consensus kidney cell type signatures. The use of these signatures may help to assure the reliability of cell type identification in future studies involving single-cell and single-nucleus transcriptomics while improving the reproducibility in cell type allocation.
Other critical confounding factors in both experimental and analytical settings may affect scRNA-seq and snRNA-seq data, including low sequencing depth, context-dependent cell states, clustering settings, or markers checked for cell type identification. Still, scRNA-seq and snRNA-seq are very powerful techniques increasingly used within the biomedical field in general, and in the kidney field in particular  14. To date, scRNA-seq or snRNA-seq studies of human kidneys have involved different technologies and different data preprocessing and analysis workflows. Cell type labelling in particular suffers from the lack of a universal definition of known nephron segments and cell types, as well as Fig. 1  Meta-analysis workflow. All available scRNA-seq and snRNA-seq data were retrieved and downloaded from the Gene Expression Omnibus (GEO) or zenedo repository.
Single cell RNA-seq and snRNA-seq samples were analysed separately, quality control metrics were measured and poor quality cells were filtered out in accordance. Then scRNA-seq samples and snRNA-seq samples were integrated independently. High resolution unsupervised clustering followed by visualisation of the expression of specific transcriptomic markers allowed to attribute each cluster a clear cell type (certain cell types were attributed to several clusters), or a cell type followed by « na » (i. e. not attributed) for the cells that did not show strong enough differenciation markers expression. Consensus signatures were computed using the FindAllMarkers() function in Seurat. Single cell RNA-seq and snRNA-seq were then integrated together to evaluate the matching between both datasets annotations.
Healthy tissue from… Here we present a meta-analysis of publicly available scRNA-seq and snRNA-seq datasets from 39 healthy adult kidneys, consisting in 68,028 single cells and 33,412 single nuclei. As data were taken from different sources, data were normalized following the SCTransform analysis pipeline in Seurat v4 and batch effects were mitigated by integration of scRNA-seq and snRNA-seq samples, respectively. Cell types were attributed to clusters using broad cell type markers, consensus cell type signatures were computed, and labelled scRNA-seq and snRNA-seq samples were integrated together to map cell types depending on the method used (sc/snRNA-seq). Finally, single-cell and single-nucleus consensus signatures were benchmarked by enrichment in previously published and annotated datasets.
Results Analysis workflow. To determine consensus gene signature associated to each kidney cell type, we first aimed to collect kidney scRNA-seq and snRNA-seq data available on public databases according to the workflow presented in Fig. 1. Data collection ended up with 3 scRNA-seq and 4 snRNA-seq datasets publicly available  [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32]  , encompassing a total of 101,431 cells and 35,764 nuclei, from 32 and 7 healthy adult kidneys, respectively (Table  1 ). Samples from the different datasets were pre-processed with Seurat v4, and cells expressing between 200 and 3500 genes were kept for analysis (discarding cell debris and cell doublets). As some kidney cell populations highly express mitochondrial genes, the percentage of mitochondrial gene expression threshold to use in kidney tissue is debating (varying between 20% and 50% across studies)  33, 34.
We chose to keep cells with less than 30% mitochondrial genes expressed  24  (Table  2 ). Despite nuclei should not express mitochondrial genes, nuclei with less than 5% mitochondrial genes expressed were kept to limit the waste due to possible little contamination. Since confounding variables may affect the different samples from the different studies and further analysis (Tables  1, 3 ), scRNA-seq and snRNA-seq samples were integrated separately using Seurat IntegrateData() function to mitigate the batch effects, following the newly implemented SCTransform framework for normalization and count data variance stabilization  35. Finally, the integrated datasets consisted in 68,028 single cells and 33,412 single nuclei.
Generation of a healthy human kidney consensus scRNa-seq dataset. To generate a healthy human kidney consensus scRNA-seq dataset, we first assessed the quality of the integration by comparing the distribution of cells on Principal Component Analysis (PCA) plot, before and after integration using both Harmony and Seurat v4 correction (Fig. 2a ). The correction of PC1 and PC2 by Harmony did not look as good as the one obtained with Seurat for which PC1 and PC2 did not depend anymore from the origins of the samples after integration. Thus the Seurat v4 correction was used for further computations. Uniform Manifold Approximation and Projection (UMAP) of the integrated dataset showed a very good scattering of cells from the different samples and from the different batches (Fig. 2b, c ). In addition, it has been suggested that kidney cells express subsets of genes that are regulated in a sex-dependent manner in mice  36.
However, as the sex was not known for 24 samples among 32, we could not evaluate whether a sex bias may occur in cell type attribution in humans (Fig. 2d ). Unsupervised clustering (Louvain, resolution = 3. 4) resulted in 54 distinct clusters (Fig. 3a ). Despite a satisfying correction of the batch effects, certain clusters were driven by a few samples, which may reflect individual differences rather than cell types or cell states (Fig. 3a ,b and Supp. Table  1 ). In particular, cluster 17 mostly belongs to sample GSM4145204 (50. 13% of the cells), clusters 20, 48 and 53 to sample GSM4145206 (54. 4%, 61. 37 and 100%, respectively), and clusters 31, 38 and 44 to sample GSM4191943 (77. 93%, 71. 28% and 57. 57%, respectively). Of note, these are the top 3 most abundant samples of the dataset (Table  2  and Supp.
Generation of a healthy human kidney consensus snRNa-seq dataset. The evaluation of the mitigation of the batch effects for snRNA-seq integrated dataset was not as good as the one obtained for scRNA-seq dataset, but PC1 and PC2 distribution was more satisfying after integration using Seurat v4 compared to Harmony (Fig. 6a ). Hence Seurat correction was adopted to pursue the analysis. When nuclei are displayed according to the origin of the sample or the origin of the batch the sample comes from (i. e. the publication), it is clear that sample GSM3135714 from batch GSE114156 is not well integrated to the dataset (Fig. 6b, c ). As there are only 7 samples, and some of the nuclei from this sample do not mix with the rest of the nuclei from the other samples, we chose to keep the nuclei from this sample in the analysis and exclude only the non-mixed ones after clustering.
By contrast to scRNA-seq dataset, the gender was known for the 7 snRNA-seq samples and allowed to appreciate differences in sex representation within each identified population, in particular for the principal cells of the collecting duct (PC. CD) and the cells from both the ascending thin limb (LoH. ATL) and the thick ascending limb of the loop of Henle (LoH. TAL; Fig. 6d ). Unfortunately, with only 5 men and 2 women, we could not assess whether these differences were due to a real gender bias rather than inter-individual differences or some remaining batch effects. Unsupervised clustering (Louvain, resolution = 3. 0) resulted in 53 distinct clusters (Fig. 7a ). As expected, several clusters (i. e. clusters 7, 23, 30, 33 and 34) consisted mainly in nuclei from sample GSM3135714 (accounting for 73.
4%, 86. 5%, 71. 6%, 64% and 86. 9%, respectively) (Fig. 6b, c , Fig. 7a, b  and Supp. Table  1 ). We also observed that clusters 3 and 17 mainly belonged to sample GSM4572195 (58. 6% and 63%, respectively), cluster 39 to sample GSM3320197-8 (52. 5%) and cluster 48 to sample GSM4572192 (51. 8%) (Fig. 7b  and Supp. Table  1 ). Again, classical markers were studied to allocate cell types to clusters 6,9,22,37-54 (Fig. 8a ,b, Fig. 4  and Table 4 ). Sticking as much as possible to the same nomenclature used for scRNA-seq dataset, a total of 22 cell types were retrieved among nuclei, including nephron epithelial cells, kidney mesenchymal cells, and 4 populations of PTC, LoH, PC and T cells labeled « not attributed » (Fig. 8a, b ). The computation of HVG for every cell type has been performed, and these gene lists correspond to the consensus transcriptomic cell type signatures of kidney nuclei from healthy adult individuals (Fig.
Joint analysis of scRNa-seq and snRNa-seq labeled datasets. To study the similarities and discrepancies between the results obtained with the two procedures, scRNA-seq and snRNA-seq samples were integrated together. The mitigation of the batch effects for the integration of 39 samples was overall acceptable, as attested by the correction of PC1 and PC2 (Fig. 9 ). Again, Harmony correction was not as satisfying as Seurat v4 one. Samples looked well merged, but nuclei and cells did not colocalize everywhere (Fig. 10a, b ). The allocated cell types were highly consistent between cells and nuclei (Fig. 10c ). Of note, we cannot rule out whether selecting viable cells on the basis of mitochondrial genes expression may influence this observation, since we cannot filter nuclei on the same basis. However the fact that overall, cell types were allocated at the same coordinates in cells and nuclei may give further confidence in the identified cell populations in both scRNA-seq and snRNA-seq datasets.
Validation of the enrichment of consensus signatures for automatic cell type annotation. To test wether enrichment of consensus signatures are suitable for the identification of cell types within scRNA-seq and snRNA-seq datasets, we downloaded publicly available annotated datasets from Kuppe et al. 55, 56  (scRNA-seq) and Lake et al. 9, 57  (snRNA-seq). Sample expression matrices were processed and integrated as previously. Then CelliD v1. 0  58  was used to perform enrichment analysis for scRNA-seq or snRNA-seq consensus signatures in every single cell or nucleus respectively, and the results were compared to the original labelling of the authors. To better evaluate automatic cell type annotation on test datasets, original labels were adapted to match consensus signatures nomenclature (Table  5 ).
After filtering out poor quality cells and cell doublets (less than 200 or more than 3500 expressed genes with more than 30% of mitochondrial genes), scRNA-seq dataset from Kuppe et al. 55, 56  consisted in 81,239 cells from 19 samples, representing a total of 13 chronic kidney disease patients (hypertensive nephrosclerosis)  55  (Fig. 11  and Fig. 12a ). Enrichment of consensus scRNA-seq signatures was performed following Multiple Correspondence Analysis (MCA), and UMAP was computed on the residues of the MCA using the RunMCUMAP() function implemented in CelliD  58. However, to avoid annotation of cells with the « na » label that is not informative, signatures for « na » annotated cell types were not tested. Enrichment retrieved cell labels closely related to the original labels (Fig. 12b, c ).
12b, c ). Concerning the validation of our identified snRNA-seq signature, Lake et al. 9, 57  snRNA-seq dataset was processed as previously described to remove poor quality nuclei, and finally consisted in 17,375 nuclei from 43 samples that belonged to 16 individuals, including 14 tumor-free regions of nephrectomies and 2 deceased donor kidneys  9  (Fig. 13 ). The nomenclature of the original labels was modified as previously described for scRNA-seq,  Continued to match the nomenclature of the consensus cell type signatures (Fig. 14a , Table  5 ). Again, enrichment of snRNA-seq consensus signatures was done after computation of MCA and UMAP and signatures for « na » annotated cell types were not included for enrichment. The annotations were overall conserved between original labelling and consensus signature-based labelling (Fig.
9  Integration of scRNA-seq and snRNA-seq datasets. PCA plots of scRNA-seq and snRNA-seq samples before any integration (left), after Harmony integration (middle), and after Seurat v4 integration (right). the differentially expressed genes computed between two populations (not to mention that these differentially expressed genes are calculated on the basis of the raw data, not the matrix normalized by the integration). Mapping reads to different versions of the reference genome, as well as the pre-processing of the data are also critical factors participating in batch effects between studies. This results in limited reproducibility and reliability between the different studies involving human kidney scRNA-seq and snRNA-seq. In this meta-analysis, we performed integration of 32 scRNA-seq and 7 snRNA-seq samples, from 3 and 4 different studies respectively  [26] [27] [28] [29] [30] [31] [32].
After assessing the mitigation of batch effects, we ran high resolution unsupervised clustering and allocated cell types to clusters, based on the expression of known markers, before computing consensus cell type signatures. Despite scRNA-seq and snRNA-seq samples did not equally distribute everywhere on the UMAP, cell type allocation was highly consistent across the two datasets. Finally, we showed that enrichment of consensus signatures achieved cell type allocation consistent with previously annotated datasets  56, 57. These consensus signatures may thus help increasing reproducibility and reliability between future studies involving scRNA-seq or snRNA-seq in the kidney field. Our present study tried to standardize cell type nomenclature by the way of meta-analysis. Even though proximal nephron is functionally and anatomically divided in three segments (S1 to S3), our study could not discriminate these segments and all proximal tubule data is lumped into one proximal category.
Same is true for the three descending thin limbs segments and distal convoluted tubule that is not resolved into DCT1 and DCT2. One plausible explanation is that these subpopulations are part of the unattributed population, i. e. PTC. na and LoH. na. There is also a possibility that we did not find these subpopulations because of the sparsity of the data, especially with such heterogeneity in the data sources. The datasets used in this study are the first published in the field, they were pre-processed with different tools and suffer from strong batch effects that prevent to detect many markers. However, the signatures that we proposed still allow to identify their cell types in the validation step we did. Single cell and single nucleus transcriptomics allow to study the expression of every detected gene (i.
Therefore, cell type assignment depending on the expression of canonical marker genes, which is the standard in single cell/nucleus transcriptomics analyses, may differ between scRNA-seq and snRNA-seq. Our analysis confirmed these assumptions and as a consequence provides two sets of cell type signatures, obtained by scRNA-seq or snRNA-seq strategies. Besides, we were not able to find immune cells within snRNA-seq datasets except few T cells, which is consistent with previous reports attesting that snRNA-seq in the kidneys failed to detect immune cells in general  9, 11, 59. Interestingly, we detected a population of parietal epithelial cells in both scRNA-seq and snRNA-seq datasets that express pluripotent cell, tubular epithelium and podocyte markers (e. g. SOD2, KRT8, KRT18, WT1, CD24, PAX2, SOX4, VIM, RACK1, NUPR1…; Supp.
For the same reason, such cell type enrichment may be used as a decision helper instead of a decision maker in cell type attribution to cells/nuclei. However, a more unbiased approach is possible for original studies, based on unsupervised clustering followed by extraction of the cell-specific signatures using CelliD, and finally enrichment of functional terms or pathways of these signatures. A more general limitation of such single cell studies is the statistical power for the computation of HVG (Wilcoxon Rank Sum test), that depends on the number of cells allocated for every single cell type. Indeed, the statistical power is higher for the computation of PTC signature (computed on 29,246 PTC cells vs 38,782 cells within the rest of the dataset) than for the one of DCT cells (computed on 248 DCT cells vs 67,780 cells) in the scRNA-seq dataset, for instance.
In the future, these signatures may benefit from being updated by integrating newly published healthy human kidney single cell datasets that may increase the biological variability and the number of cells for every population while mitigating the batch effects even better. In addition, the very low sequencing depth of these experiments implies that the results should be interpreted with caution. To solve this issue in cell type identification while specifically working with kidney tissue and validate the identified cell type-specific signatures, a bulk transcriptomic analysis of micro-dissected healthy human nephron segments would be really helpful, as it has been performed in rodents  6. Nevertheless, cell type allocation by enrichment of consensus signatures may depend on the size of the signatures -i.