dataesr/scientific-paragraphs-categorization huggingface dataset:
841, p-value < 2. 2 10 -16 , Supplementary Fig. 17a-d ). This result indicated the feasibility and accuracy of our cellular deconvolution pipeline in pigs. To identify cell-type-speci c eQTL × in an unbiased manner, we performed eQTL deconvolution analysis by integrating our cross-tissue snRNA-seq data with the large-scale bulk RNA-seq collections from the PigGTEx project. The pseudo-bulk gene expressions of our snRNA-seq data were signi cantly correlated with those of PigGTEx bulk samples across all the 17 matching tissues, with correlation coe cients ranging from 0. 498 (colon) to 0. 745 (spleen), implying su cient concordance for the subsequent integration (Supplementary Fig. 17e ). We thus estimated the relative cell fractions of these 17 PigGTEx tissues using the snRNA-seq signature matrix of the respective tissues, where sample sizes of PigGTEx tissues varied from 44 (kidney) to 1,321 (muscle).
6a ). However, we did not nd any signi cant enrichment for health and exterior traits, possibly due to their relatively low GWAS power. To validate the results, we partitioned the heritability of two production traits, backfat thickness, and loin muscle depth, by cell types in a large population of over 26,000 genotyped individuals (Fig. 6b-c ). As expected, we observed the enriched heritability of muscle depth trait in type IIx myonuclei. Likewise, backfat thickness showed a remarkable enrichment for enterocytes in the duodenum and enteroendocrine cells in the jejunum and colon. Although both results were obtained from two datasets with different sample sizes and distinct enrichment approaches, they showed to some extent consistency (Fig. 6a-c ). Furthermore, through examining the gene-traits/disorders from Online Mendelian Inheritance in Animals database (OMIA, https://omia.
We retrieved 15,354 one-to-one pig-human orthologous protein-coding genes from the Ensembl dataset (version 104) for the following analyses. Our results revealed a total of 1,547 signi cant associations (the corrected enrichment p-value 0. 05) between pig cell types and human complex phenotypes (Fig. 6g , Supplementary Fig. 23 , and Supplementary Table  5 ). As expected, we observed signi cant enrichments of several neurological and psychiatric phenotypes, such as multiple sclerosis, schizophrenia, and bipolar disorder, in neural cell types, including excitatory neurons and neural progenitor cells from the cerebrum, as well as in certain immune cell clusters such as microglia from cerebrum and macrophages from pituitary. Additionally, metabolic traits, including type 2 diabetes and cholesterol-related phenotypes, showed expected associations with hepatocytes, pancreatic duct cells, and ileum goblet cells, as well as interesting associations with several skeletal muscle and intestine cell populations.
Discussion The domestic pig (Sus scrofa) is a valuable livestock species that contributes signi cantly to both agricultural and biomedical research. Recent studies, including our PigGTEx project, have revealed that many traits-associated variants are located in non-coding regions and affect the spatiotemporal expression of candidate genes in a context-speci c (tissue-or cell-type-speci c) fashion. However, the impacts of genetic variations on these regulatory pathways and how they vary across trait-relevant cell types have not been explored in pigs. To bridge the gaps between genetic variants and phenotypes at single-cell resolution, we performed a comprehensive analysis by integrating a cross-tissue snRNA-seq atlas with the large-scale PigGTEx datasets. This work not only establishes a comprehensive single-cell reference map as a baseline for dissecting cellular heterogeneity within and across tissues but also highlights a more powerful strategy for identifying trait-critical cellular signatures and cell-type-speci c eQTL in pigs.
The present study employed single-nucleus RNA-seq to pro le gene expression in 229,268 high-quality cells from 19 tissues in pigs, similar to a recent study  27  which constructed the rst single-cell transcriptomic atlas of 222,526 cells across 20 swine tissues. Compared with that work, our dataset represents a broader range of pig organ sources covering nine major body systems and especially < comprises several highly important tissues in pig production performance, such as skeletal muscle, four intestine segments, and three reproductive organs. Given the large diversity in the chosen tissues, the two studies demonstrate a good complement and represent very signi cant contributions to the efforts of the pig single-cell consortium. In line with single-cell landscapes in other species 1, 2, 4, 5, 49, 50, 51 , we identi ed primary cell classes based on known canonical marker genes and captured a few rare cell types like Purkinje cells from the brain and enteroendocrine cells from the intestine, which may facilitate our understanding of cell lineage trajectory and tissue homeostasis.
Furthermore, we mapped several unique associations between cell types and important phenotypes in pigs, such as the driving roles of myo ber cell types for meat production traits and Leydig cells from the testis for reproduction traits. Overall, our results provide meaningful insights into previously cryptic molecular and cellular mechanisms behind traits of economic importance and offer new opportunities for precision breeding in pigs. Despite the signi cant ndings of our study, several limitations must be noted. Firstly, the current dataset comprises only one male and one female pig and is not an exhaustive characterization of all pig organs. As such, we cannot fully capture the complete single-cell picture and inter-individual variation in cellular composition, potentially limiting our ability to explore rare cell types and map entire trait-associated cellular signatures.
The ampli ed cDNA was then used for 3' gene expression library construction. Then, indexed libraries were constructed according to the manufacturer's recommendations. After quality control, eligible libraries were sequenced on the Novaseq 6000 platform (Illumina) in a 150 bp paired-end manner. The rst 28 bp in read 1 captured both the 16 bp 10 barcode and the 12 bp UMI. Preprocessing of snRNA-seq data The Sscrofa11. 1 reference assembly 61 in FASTA format and annotated gene model in GTF format were downloaded from the Ensembl database (ftp://ftp. ensembl. org/pub/release-101/). Raw snRNA-seq data were aligned to the pig reference genome and subjected to barcode assignment and unique molecular identi er (UMI) counting using the commands recommended by the CellRanger pipeline (10 Genomics, CA, USA).
Given that snRNA-seq captures both unspliced pre-mRNA and mature mRNA, we used the include-introns option for counting exonic and intronic reads together. The ltered gene expression matrix was used for further analysis with the Seurat package 62. To ensure the accuracy and robustness of our results, we removed ambient RNA and potential doublets using DecontX 63 and DoubletFinder 64 with default settings. We also ltered out low-quality nuclei expressing less than 200 genes or more than 5,000 genes, and less than 500 UMIs or more than 15,000 UMIs, as well as those exceeding 5% of mitochondrial content. During the gene lter step, all genes not expressed in at least three nuclei were removed. In addition, to balance our dataset in subsequent analyses, we randomly selected 20,000 nuclei from the spleen, as it had a much higher number (n = 53,444) of captured nuclei compared to other tissues.
Cell clustering and cell type annotation ℃ ℃ × × × × After ltering, the remaining high-quality data were log-normalized and scaled to account for cell-to-cell variation with regression on the number of UMIs and percentage of mitochondrial genes. Subsequently, PCA linear dimensionality reduction analysis was performed, followed by t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) visualization approaches using the Scanorama tool 65 , to capture the global cell type landscapes across tissues. For individual clustering, each tissue dataset was visualized using the Seurat package 62. Parameters used in each function were manually curated to obtain the optimal clustering of cells by adjusting the number of principal components and resolutions on a per-dataset basis.
We employed the FindAllMarkers or FindMarkers function with default parameters to identify marker genes of each cluster and annotated each cell type based on known classical markers from extensive published literature. The Pearson or Spearman correlation coe cients among cell types were calculated using the average expression of the top 1,000 highly variable features, and we used the pheatmap package (https://github. com/raivokolde/pheatmap) to visualize the results. Besides, the expression of marker genes in different cell types was visualized with the ggplot2 R package.
Cellular deconvolution analysis using CIBERSORT For each tissue, we rst identi ed differentially expressed genes speci c to each cell type using the Findmarkers function in the Seurat package. We then selected the top 50 genes with the most signi cant overexpression, based on adjusted p-value (< 0. 05) and average log 2 fold change (> 0. 5), to build the gene expression signature matrix for the cell-type reference set. To predict the abundances of cell types in a mixed cell population for each tissue, we collected the RNA-seq expression matrix of 17 matching bulk tissues with our snRNA-seq data from the PigGTEx database (http://piggtex. farmgtex. org/). Subsequently, the CIBERSORT tool  44  was selected for cellular deconvolution analysis, given its great resolving power  70. To test the robustness of cellular deconvolution, we rst used the generateBulk_norep function in the SCDC package  45  to obtain the transcript per million (TPM) matrix of 1,000 pseudo bulk samples (default parameters) with known cell type distribution based on our liver snRNA-seq data.
Then we used CIBERSORT to perform deconvolution on these samples using the TPM matrix of signature genes from each cell type in pig liver. The number of permutation tests was set to 1,000 times to determine the signi cance level, and p < 0. 05 was regarded as statistical signi cance. Finally, we calculated the Spearman correlation coe cient between the known and predicted cell type distribution of hepatocyte cells to assess the accuracy of CIBERSORT deconvolution in our pig dataset.
We rst estimated the effect size (i. e. , allelic fold change, aFC) of the top ieQTL for each ieGene from ASE data using the script phaser_cis_var. py in phASER (v1. 1. 1)  73  and considered only ieQTL with nominally signi cant ASE (p-value < 0. 05) data in more than ten heterozygous individuals with more than eight reads for a gene. To lter out outlier samples in ASE data, we applied the median absolute deviation (MAD) based on Hampel's test to the allelic imbalance (AI) ratio values ( ) across samples  46, 74. When a sample had , where and is the allelic imbalance ratio value for the ith individual, we de ned it as an outlier and eliminated it in the validation process. Within a given tissue, we determined that an ieQTL was validated by ASE data if it presented a nominally signi cant (p-value < 0.
Genetic mapping of cell type speci for complex traits in pigs and humans To uncover associations of traits with cell types, we performed an enrichment analysis of signi cant GWAS loci and cell-type-speci c genes using the LOLA (v1. 22. 0) R package  76. Speci cally, we extracted the top 200 cell-type-speci c genes sorted in ascending order by the p-value for each of the 19 available tissues and created an annotation based on the genomic regions of these candidate genes for each cell type-tissue pair. We then examined the GWAS summary statistics of 268 pig complex traits and selected signi cant genetic variants with p < 5 10 -8 for each trait  24  , using all tested SNPs of the 268 GWAS summaries as the background set. Finally, we calculated the signi cance level (p-value) of the enrichment fold using Fisher's exact test with FDR correction and de ned trait-tissue-cell type trios with p-value < 0.
05 as signi cant enrichment. Furthermore, we expanded our enrichment analysis to a larger Duroc population (> 26,000 individuals) from a commercial company, given that the current GWAS dataset is relatively small. We performed heritability enrichment analysis for backfat thickness and loin muscle depth traits with genomic partitioning of quantitative genetic variance similar to  77. A total of 11. 7 M imputed variants that had been quality-controlled were grouped into two sets: one containing variants within ± 10 Kb of the top genes speci c to each cell type, and the other containing the remaining variants. Per-variant heritability enrichment was calculated for each cell type-speci c variant set. To test the enrichment of genes associated with human traits and diseases for each speci c pig cell type, we collected the GWAS summary statistics of 137 human complex traits from the UK Biobank and public literature (Supplementary Table  4 ).
We converted cell-type-speci c genes in pigs to the corresponding human orthologous genes with one-to-one mapping with the Ensembl database. Finally, we employed linkage disequilibrium (LD) score regression analysis (https://github. com/bulik/ldsc) 78, 79 to partition the heritability based on 262 annotations, including 261 cell-type-speci c gene lists and one base annotation including all SNPs. Heritability enrichment was calculated as the proportion of trait heritability contributed by SNPs in the annotation over the total proportion of SNPs in that annotation. We reported the coe cient p-value as a measure of the association of each cell type with the traits. All plots showed the predicted z-score of partitioned LD score regression.
Acknowledgments We thank  Prof. Elisabetta Giuffra  ( INRAE, France ) for her valuable comments and suggestions on this project. This work was supported by the  National Key Research and Development Program of China  ( 2021YFF1000600 ), the  National Natural Science Foundation of China  ( 32002150  to G. Y. ,  32022078  to Z. Z. ), the  Basic and Applied Basic Research Foundation of Guangdong Province  ( 2020B1515120053  to G. Y. ), the  Shenzhen Science and Technology Innovation Commission  ( JCYJ20190813114401691  to G. Y. ), the  Local Innovative and Research Teams Project of Guangdong Province  ( 2019BT02N630  to Z. Z. ), the  China Agriculture Research System  ( CARS-35  to Z. Z. and Jiaqi L. ), the  Key Research and Development projects in Yangzhou City  ( YZ2021037  to Z. C. ), the  Independent Research Fund Denmark  ( 8048-00072A  to L.
Acknowledgments We highly appreciate the support of the  Spanish Ministry of Science and Innovation. This ministry has funded the work of  Aitor Orio-Alonso  through the program " Ayudas para contratos para la formación de doctores en empresas (Doctorados Industriales) 2018 " (Reference:  DIN2018-009886 ). Esteban Alonso-González  was supported by the postdoctoral grant of the  Centre National d'Etudes Spatiales (CNES). We also esteem the funding and support of the project of  Muon Systems  by  Centro para el Desarrollo Tecnológico Industrial (CDTI)  and the  Government of the Basque Country.
Study design and ethics This prospective pilot study was based on a double-blind randomized controlled model approved by the institutional review board (Comité d'éthique Hospitalo-Facultaire Saint Luc-UCL) of a tertiary university hospital in Belgium. The study did not involve any patients. The approval of the ethics committee was therefore consultative, and no registration number was given. This paper was written according to the CONSORT guidelines. The study protocol was previously registered under clinical trials. gov (NCT04879459).
Statistical analysis Statistical analysis was performed with SPSS 26. 0 ® and JMP pro16. 0. 0 ®. The primary outcome was evaluated using sensitivity and specificity estimates with a 95% confidence interval (CI) and the area under the curve (AUC) calculated based on receiver operating characteristic (ROC) curve analysis for the IG and CG. Regarding the secondary outcomes, a Mann-Whitney-Wilcoxon test was used to assess between-group differences in students' confidence about their diagnosis and machine use according to a Likert scale. A Mann-Whitney-Wilcoxon test was also used to assess the between-group difference in the time taken for image acquisition. A nonparametric test was chosen due to the relatively small sample sizes and the potential skewness of the distribution of acquisition times.
Both the raw data and the delayed-mode data can be accessed via https://doi. org/10. 17882/42182  (Argo, 2022). In this paper, we first describe the Argo delayed-mode process. The bias in the raw salinity data is then analyzed by using the adjustments that have been applied in delayed mode. There was an increase in salty bias in the raw Argo data beginning around 2015 and peaking during 2017-2018. This salty bias is expected to decrease in the coming years as the underlying manufacturer problem has likely been resolved. The best ways to use Argo data to ensure that the instrument bias is filtered out are then described. Finally, a validation of the Argo delayed-mode salinity dataset is carried out to quantify residual errors and regional variations in uncertainty. These results reinforce the need for continual re-evaluation of this global dataset.
It has two components: a random component and a systematic component. The random component is influenced by unpredictable effects and cannot be corrected. The systematic component, or bias, arises from recognized effects and thus can be corrected. When all the components of error have been evaluated and corrected, uncertainty refers to the doubt about the validity of the evaluation and the correction. Quantifying the uncertainties of an ocean dataset increases its usefulness to scientists and other stakeholders  (Elipot et al. , 2022). The instruments used in Argo floats and the impacts that their respective technical limitations have on the data have been described in  Wong et al. (2020). The uncertainties of Argo data have been assessed by comparison with highquality shipboard measurements, and they are concluded to be near the manufacturer instrument accuracy specifications of 0.
002 • C for temperature and 2. 4 dbar for pressure. For salinity, even though the manufacturer-specified initial instrument accuracy is 0. 0035 psu (0. 0003 S m -1 at 2 • C and 2000 dbar), the uncertainties of Argo salinity have been assessed to be around 0. 01 psu  (Riser et al. , 2008; Wong et al. , 2020). This paper aims to improve understanding of the treatment and uncertainty of Argo salinity data. Section 2 describes the evolution of Argo's salinity adjustment method and its implementation. Section 3 describes the temporal and spatial distribution of bias in the raw Argo salinity. The best ways to use Argo data are described in Sect. 4. Lastly, an evaluation of the uncertainty in Argo's delayed-mode salinity data against a shipboard CTD reference database is discussed in Sect. 5.
The result is a multiplicative correction in conductivity (or an additive correction in salinity) for each vertical profile. Böhme and Send (2005)  improved on the original method by using float-observed θ surfaces and introduced potential vorticity as a factor for selecting reference data in areas affected by topographic constraints. Owens and Wong (2009)  combined the original method with the improvements of  Böhme and Send (2005)  and introduced a piecewise linear fit with the Akaike information criterion in the treatment of the time series. Moreover, the analysis was done on the 10 best float-observed θ surfaces that had minimum salinity variance. More recently,  Cabanes et al. (2016)  suggested modifications to better account for interannual variability and provide more realistic error estimates.
As these methods evolve, their authors have maintained a set of computational code that can be used by all Argo float providers. Transparency and reproducibility of the salinity adjustments are achieved via this provision of code that operates on the raw measurement inputs to produce the delayedmode adjusted data. Currently, the code used for salinity adjustment in Argo is a combined set from  Owens and Wong (2009)  and  Cabanes et al. (2016). See https://github. com/ ArgoDMQC/matlab_owc (last access: 22 October 2020). These salinity adjustment methods rely on accurate reference data. To that end, two reference databases are provided internally in Argo for salinity adjustment: (1) a reference database which consists of shipboard CTD data (internally named CTD_for_DMQC, maintained by the Coriolis Data Center, and (2) a reference database which consists of Argo data that have been verified as having good quality without needing adjustments (internally named Argo_for_DMQC, maintained by the Scripps Institution of Oceanography).
How is salinity adjustment implemented in Argo? Delayed-mode salinity evaluation in the Argo program is carried out by each data-providing group and not by a central institution. Each data-providing group in Argo has a team of delayed-mode operators who manually inspect the data. As both pressure and temperature are required to measure salinity, all three parameters (P , T , S) are evaluated together in delayed mode. Random point-wise errors, such as spikes, are flagged as bad data. Sensor drifts are identified and either adjusted or flagged as unadjustable data. Evaluation of sensor drifts, not to be confused with real ocean signals, requires significant oceanographic knowledge, scientific judgment, and insights based on experience. To ensure all dataproviding groups are consistent in following best practices, two technical documents are maintained internally in Argo to describe the data processing procedures and to provide examples.
These are (1) the Argo Quality Control Manual for CTD and Trajectory Data  (Wong et al. , 2022)  and (2) the DMQC Cookbook for core Argo parameters  (Cabanes et al. , 2021). These are living documents, which are modified and updated as the data processing procedures develop and evolve. Due to the need to accumulate a time series for reliable evaluation of sensor drifts, delayed-mode data for a float may not be available until a sufficiently long time series from that float has been accumulated. The timeframe for availability of delayed-mode data is therefore dependent on the nature of the sensor drift, as well as the availability of the delayedmode operators. In general, most Argo delayed-mode salinity data are available about 12-18 months after the raw measurements are collected. These data are re-evaluated periodically to reduce inconsistencies between the various dataproviding groups.
Bias in Argo raw salinity data Bias in raw Argo salinity can contain effects from three different sources: 1. error from the pressure measurements  (Barker et al. , 2011) ; 2. error from conductivity cell thermal inertia, due to the lag between the temperature and conductivity measurements  (Johnson et al. , 2007; Martini et al. , 2019; Dever et al. , 2022) ; 3. error from conductivity cell sensor drift  (Wong et al. , 2020). The effect of pressure error on salinity is not negligible. For example, assuming standard seawater properties of S = 35 and T = 15 • C, a pressure error of 10 dbar will result in a salinity error of about 0. 004 psu. However, less than 1 % of Argo vertical profiles have identifiable pressure error of greater than 10 dbar. The effect of the conductivity cell thermal inertia error on salinity can exceed 0.