dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Data acquisition. Single-cell RNA-seq and snRNA-seq datasets generated from healthy adult kidney samples were downloaded from the Gene Expression Omnibus database (GEO; https://www. ncbi. nlm. nih. gov/geo/) as count matrices  [26] [27] [28] [29] [30] [31] [32]. The collection consists of 7 snRNA-seq samples from 4 independent studies (GEO Accession ID: GSE114156, GSE118184, GSE131882, GSE151302) and 32 scRNA-seq samples from 3 independent studies (GEO Accession ID: GSE131685, GSE140989, GSE159115)  [26] [27] [28] [29] [30] [31] [32]. The clinical and technical informations regarding the samples gathered from these studies are provided in Tables  1  and 3. Expression matrices of scRNA-seq samples GSM4819730 and GSM4819731 from batch GSE159115 were merged together prior to the analysis since they belong to the same individual, as well as snRNA-seq samples GSM3320197 and GSM3320198 from batch GSE118184  21, 23.
Data downloaded from GEO were already pre-processed for each dataset, in different ways across the different studies involved (Table  3 ). This heterogeneous pre-processing of the samples may biase the analysis. However since our goal is to provide widely usable and consensus cell type signatures, this technical variation is important to retain. To test whether the computed consensus signatures may be useful to automatically allocate cell types to clusters, we also downloaded available annotated datasets. Thus, Kuppe et al. (#4059315)  55, 56  scRNA-seq dataset was obtained from zenodo repository (https://zenodo. org/), and Lake et al. snRNA-seq dataset was downloaded from GEO under accession number GSE121862  9, 57. These datasets consisted in 19 chronic kidney disease samples and 43 healthy samples, respectively.
Quality control and filtering out of poor quality cells and nuclei. We used R software v4. 1. 0 (https://www. r-project. org/) and Seurat v4. 0. 5 package 61 (https://satijalab. org/seurat/) to perform the analysis. As observed in previous studies, human kidney scRNA-seq datasets generally present with high mitochondrial gene counts, which may be attributed to the processing time of human kidney samples as well as the processing itself. Moreover kidney tissue notoriously contains a lot of mitochondria, consistent with the high levels of energy needed for a proper filtration process. Therefore the standard filtering out of cells with >5% mitochondrial gene expressed was not suitable for the processing of these scRNA-seq data. Cells with <200 or >3500 (cell debris and doublets) expressed genes, and >30% mitochondrial gene expressed, were filtered out, whereas nuclei with <200 or >3500 expressed genes, and >5% mitochondrial gene expressed, were filtered out.
In total, 68,028 high quality cells and 33,412 high quality nuclei were obtained after applying these thresholds. Table  2  presents quality control  Continued metrics of every sample (i. e. number of cells/nuclei, mean number of reads per cell/nucleus, mean number of features expressed per cell/nucleus, % mitochondrial genes, % ribosomal genes), prior to and after filtering. Data were normalized and scaled (regressing out % mitochondrial genes), and highly variable genes computed using the SCTransform() function  35  (Seurat v4) for every scRNA-seq and snRNA-seq sample. Identified HVG were then used to compute PCA for every sample. SCTransform is a newly implemented statistical method in Seurat v4, pooled from the sctransform R package (https://github. com/satijalab/sctransform), that aims to better resolve the technical variability and sequencing depth differencies between cells/nuclei across datasets  35.
It is particularly interesting when working with datasets obtained from different sources, which induce important variability. Integration and dimensional reduction. Single-cell and single-nucleus samples always depend on confounding variables and may thus present differences that are called batch effects. To allow any comparison between samples, batch effects need to be mitigated as much as possible, which is done by the integration process (i. e. normalization step). Because further computations depend on this process, the quality of the integration deserves to be evaluated. Thus, two integration approaches were considered: the Seurat v4 method that outputs a corrected expression matrix for a list of genes to consider, and the Harmony v0. 1. 0 method that directly corrects the residues of the PCA for each sample.
Integration of 32 scRNA-seq samples on one hand, and 7 snRNA-seq samples on the other, was achieved by running consecutively PrepSCTIntegration(), FindIntegrationAnchors() and IntegrateData() functions from Seurat, with 2,500 integration features. Then PCA was computed and the first 30 PCs were inputed for uniform manifold approximation and projection (UMAP) of integrated scRNA-seq and snRNA-seq datasets. Harmony ran as well and UMAP was computed on the 30 first corrected PCs of both dataset. The distribution of the cells or nuclei from the different samples was compared between the two methods. Of note, Seurat and Harmony are among the best batch effect correction methods to date  17, 18. Clustering and cell type annotation. High resolution clustering is important in such meta-analysis: since there are still notable batch effects, small batch-dependent clusters may be identified.
In addition, more clusters may identify more cell types when closely related, thus more consensus cell type signatures if so. Unsupervised clustering was performed using FindClusters() function with Louvain algorithm in both dataset (resolution = 3. 4 and 3. 0 in scRNA-seq and snRNA-seq datasets, respectively). Distribution of samples across clusters was studied thanks to the pheatmap v1. 0. 12 R package. The cells were then labelled according to the expression of specific markers (Table  4 ). To match the nomenclature adopted for consensus cell type signatures, original labels from Kuppe C, et al. and Lake BB, et al. were changed (Table  5 ).
Data availability The single-cell and single-nucleus datasets generated in the study have been deposited on Figshare  62, 63. These files contain 4 assay slots (raw counts matrix, sample-dependent SCT-transformed values, post-integration SCTcorrected values, and the secondary integration SCT-corrected values) and some meta-data slots, including the dataset of origin (GEO sample accession number), the batch of origin (GEO series accession number), the method used (scRNA-seq vs snRNA-seq), the clusters, and the cell type labelling. The Figshare repository also contains supplementary Tables  1  and 2.
01 1895. 09 0. 92 GSE151302 GSM4572195 4307 1447. 19 2972. 75 0. 11 GSE151302 GSM4572196 4672 1142. 99 2109. 04 0. 11 Keys: GEO: gene expression omnibus; M: male; F: female; Y: yes; N: no; GFR: glomerular filtration rate; IFTA: interstitial fibrosis and tubular atrophy; BUN: blood urea nitrogen; NA: not available.
Batch ID Sample ID Number of cells Mean number of features per cell Mean number of counts per cell % mitochondrial genes GSE140989 GSM4191943 5915 645. 66 1327. 69 9. 19 GSE140989 GSM4191944 368 817. 72 2356. 84 5. 10 GSE140989 GSM4191945 2207 872. 55 2332. 26 4. 51 GSE140989 GSM4191946 2953 603. 88 1504. 32 18. 17 GSE140989 GSM4191947 992 711. 93 1934. 63 6. 87 GSE140989 GSM4191948 3420 463. 18 1027. 92 11. 65 GSE140989 GSM4191949 541 556. 00 1351. 68 5. 16 GSE140989 GSM4191950 3675 763. 50 1960. 04 8. 58 GSE140989 GSM4191951 391 666. 83 1718. 86 5. 25 GSE140989 GSM4191952 531 2049. 43 6304. 25 23. 51 GSE140989 GSM4191953 649 1994. 28 6019. 57 23. 39 GSE140989 GSM4191954 504 1662. 21 5190. 07 21. 80 GSE140989 GSM4191955 2756 635. 38 1684. 71 12. 40 GSE140989 GSM4191956 2521 589. 11 1370. 54 9.
10 GSE140989 GSM4191957 2582 579. 20 1316. 96 9. 09 GSE140989 GSM4191958 1087 494. 10 985. 52 9. 36 GSE140989 GSM4191959 534 572. 36 1220. 78 6. 18 GSE140989 GSM4191960 475 956. 46 2781. 59 14. 44 GSE140989 GSM4191961 523 1026. 92 2979. 92 18. 13 GSE140989 GSM4191962 782 903. 26 2358. 95 13. 12 GSE140989 GSM4191963 457 1318. 73 3882. 06 18. 11 GSE140989 GSM4191964 3499 827. 35 2026. 18 17. 35.
scRNA-seq test dataset: Kuppe C, et al. Nature. 2021 37,38 snRNA-seq test dataset: Lake BB, et al. Nat Commun. 2019 9,39 Original labels Replacement Original labels Replacement C1 Arteriolar Endothelium EC. art C1 Epithelial Cells (unassigned) Epi. na C2 B Cells B. cells C2 Podocytes Podo. C3 Collecting Duct Principal Cells PC. CD C3 Proximal Tubule Epithelial Cells (S1) PTC C4 Connecting Tubule CNT C4 Proximal Tubule Epithelial Cells (S2) PTC C5 Dendritic Cells DC C5 Proximal Tubule Epithelial Cells -Stress/Inflam PTC. na C6 Descending Thin Limb LoH. DTL C6 Proximal Tubule Epithelial Cells -Fibrinogen + (S3) PTC C7 Distal Convoluted Tubule DCT C7 Proximal Tubule Epithelial Cells (S3) PTC C8 Fibroblast 2 Fibro. C8 Decending Limb LoH. DTL C9 Fibroblast 4 Fibro. C9 Thin ascending limb LoH. ATL C10 Fibroblast 6 Fibro.
seq test dataset: Kuppe C, et al. Nature. 2021 37,38 snRNA-seq test dataset: Lake BB, et al. Nat Commun. 2019 9,39 Original labels Replacement Original labels Replacement 64,65. C41 S3 1 PTC C42 S3 2 PTC C43 S3 3 PTC C44 Schwann Cells Schwann. cells C45 T Cells T. cells C46 Thick Ascending Limb 2 LoH. TAL C47 Thick Ascending Limb 3 LoH. TAL C48 Thick Ascending Limb 4 LoH. TAL C49 Uroethlial Cells Uro. C50 Vasa Recta 1 EC. vasa. recta C51 Vasa Recta 2 EC. vasa. recta C52 Vasa Recta 3 EC. vasa. recta C53 Vasa Recta 4 EC. vasa. recta C54 Vasa Recta 5 EC. vasa. recta C55 Vasa Recta 6 EC. vasa. recta C56 Vascular Smooth Muscle Cells vSMC C57 Venular Endothelium EC. vei scRNA-.
, 2021)  that are well suited to the considered models. The corresponding tools allows us to develop optimization methods that ensure the desired structures for the covariance matrix, thus providing a flexible and numerically efficient framework for learning graphical factor models. Finally, experiments 1 conducted on synthetic and real-world data sets demonstrate the interest of considering both elliptical distributions and factor model 1 The code is available at: https://github. com/ahippert/graphfactormodel. structures in a graph learning process. We observe that the proposed algorithms lead to more interpretable graphs compared to unstructured models. Notably, the factor model approaches compare well with the current state-of-the-art on Laplacian-constrained graph learning methods that require to set the number of components as additional prior information  (Kumar et al.
Real-world data sets In this section, the performances of the proposed algorithms (GGM/GGFM and EGM/EGFM) are evaluated on three real-world data sets consisting of (i) animal species; (ii) GNSS time-series; (iii) concepts data. The purpose of the following experiments is to verify whether the factor model, which accounts for potential structures in the covariance matrix, provides an improved version of the learned graph topology. The latter is evaluated using both graph modularity m, for which high values measure high separability of a graph into sub-components  (Newman, 2006) , and visual inspection 3. Benchmarks and parameter setting: The proposed algorithms are compared with state-of-the-art approaches for (i) connected graphs: GLasso  (Friedman et al. , 2008) , which uses ℓ 1 -norm as a sparse-promoting penalty, and NGL (Ying et al.
GNSS data: We present an application on Earth surface displacement data collected by a network of Global Navigation Satellite System (GNSS) receivers from the volcanological observatory of Piton de la Fournaise (OVPF-IPGP), located at La Réunion Island. The presented network is composed of p = 22 receivers recording daily vertical displacements from January 2014 to March 2017  (Smittarello et al. , 2019) , with a total of n = 1106 observations. During this time, vertical displacements induced by volcano eruptions have been recorded by the receivers network, sometimes leading to abrupt motion changes over time. Depending on their spatial position, some receivers might move in a particular direction (upwards or downwards), thus indicating (thin) spatial correlations. Results of the learned graphs are presented in Figure  2.
Concepts data: The concepts data set  (Lake and Tenenbaum, 2010)  collected by Intel Labs, includes p = 1000 nodes 4  and n = 218 semantic features. As stated in  (Cai et al. , 2022) , "each node denotes a concept such as 'house', 'coat', and 'whale', and each semantic feature is a question such as 'Can it fly?', 'Is it alive?', and 'Can you use it?' The answers are on a five-point scale from 'definitely no' to 'definitely yes', conducted on Amazon Mechanical Turk". This dataset will only be used to assess the suitability of our factor model approaches, which are expected to be more efficient for such high-dimensional setting. Since GGFM and EGFM lead to very similar results, only the former is shown. In comparison, GGM and EGM did not provide as clean interpretable results, which motivates the use of factor models in practice.
B. 2 Limits of the graph prediction Our proposed algorithms have been designed to learn graph connections between related entities. As discussed above, the concepts data set might incorporate various forms of biases because each semantic feature are questions which require a subjective answer. As no ground truth is available for the considered data sets, we advise users to carefully interpret the predicted graph structures. Indeed, spurious graph links might be learned, which would highlight the aforementioned biases. Any predicted graph is no immutable truth and should be questioned in a critical manner. We strongly encourage the development of strategies to mitigate spurious correlations, as reliance on expert advice (i. e. , a volcanologist for the GNSS data set) or the design of more open, transparent and representative training data.
This study design improves substantially upon XWAS and exposome analyses published to date (see our systematic review in the Supplementary Information). When compared with the only previously published "environment-wide" analysis of mortality  24  that focused on a narrower range of chemical and lifestyle exposures in a small sample (n=6,008), our study identified approximately 17x more factors associated with all-cause mortality and improved the final mortality variance explained (R 2 ) by 31x from 2. 1% in this previous study to 66%. This demonstrates the importance of using large datasets and testing as broad a range of exposome influences as possible. Overall, our results indicate that environment-focused interventions are likely to have the highest impact on ameliorating premature mortality and most age-related morbidity.
Exposures We considered as potential XWAS exposures all non-genetic variables available as of July 24, 2020 that were collected at baseline and were available for participants recruited across all assessment centers. After all exclusions, recoding, and quality control (Supplementary Information), 176 unique exposures remained that were available in the full cohort that were common to both women and men. All continuous exposure variables were centered and standardized before analysis, except for age at recruitment. All ordinal categorical variables were recoded to only test linear associations and other polynomial contrasts (e. g. , quadratic or cubic associations) were not assessed. All nominal categorical exposures were analyzed with the most common category set as the reference. Detailed data dictionaries including all exposures used in imputation and XWAS steps are included in Supplementary Files SF1-SF2.
Incident chronic disease diagnoses were ascertained using ICD diagnosis codes and corresponding dates of diagnosis taken from linked hospital inpatient records and death register data. ICD data were accessed from the UK Biobank data portal on May 30, 2022, with a censoring date of September 30, 2021; July 31, 2021; or February 28, 2018 for participants recruited in England, Scotland, or Wales, respectively (8-15 years of follow-up).
Missing data imputation UK Biobank participants recruited from England were randomly assigned to a discovery (n=218,446) or replication set (n=218,445) while maintaining the same proportion of mortality cases in each. We performed missing data imputation separately in the discovery, replication, and Scottish/Welsh validation (n=55,676) datasets using the R package missRanger  27  , which combines random forest imputation with predictive mean matching (Supplementary Information). We imputed 5 datasets, with a maximum of 10 iterations for each imputation. All subsequent study analyses were run independently in each of the five imputed datasets, and results were pooled using Rubin's rule  28.
Exposome-wide association study XWAS of all-cause mortality were initially carried out separately in women and men, and then a final XWAS was calculated in the pooled dataset with both women and men to increase power. Exposures in the final pooled XWAS were limited to those asked to both women and men, omitting sex-specific reproductive factors. In each XWAS, we serially assessed associations of each individual exposure with all-cause mortality using Cox proportional hazards models with age as the time scale; stratified by 5-year birth cohorts and sex (in the pooled analysis only); and adjusted for assessment center, years of education (7 years, 10 years, 13 years, 15 years, 19 years, 20 years), and ethnicity (White, Asian, Black, Mixed, Other). Since it has been shown that UK Biobank participants are likely to misreport alcohol consumption as a function of higher disease burden  29  , self-reported overall health status was added as an additional XWAS covariate for the self-reported alcohol intake exposure only.
Correlation and cluster analyses Correlation between all variables was calculated in the full English sample using the R package polycor  31  to create a heterogenous correlation matrix for each imputed dataset (Supplementary Information). We used hierarchical clustering via Euclidean distance to identify the cluster structure of exposures replicated in the pooled XWAS and not susceptible to reverse causation bias (plus education and ethnicity). Within-cluster Cox multivariable mortality models included all remaining variables in the cluster after removing collinear variables (Supplementary Information), with additional adjustment for assessment center, household income (Less than 18,000, 18,000 to 30,999, 31,000 to 51,999, 52,000 to 100,000, Greater than 100,000), Townsend deprivation index, years of education, and ethnicity (if those variables were not already in the cluster).
Exposome and polygenic risk multivariable models For each outcome, five multivariable models were calculated. The first only includes (scaled) and sex in the model (model 1). Model 2 includes age, sex, and the polygenic risk score (PRS) for the outcome, if available (see below for more detail). Model 3 includes age, sex, and all exposures associated with the outcome (exposome). Model 4 includes age, sex, exposome, and PRS. If a PRS was not available for a particular outcome, then models 2 and 4 were not calculated for that outcome. Each model was validated in the independent Scottish/Welsh dataset (n=55,676) by obtaining the linear predicted values from the models in the English dataset and measuring the C-index and R 2 for these values in relation to the outcome rates in the Scottish/Welsh population.
Figures Fig. 1. Study overview. (a) After participant exclusions, UK Biobank participants were split into independent discovery, replication, and validation sets. Missing values were imputed separately within each group using random forest multiple imputation, resulting in 5 imputed datasets for each dataset. (b) Among UK Biobank participants recruited in England (n=436,891), an exposome-wide association study (XWAS) for all-cause mortality was conducted using the discovery and replication sets. The discovery and replication sets were then pooled, and further analyses were conducted in the full sample to identify and remove replicated exposures that are sensitive to reverse causation (disease sensitivity), mismeasurement (PheWAS per exposure), and correlation bias (cluster analysis). (c) Exposures surviving all analyses in (b) were then tested in relation to 25 age-related biomarkers, 25 age-related diseases, and 3 cardiometabolic risk factors (hypertension, obesity, dyslipidemia).
Acknowledgements We thank  Robert Clarke ,  Roel Vermeulen ,  Gary Miller , and  Timothy Key  for critical review of the analyses presented in this manuscript. This research has been conducted using the  UK Biobank Resource  under Application Number  61054. Funding: A. N-H. receives research funding from  Novo Nordisk ,  GSK , and  Ono Pharma. A. D. is supported by the  Wellcome Trust  [ 223100/Z/21/Z ],  Novo Nordisk ,  Swiss Re , the  British Heart Foundation Centre of Research Excellence  (grant number  RE/18/3/34214 ), and  Health Data Research UK , an initiative funded by  UK Research and Innovation, Department of Health and Social Care (England)  and the devolved administrations, and leading medical research charities. C. M. vD. is supported by the common mechanisms and pathways in Stroke and Alzheimer's disease ( CoSTREAM ) project (www.
Milk sampling was conducted during winter (December 2020 -February 2021, n = 52) and summer (June 2021 -September 2021, n = 8) seasons. The resulting dataset was characterized by an average SCC value of 357,220 ± 1,102,894 cells/mL, 138 ± 87. 9 (average value ± sd) days in milk (DIM) and 15,8 ± 4. 2 kg of milk matter avarage production. Moreover, 17/60 (28. 33%) samples were positive to Staphylococcus spp. , 2/60 (3. 33%) were positive to Streptococcus uberis, while the remaining samples (41/60, 68. 33%) had negative bacteriological tests  [23]. Before sequencing, samples were clustered using a SCC threshold level of 200,000 cells/mL, normally considered as a SCC level of subclinical mastitis, in two groups high SCC (group H) and low SCC (group L).
Statistical analysis The dataset related to the collected milk samples, including parity, DIM, season, farms, milk yield, microbiology and SCC, was tted to a linear mixed model using the lmer() function from lme4 package  [24]  in RStudio (R v. 4. 1. 2, RStudio v. 1. 4. 1103). Parity and DIM data were set as xed effects, while time of the sampling (season), farms, milk yield, microbiology and SCC results were set as random effects and the model was then tested using anova() function.
Bioinformatic analysis Fastq les were trimmed using cutadapt (cutadapt 3. 5 with Python 3. 7. 7) following the kit manufacturer instructions (parameters: -m 15 -u 3 -a AAAAAAAAAA). Trimmed fastq les were processed using the miRNAseq work ow implemented in docker4seq [26-28]. Brie y, quality control of trimmed reads was performed using FastQC software v. 0. 11. 9 (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Quality of trimmed reads was checked to evaluate the overall distribution of sequenced fragments length. Then, trimmed reads were mapped on bovine miRNA precursors (miRbase 22) using BWA aligner (v. 0. 7. 12)  [29] , and mature 5p and 3p miRNAs were counted with a R script embedded in the docker4seq work ow. Counts ltering, data normalisation, and differential expression analysis were performed in RStudio.
We rst normalised the miRNAs count matrix with the sequencing depth for each sample by calculating counts per million (CPM). Then, we ltered out genes expressed in less than 10 samples with CPM < 0. 5 using the cpm() function from edgeR package (v. 3. 36. 0)  [30]. miRNAs failing these criteria were removed from the count matrix before the exploration, and differential expression analyses. Once achieved a ltered miRNAs matrix, exploratory analysis of the expressed miRNAs was performed using unsupervised principal component analysis (PCA) and the non-parametric multidimensional similarity (NMDS) analysis with ggplot2 (v 3. 3. 5) R package  [31]. Differentially expressed (DE) miRNAs analysis was performed pairwise using edgeR (v. 3. 36. 0) package  [30]. The differential analysis was performed by comparing samples according to their SCC threshold level of 200,000 cells/mL.