dataesr/scientific-paragraphs-categorization huggingface dataset:
Heterogeneous subpopulations of neurons associated with brain-related diseases and traits We sought to further understand the heterogeneity across neurons (in the non-myeloid brain tissue) in the TMS FACS data (484 cells labeled as "neuron") in association with brain-related diseases and traits (Fig. 3 ). We considered 7 brain-related diseases and traits: SCZ, MDD, BP, neuroticism (NRT), ECOL, BMI, Smoking (Supp. Table  1 ); we also considered height as a negative control trait. While these traits were broadly associated with neurons, pairwise scDRS disease score correlations were moderate across all TMS FACS cells (average of 0. 44; Supp. Fig. 22 , Supp. Table  19 ); there were also notable differences in associated brain cell populations, e. g. , oligodendrocytes produced stronger associations for SCZ than for Smoking (Supp.
Heterogeneous subpopulations of hepatocytes associated with metabolic traits We sought to further understand the heterogeneity across hepatocytes (in the liver) in the TMS FACS data in their association with metabolic traits (Fig. 3 ). Since the original study clustered all cells from the liver together  19  (limiting the resolution for distinguishing cell states within hepatocytes), we reclustered the hepatocytes alone, resulting in 6 clusters (1,102 cells; Fig. 5C ; Methods). We considered 9 metabolic traits: TG, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol (TC), TST, alanine aminotransferase (ALT), alkaline phosphatase (ALP), sex hormone-binding globulin (SHBG), and total bilirubin (TBIL) (Supp. Table  1 ); we also considered height as a negative control trait.
Fig. 32 ; Methods); we validated these inferred scores using expression signatures and independent data sets with experimentally determined annotations of ploidy level  82  and zonation  83  (Supp. Note). The inferred ploidy level and zonation varied across clusters, providing visual evidence of stronger cell-TG associations in high-ploidy clusters (clusters 1,2), particularly the periportal high-ploidy cluster (cluster 2; Fig. 5C ). We further compared the associations between the 9 metabolic traits. While these traits were broadly associated with hepatocytes, pairwise scDRS disease score correlations were moderate across all TMS FACS cells (average of 0. 36; Supp. Fig. 22 , Supp. Table  19 ); however, there were no notable differences in the associated hepatocyte subpopulations across traits (Supp.
005 except P =0. 006 for HDL and P =0. 007 for LDL, MC test; P =0. 63 for height), suggesting that high-ploidy hepatocytes may be more relevant to these metabolic traits. The association between ploidy level and metabolic traits is consistent with previous findings that ploidy levels are associated with changes in the expression level of genes for metabolic processes such as de novo lipid biosynthesis and glycolysis  80, 81  , and supports the hypothesis that liver functions are enhanced in polyploid hepatocytes  80. In addition, the periportal score was associated with the 9 metabolic traits (P <0. 005 for TC, TST, ALP, MC test; all P <0. 05 except P =0. 24 for TBIL; P =0. 24 for height). While the pericentral score was not significantly associated with these traits in the TMS FACS data, we detected significant associations across multiple other data sets (see below).
To ensure a reasonable number of scDRS discoveries, we recommend using GWAS data with a heritability z-score greater than 5, or sample size greater than 100K if heritability z-score is not available (although less stringent thresholds can be used for less polygenic traits) (Supp. Fig. 35 ). We also recommend using single-cell RNA-seq data with a diverse set of cells potentially relevant to disease, although a smaller number of cells should not affect the scDRS power. However, scDRS will not produce false positives for less ideal GWAS or single-cell data sets. scDRS does not rely on annotations of classical cell types based on known marker genes, a standard approach for integrating GWAS with scRNA-seq data  [6] [7] [8]  (and bulk gene expression data  [9] [10] [11] [12]  ; see Supp. Note), because the scDRS analysis uses the gene expression levels measured in individual cells.
We anticipate that application of scDRS to future scRNA-seq/snRNA-seq and GWAS data sets will continue to further these goals. We note several limitations and future directions of our work. First, identifying a statistical correlation between individual cells (or cell types) and disease does not imply causality, but may instead reflect indirect tagging of causal cells/cell types, analogous to previous work  6, 7, 12, 20. However, even in such cases, the implicated cells/cell types are likely to be closely biologically related to the causal cells/cell types, based on their similar expression patterns. Second, we identified putative disease genes using MAGMA, a widely used method  20. However, scDRS can be applied to any disease gene sets and gene weights, and it may be possible to construct more accurate sets of disease genes by incorporating other types of data, such as eQTL data  89  , protein-protein interaction data  90  or functionally informed SNP-to-gene linking strategies  91  ; we caution that such efforts must strive to avoid biases towards well-studied tissues.
Simulations We performed simulations on a data set with 10,000 cells subsampled from the TMS FACS data. In null simulations, we randomly selected putative disease genes from a set of non-informative genes. We considered four numbers of putative disease genes (100, 500, 1,000, or 2,000) and four types of genes to sample from: (1) the set of all genes, (2) the set of top 25% genes with high mean expression, (3) the set of top 25% genes with high expression variance, (4) the set of top 25% overdispersed genes, where the level of overdispersion is calculated as the difference between the actual variance and the estimated technical variance in the log scale data. For the default version of scDRS, we simulated GWAS gene weights by first randomly selecting a disease (out of the 74 diseases/traits) and then randomly permuting the top MAGMA z-scores from the selected disease.
scRNA-seq data sets We analyzed 16 scRNA-seq or snRNA-seq data sets (Supp. Table  2 ). We included 3 atlas-level data sets (TMS FACS, TMS droplet, and TS FACS) to broadly associate diverse cell types and cell populations to disease; these 3 data sets cover different species (mouse and human) and different technologies (FACS and droplet), which allows us to assess the robustness of our results across different species and technologies. We included another 13 data sets that focus on a single tissue and contain finer-grained annotations of cell types and cell states. Notably, several of these data sets contain experimentally determined annotations which allow us to better validate our results, including Cano-Gamez & Soskic et al. data  51.
Adjusting for cell type proportions scDRS can additionally take a set of cell type annotations (or any cell group annotations) and adjust for cell type proportions by inversely weighting cells by the number of cells in the corresponding cell type (weights were normalized to have mean 1 and were constrained between 0. 1 and 10). This version of scDRS generated highly consistent as the default version in the TMS FACS data (median of 0. 97 across 74 traits for the disease score correlation computed across all TMS FACS cells) and was well-calibrated in null simulations (Supp. Fig. 4 ). We recommend the use of this new option only for extremely unbalanced data sets, for 3 reasons. First, it produced consistent results for relatively balanced data sets such as TMS FACS. Second, it requires cell type annotations where the cell types have a similar level of granularity (e.
Analysis of T cells and autoimmune diseases We collectively analyzed all T cells from the TMS FACS data (4,125 cells labeled as CD4 + a-b T cell, CD8 + a-b T cell, regulatory T cell, mature NK T cell, mature a-b T cell, or T cell in the TMS data; Supp. Table  5 ); the more general terms like "T cell" and "mature a-b T cell" were used for cells whose more specific identities were not clear. We processed the T cells following the standard procedure using scanpy  94. First, we performed size factor normalization (10,000 counts per cell) and log transformation. Second, we selected highly variable genes and computed the batch-corrected PCA embedding using Harmony  120  , treating each mouse as a batch. Finally, we constructed KNN graphs and clustered the cells using the Leiden algorithm  121  (resolution=0.
Analysis of neurons and brain-related diseases/traits For the TMS FACS data, we focused on the 484 neurons (TMS label "neuron", excluding cells with TMS label "medium spiny neuron" or "interneuron"). For the Zeisel & Muñoz-Manchado et al. data, we applied scDRS to all 3,005 cells and then focused on the 827 CA1 pyramidal neurons ("level1class" label "pyramidal CA1"). For inferring spatial coordinates, we curated differentially expressed genes for each of the 6 spatial regions (dorsal vs. ventral, ventral vs. dorsal, proximal vs. distal, distal vs. proximal, deep vs. superficial, and superficial vs. deep) using the gene expression data from Cembrowski et al. 68  (GEO GSE67403; gene sets in Supp. Table  10 ). For each differential gene expression analysis, we selected genes based on FPKM>10 for the average expression in the enriched region (e.
Analysis of hepatocytes and metabolic traits We considered all hepatocytes in the TMS FACS data (1,162 cells) and reprocessed them following the same procedure as we did for the T cells. We further filtered out low-quality cells (mitochondrial proportion 0. 3; likely to be apoptotic or lysing cells), resulting in 1,102 hepatocytes (Fig. 5C ). We curated signature gene sets for ploidy level, zonation, and putative zonated pathways. We curated 4 sets of polyploidy signatures, including differentially expressed genes (DEGs) for partial hepatectomy (PH) vs. pre-PH  81  (used for the polyploidy score), Cdk1 knockout (case) vs. control 81 , 4n vs. 2n hepatocytes 82 , large vs. small hepatocytes  81. We curated 3 sets of diploidy signatures, including DEGs for pre-PH vs. PH 81 , control vs. Cdk1 knockout  81  , and 2n vs.
51ticles/dataset/Tabula_Sapiens_release_1_0/14267219. The Cano-Gamez & Soskic et al. data51was downloaded from https://www. opentargets. org/projects/effectorness. The Nathan et al. data 64 was downloaded from https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE158769. The Zeisel & Muñoz-Manchado et al. data 65 was downloaded from http://linnarssonlab. org/cortex/. The Zeisel et al. data 73 was downloaded from http://mousebrain. org/downloads. html. The Habib & Li et al. data 69 and Habib, Avraham-Davidi, & Basu et al. data 75 were downloaded from https://singlecell. broadinstitute. org/ single_cell. The Ayhan et al. data 70 was downloaded from https://cells. ucsc. edu/human-hippo-axis/. The Yao et al. data 74 was downloaded from https://assets. nemoarchive. org/dat-jb2f34y. The Zhong et al.
data 76 was downloaded from https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE119212. The Aizarani et al. data 86 was downloaded from https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE124395. Halpern & Shenhav et al. data 83 was downloaded from https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE84498. The Richter & Deligiannis et al. data 82 (annotated count matrix) was obtained via communication with the authors (raw data publicly available via links in the paper). The Taychameekiatchai et al. data 85 is not publicly available, but was obtained via communication with the authors.
Acknowledgements We thank  Huwenbo Shi ,  Katherine Siewert-Rocks ,  Tiffany Amariuta ,  Xiaoyu Xu ,  Benjamin J Strober , and  Alexander Gusev  for helpful suggestions. This research was funded by  NIH  grants  U01 HG009379 ,  R01 MH101244 ,  R37 MH107649 ,  R01 MH115676 , and  U01 HG012009.
Data availability We release our data at https://figshare. com/projects/Single-cell_Disease_Relevance_Score_scDRS_ /118902 (instructions at https://github. com/martinjzhang/scDRS), including GWAS summary statistics of the 74 diseases/traits, TMS FACS scRNA-seq data, reprocessed TMS FACS data (for T cells and hepatocytes), MAGMA and gold standard gene sets, and scDRS results for TMS FACS (disease scores and control scores for the 74 diseases/traits). The 16 scRNA-seq data sets were obtained as follows. The TMS FACS data and TMS droplet data  19  was downloaded from the official release https://figshare. com/articles/dataset/Processed_files_to_use_with_ scanpy_/8273102. The TS FACS data  25  was downloaded from the official release https://figshare. com/.
The use of statistical models to study the impact of weather on crop yield has not ceased to increase. Unfortunately, this type of application is characterised by datasets with a very limited number of samples (typically one sample per year). In general, statistical inference uses three datasets: the training dataset to optimise the model parameters, the validation datasets to select the best model, and the testing dataset to evaluate the model generalisation ability. Splitting the overall database into three datasets is impossible in crop yield modelling. The leave-one-out cross-validation method or simply leave-one-out (LOO) has been introduced to facilitate statistical modelling when the database is limited. However, the model choice is made using the testing dataset, which can be misleading by favouring unnecessarily complex models.
The nested cross-validation approach was introduced in machine learning to avoid this problem by truly utilising three datasets, especially problems with limited databases. In this study, we proposed one particular implementation of the nested cross-validation, called the leave-two-out method (LTO), to choose the best model with an optimal model complexity (using the validation dataset) and estimate the true model quality (using the testing dataset). Two applications are considered: Robusta coffee in Cu M'gar (Dak Lak, Vietnam) and grain maize over 96 French departments. In both cases, LOO is misleading by choosing too complex models; LTO indicates that simpler models actually perform better when a reliable generalisation test is considered. The simple models obtained using the LTO approach have reasonable yield anomaly forecasting skills in both study crops.
Introduction Many approaches are available to study the impact of climate/weather variables on crop yield. Statistical modelling, which aims to find relations between a set of explanatory variables and crop yields variability, is a widely used approach  (Lobell and Burke, 2010; Mathieu and Aires, 2016; Gornott and Wechsung, 2016; Kern et al. , 2018). This approach with various forms has many advantages, such as identifying crop production constraints (Mathieu and Aires, 2018), complementing field experiments  (Gaudio et al. , 2019) , and helping in adaptation strategies (Iizumi et al. , 2013), but it is often complex to understand and to use for several reasons. Unfortunately, the crop model is often characterised by datasets with a very limited number of samples. For instance,.
meteorological drivers over 26 years on grain maize and winter wheat yield in France. One year of data represents one sample in these applications, and about 20 samples are small for a data-driven approach. It is not easy to assess the true quality of an obtained model. The small sample size issue makes model selection very challenging. In principle, increasing the model complexity can increase the model quality. However, it can lead to overfitting if the model is too complex considering the limited information included in the database. Overfitting occurs when the model fits the training dataset artificially well, but it cannot predict well on unseen data. In statistical models, the overall database is divided into three datasets: the training dataset to optimise the model parameters, the validation datasets to select the best model, and the testing dataset to evaluate the model generalisation ability  (Ripley, 1996).
Splitting a small number of samples into three datasets is not easy. Cross-validation  (Allen, 1974; Stone, 1974)  was introduced as an effective method for both model assessment and model selection when the data is relatively small. A common type of cross-validation is the leave-one-out cross-validation (LOO) that has been used in many crop models  (Kogan et al. , 2013; Zhao et al. , 2018; Dinh et al. , 2021). This approach relies on two datasets: a training dataset is used to calibrate the model, and a testing dataset is used to assess its quality. The testing is also used to select the best model, which is not a good practice and introduces difficulties, as seen in the following. Thus, the chosen model is not independent of the testing dataset, and the obtained testing score may be unreliable.
This is not a problem if there are many available samples, but a small sample size can cause many issues: the model can overfit the training dataset; thus, the complexity of the chosen model is not adequate, and our assessment of its generalisation ability is false. This is often a mistake in crop yield modelling that uses over-complex models that cannot be calibrated with a limited number of samples. Some regularisation techniques (e. g. , information content techniques or dimesion reduction techniques) can help to constraint models toward lower complexity to limit the overfitting problem  (Dinh et al. , 2021). However, these approaches can become more technical and more challenging to master from non-statisticians. To solve the issues of LOO, another more complex approach has been introduced: the nested cross-validation  (Stone, 1974) , also known as double cross-validation or k*l-fold cross-validation, is able to use three datasets: training, validation, and testing.
In details, this approach considers one inner loop cross-validation nested in an outer cross-validation loop. The inner loop is to select the best model (validation dataset), while the outer loop is to estimate its generalisation score (testing dataset). This approach has, however, never been used in statistical crop modelling. This study proposes one particular implementation of this nested cross-validation (or k*l-fold cross-validation when l=k-1) called the leave-two-out (LTO). The LTO will be used here to obtain a reliable assessment of the model generalisation ability, compare the performances of different predictive models, and thus determine the optimal complexity of the statistical crop models. This approach will be tested in two real-world applications: Robusta coffee in Cu M'gar (a district of Dak Lak province in Vietnam) from 2000 to 2018 and grain maize over 96 departments (i.
e. , administrative units) in France for the 1989-2010 period. The following sections of this study will (1) introduce the databases used for statistical crop models, (2) describe the role of three datasets in statistical inference, (3) introduce the two cross-validation approaches, (4) evaluate and select the "best model " by using LOO and LTO approaches, (5) estimate the Robusta coffee yield anomalies in Cu M'gar (Dak Lak, Vietnam), and (6) assess the seasonal yield anomaly forecasts for grain maize in France. 2 Modeling crop yield using machine learning 2. 1 Databases.
Coffee yield database The Robusta coffee yield data were obtained from the General Statistics Office of Vietnam for the 2000-2018 period (n samp = 19). The data are available at the district and provincial levels. Here, we focus on Robusta coffee in Cu M'gar, one major coffee-producing district of Dak Lak (Vietnam), as this district is most sensitive to weather  (Dinh et al. , 2021). Our goal is to forecast the weather sensitivity of crop yield. The long-term trend represents the slow evolution of the crop yield; it often describes the changes in management like fertilisation or irrigation. Thus, suppressing this trend from the yield time series allows removing the influence of non-weather related factors. For Robusta coffee, a simple linear function is used to define the yield trend: y(t) = y 0 + α • t, where y 0 is the yield in 2000, and α is the constant annual rate of improvement.
Grain maize yield database The French crop data (area, production, and yield) on the regional level (i. e. , department which is an administrative unit in France) were collected from Agreste website (https://agreste. agriculture. gouv. fr; "Statistique agricole annuelle ") for a period of 22 years (from 1989 to 2010). The data are available for several crops such as soft wheat, durum wheat, maize, oats, etc. This study considers an application for grain maize over 96 French departments (Fig. 1 ). Some specific tests (in Sect. 5) will focus on ten departments (as presented in Fig. 1(d )) where the average grain maize production is higher than 4 × 10 5 tons (or the area is higher than 40 thousand hectares). Other available crop data will be considered in future studies. Similar to the Robusta coffee case, the grain maize anomalies are calculated by removing the long-term yield trend.
Weather database The monthly-mean total Precipitation (P) and 2 m Temperature (T) variables were collected for the period 1981-2018 from the ERA5-Land, i. e. , a replay of the land component of ERA5 re-analysis of the European Center for Medium-Range Weather Forecasts (ECMWF)  (Hersbach et al. , 2018). This database is at a spatial resolution of 0. 1 • × 0. 1 • (about 10 km × 10 km in the Equator). The monthly data are then projected from its original 0. 1 -For Robusta coffee: we evaluated n=19 corresponding to the period from the bud development process to the harvest season's peak  (Dinh et al. , 2021). Thus, 2 × 19 month weather data (P and T from May of year (t -1) to November of year t) are used as potential explanatory variables for Robusta coffee yield anomalies. -For grain maize: six months of growing period (from sowing to harvest) will be studied.
Thus, n = 6 results into 2 × 6 weather variables: P and T from April to September. Weather anomalies could be considered as for crop yield data. However, the climate trend of the 10 to 20 years is relatively low compared to the inter-annual variations. Thus, the long-term trend can be neglected, and the relative anomalies will be estimated based on the long-term average. This average value is computed for each of the n months before the harvest time. In addition, we applied a 3-month moving average centred on the particular month (instead of the monthly data) to reduce the variability at the monthly scale since this variability would introduce instabilities in our analysis due to the short database time length. (This is actually a regularisation technique).
Model complexity and overfitting Various factors control the complexity of a statistical model: the model architecture (the number of potential predictors on which the inputs are chosen, the number of inputs, or simply the model types) or the training process (e. g. , the number of epochs in NN). In principle, it is possible to increase the model quality by increasing its complexity because a more complex model can fit better a database. However, it is not always the case: the model complexity can be too high compared to the limited information included in the training database. This leads to the overfitting (or overtraining) problem, i. e. , the model fits the training dataset artificially well, but it cannot predict well data not present in the training dataset, meaning that the model is not reliable not to be used.