dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In this paper, we have proposed the Data Gathering system for image Pose Estimation (DGPE) used to refine image localization and camera orientation estimation. We have used simple input data for DGPE because we believe this data is widely available and constantly updated on active sources, such as social media and collaborative cartography. We have explained our new building detection method SBBD and proven its robustness against some occlusion problems and weather changes. Finally, we have presented two case studies that show fully automatic results DGPE gave for image pose recognition. We have also presented the total DGPE results using two images databases. For the first database, only images are used as input data. Therefore, only 26% of the images' poses are found using the building shape and 2D GIS matching process.
The text detected in the images is also useful in a few cases but needs preprocessing and filtering to be more relevant. For the second database, the images were uploaded on "Flickr", semantic data were added, such as image titles and hash tags. We compared with this second database the difference in localization when using semantic information or not. We have found that adding semantic information to the process improves the localization process by 48%, yet it does not add orientation information that could be found using the building shape and GIS matching.
As in our previous study  11  , we found that the association between schizophrenia and telencephalon projecting inhibitory neurons (that is, medium spiny neurons) was independent from telencephalon projecting excitatory neurons (that is, pyramidal neurons). For Parkinson's disease, enteric neurons, oligodendrocytes and cholinergic and monoaminergic neurons were independently associated with the disorder (Fig. 2b ), suggesting that these three different cell types play an independent role in the etiology of the disorder. Replication in other single-cell RNA-seq datasets. To assess the robustness of our results, we repeated these analyses in independent datasets. A key caveat is that these other datasets did not sample the entire nervous system as in the analyses above. First, we used a single-cell RNA-seq dataset that identified 88 broad categories of cell types from 9 mouse brain regions  34.
We found similar patterns of association in this external dataset (Fig. 3a , Extended Data Fig. 6  and Supplementary Table  6 ). Notably, for schizophrenia, we strongly replicated associations with neurons from the cortex, hippocampus and striatum. We also observed similar cell-type associations for other psychiatric and cognitive traits (Fig. 3a  and Extended Data Figs. 6  and 7 ). For neurological disorders, we found that stroke was significantly associated with mural cells while Alzheimer's disease was significantly associated with microglia (Extended Data Fig. 6 ). The associations of Parkinson's disease with neurons from the substantia nigra and oligodendrocytes were significant at a nominal level in this dataset (P = 0. 006 for neurons from the substantia nigra; P = 0. 027 for oligodendrocytes using LDSC).
By computing gene expression specificity within neurons, we replicated our findings that neurons from the cortex can be prioritized for multiple traits (schizophrenia, bipolar disorder, educational attainment, intelligence, BMI, neuroticism, MDD and anorexia; Extended Data Fig. 8 ). Second, we reanalyzed these GWAS datasets using our previous dataset  11  (24 cell types from 5 mouse brain regions; Fig. 3b , Extended Data Fig. 9  and Supplementary Table  7 ). We again found strong associations of pyramidal neurons from the somatosensory cortex, pyramidal neurons from region 1 of the cornu ammonis (CA1) of the hippocampus (both corresponding to telencephalon projecting excitatory neurons in our main dataset) and medium spiny neurons from the striatum (corresponding to telencephalon projecting inhibitory neurons) with psychiatric and cognitive traits.
MDD and autism were most associated with neuroblasts, while intracranial volume was most associated with neural progenitors. The association of dopaminergic adult neurons with Parkinson's disease was significant at a nominal level using LDSC (P = 0. 01), while oligodendrocytes did not replicate in this dataset, perhaps because they NATuRE GENETICS | VOL 52 | MAy 2020 | 482-493 | www. nature. com/naturegenetics were not sampled from the regions affected by the disorder (that is, spinal cord, pons, medulla or midbrain). A within-neuron analysis again found that projecting excitatory (that is, pyramidal CA1) and projecting inhibitory neurons (that is, medium spiny neurons) can be prioritized for multiple traits (schizophrenia, bipolar disorder, intelligence, educational attainment and BMI). In addition, neuroblasts could be prioritized for MDD and neural progenitors could be prioritized for intracranial volume (Extended Data Fig.
10 ). Third, we evaluated a human dataset consisting of 15 different cell types from the cortex and hippocampus  35  (Fig. 4a  and Supplementary Table  8 ). We replicated our findings with psychiatric and cognitive traits being associated with pyramidal neurons (excitatory) and interneurons (inhibitory) from the somatosensory cortex and hippocampus. We also replicated the association of Parkinson's disease with oligodendrocytes (enteric neurons and cholinergic and monoaminergic neurons were not sampled in this dataset). No cell types reached our significance threshold using specificity metrics computed within neurons, possibly because of similarities in the transcriptomes of neurons from the cortex and hippocampus. Fourth, we evaluated a human dataset consisting of 35 different cell types from 3 different brain regions (visual cortex, frontal cortex and cerebellum) (Fig.
4b  and Supplementary Table  9 )  36. We found that schizophrenia, educational attainment, neuroticism and BMI were associated with excitatory neurons, while bipolar disorder was associated with both excitatory and inhibitory neurons. As observed previously 10,16,25 , Alzheimer's disease was significantly associated with microglia. Oligodendrocytes were not significantly associated with Parkinson's disease in this dataset, again possibly because the spinal cord, pons, medulla and midbrain were not sampled. No cell types reached our significance threshold using specificity metrics computed within neurons in this dataset. Validation of oligodendrocyte pathology in Parkinson's disease. We investigated the role of oligodendrocytes in Parkinson's disease. First, we confirmed the association of oligodendrocytes with Parkinson's disease by combining evidence across all datasets (Fisher's combined probability test, P = 2.
5 × 10 -7 using MAGMA and 6. 3 × 10 -3 using LDSC; Supplementary Table  2  and Supplementary Fig. 5 ). In addition, oligodendrocytes remained significantly associated with Parkinson's disease after conditioning on the top neuronal cell type in each dataset (P = 1. 2 × 10 -7 , Fisher's combined probability test). Second, we tested whether genes with rare variants associated with parkinsonism (Supplementary Table  10 ) were specifically expressed in cell types from the mouse nervous system (Methods). As for the common variant, we found the strongest enrichment for cholinergic and monoaminergic neurons (Supplementary Table  11 ). However, we did not observe any significant enrichments for oligodendrocytes or enteric neurons for these genes. Third, we applied expression-weighted cell-type enrichment (EWCE)  10  to test whether genes that are upregulated/downregulated in post-mortem brains from humans with Parkinson's disease (from six separate cohorts) were enriched in cell types located in the substantia nigra and ventral midbrain (Fig.
Discussion In this study, we used gene expression data from cells sampled from the entire nervous system to systematically map cell types to GWAS results from multiple psychiatric, cognitive and neurological complex phenotypes. We note several limitations. First, we emphasize that we can implicate a particular cell type, but it is premature to exclude cell types for which we do not have data  11. Second, we used gene expression data from mice to understand human phenotypes. We believe our approach is appropriate for several reasons. First, crucially, the key findings were replicated in human data. Second, single-cell RNA-seq is achievable in mouse but difficult in human neurons (where singlenuclei RNA-seq is typical  35, 36, 42, 43  ). In the brain, differences between single-cell and single-nuclei RNA-seq are important as transcripts  that are missed by sequencing nuclei are important for psychiatric disorders  11  , and we previously showed that dendritically transported transcripts are specifically depleted from nuclei datasets  11  (confirmed in four additional datasets; Supplementary Fig.
6 ). Third, correlations in gene expression for cell type across species are high (median correlation 0. 68; Supplementary Fig. 7 ), and as high as or higher than correlations across methods within cell type and species (single-cell versus single-nuclei RNA-seq, median correlation 0. 6)  44. Fourth, we evaluated only protein-coding genes with 1:1 orthologs between mice and humans, which are highly conserved. Fifth, we previously showed that gene expression data cluster by cell type and not by species  11  , indicating broad conservation of core brain cellular functions across species. Sixth, we used a large number of genes to map cell types to traits (~1,500 genes for each cell type), minimizing potential bias due to individual genes differentially expressed across species. Seventh, if there were strong differences in cell-type gene expression between mice and humans, we would not expect that specific genes in mouse cell types would be enriched in genetic associations with human disorders.
Gene expression data. We collected publicly available single-cell RNA-seq data from different studies. The core dataset of our analysis is a study that sampled more than 500,000 single cells from the entire mouse nervous system (19 regions) and identified 39 broad categories (level 4) and 265 refined cell types (level 5)  23. The 39 cell types expressed a median of 16,417 genes, had a median unique molecular identifier (UMI) total count of ~8. 6 million and summed the expression of a median of 1,501 single cells (Supplementary Table  13 ). The replication datasets were: a mouse study that sampled 690,000 single cells from 9 brain regions (frontal cortex, striatum, globus pallidus externus/nucleus basalis, thalamus, hippocampus, posterior cortex, entopeduncular nucleus/subthalamic nucleus, substantia nigra/ventral tegmental area and cerebellum) and identified 565 cell types  34  (note that we averaged the UMI counts by broad categories of cell type in each brain region, resulting in 88 different cell types); our prior mouse study of ~10,000 cells from 5 different brain regions (and samples enriched for oligodendrocytes, dopaminergic neurons, serotonergic neurons and cortical parvalbuminergic interneurons) that identified 24 broad categories and 149 refined cell types  11  ; a study that sampled 19,550 nuclei from frozen adult human post-mortem hippocampus and prefrontal cortex and identified 16 cell types  35  ; a study that generated 36,166 single-nuclei expression measurements (after quality control) from the human visual cortex, frontal cortex and cerebellum  36.
We also obtained bulk-tissue RNA-seq gene expression data from 53 tissues from the GTEx consortium  7  (v8, median across samples). Gene expression data processing. All datasets were processed uniformly. First we computed the mean expression for each gene in each cell type from the single-cell expression data (if this statistic was not provided by the authors). We used the pre-computed median expression across individuals for the GTEx dataset and excluded tissues that were not sampled in at least 100 individuals, non-natural tissues (for example, Epstein-Barr virus-transformed lymphocytes) and testis tissues (outlier using hierarchical clustering). We then averaged the expression of tissues by organ (with the exception of brain tissues) resulting in gene expression profiles of a total of 37 tissues.
For all datasets, we filtered out any genes with non-unique names, genes not expressed in any cell types, non-protein-coding genes and, for mouse datasets, genes that had no expert-curated 1:1 orthologs between mice and humans (Mouse Genome Informatics, The Jackson Laboratory, version 11/22/2016). Gene expression was then scaled to a total of 1 million UMIs (or transcripts per million (TPM)) for each cell type/tissue. We then calculated a metric of gene expression specificity by dividing the expression of each gene in each cell type by the total expression of that gene in all cell types, leading to values ranging from 0 to 1 for each gene (0: meaning that the gene is not expressed in that cell type; 0. 6: that 60% of the total expression of that gene is performed in that cell type; 1: that 100% of the expression of that gene is performed in that cell type).
The 10% most specific genes (Supplementary Tables  14  and 15 ) in each tissue/cell type partially overlapped for related tissues/cell types, did not overlap for unrelated tissue/cell types and allowed us to cluster related tissues/cell types as expected (Supplementary Figs. 10 and 11). MAGMA primary and conditional analyses. MAGMA (v1. 06b)  18  is a software for gene-set enrichment analysis using GWAS summary statistics. Briefly, MAGMA computes a gene-level association statistic by averaging P values of SNPs located around a gene (taking into account LD structure). The gene-level association statistic is then transformed to a z score. MAGMA can then be used to test whether a gene set is a predictor of the gene-level association statistic of the trait (z score) in a linear regression framework.
Only genes with at least 1 TPM or 1 UMI per million in the tested cell type were used for this analysis. The significance level of the different cell types was highly correlated with the effect size of the cell type (Supplementary Fig. 12 ), with values ranging between 0. 999 and 1 across the 18 brain-related traits in the Zeisel et al. dataset  23. The significance threshold was set to a 5% false discovery rate across all tissues/cell types and traits within each dataset. MAGMA can also perform conditional analyses given its linear regression framework. We used MAGMA to test whether cell types were associated with a specific trait conditioning on the gene-level genetic association of another trait (z score from MAGMA. out file) or to look for associations of cell types conditioning on the 10% most specific genes from other cell types by adding these variables as covariates in the model.
94 to 0. 98 with the other window sizes, indicating that our results are robust to this parameter. In a recent paper, Watanabe et al. 76  introduced a different methodology to test for association between cell type and complex traits based on MAGMA. Their proposed methodology tests for a positive relationship between gene expression levels and gene-level genetic associations with a complex trait (using all genes). Their method uses the average expression of each gene in all cell types in the dataset as a covariate. We examined the method of Watanabe et al. in detail, and decided against its use for multiple reasons. First, Watanabe et al. hypothesize that genes with higher levels of expression should be more associated with a trait. In extended discussions among our team (which includes multiple neuroscientists), we have strong reservations about the appropriateness and biological meaningfulness of this hypothesis; it is a strong requirement and is at odds with decades of neuroscience research where molecules expressed at low levels can have a profound biological impact.
For instance, many cell-type-specific genes that are disease relevant are expressed at moderate levels (for example, Drd2 is in the 10% most specific genes in telencephalon projecting inhibitory neurons but in the bottom 30% of expression levels). Our method does not make this hypothesis. Second, the method of Watanabe et al. corrects for the average expression of all cell types in a dataset. This practice is, in our view, problematic as it necessarily forces dependence on the composition of a scRNA-seq dataset. For instance, if a dataset consists mostly of neurons, this amounts to correcting for neuronal expression and necessarily erodes power to detect trait enrichment in neurons. Alternatively, if a dataset is composed mostly of non-neuronal cells, this will impact the detection of enrichment in non-neuronal cells.
nature. com/naturegenetics upstream and by 100 kb downstream of each gene as previously  12. SNPs located in 100-kb regions surrounding the 10% most specific genes in each cell type were added to the baseline model (consisting of 53 different annotations) independently for each cell type (1 file for each cell type). We then selected the coefficient z-score P value as a measure of the association of the cell type with the traits. The significance threshold was set to a 5% false discovery rate across all tissues/cell types and traits within each dataset. All plots show the mean -log 10 [P] of partitioned LDscore regression and MAGMA. All results for MAGMA or LDSC are available in supplementary data files (Supplementary Tables 1, 2 and 5-9). We evaluated the effect of varying the window size and varying the percentage of most specific genes on the schizophrenia cell-type association strength (-log 10 [P]).
MAGMA versus LDSC ranking. To test whether the cell-type rankings obtained using MAGMA and LDSC in the Zeisel et al. dataset  23  were similar, we computed the Spearman rank correlation of the cell-type association strength (-log 10 [P]) between the two methods for each complex trait. The Spearman rank correlation was strongly correlated with λ GC (a measure of the deviation of the GWAS test statistics from the expected; Spearman correlation = 0. 89; Supplementary Fig. 16 ) and with the average number of cell types below our stringent significance threshold (Spearman correlation = 0. 92), indicating that the overall ranking of the cell types is very similar between the two methods, provided that the GWAS is well powered (Supplementary Fig. 17 ). In addition, we found that λ GC was strongly correlated with the strength of association of the top tissue (-log 10 [P]; Spearman correlation = 0.
88; Supplementary Fig. 18 ), as well as with the effect size (beta) of the top tissue (Spearman correlation = 0. 9), indicating that the associations between cell type and trait are stronger for well-powered GWASs. The significance level (-log 10 [P]) was also strongly correlated with the effect size (Spearman correlation = 0. 996; Supplementary Fig. 18 ) for the top cell type of each trait. Dendritic depletion analysis. This analysis was performed as previously described  11. In brief, all datasets were reduced to a set of six common cell types: pyramidal neurons, interneurons, astrocytes, microglia and oligodendrocyte precursors. Specificity was recalculated using only these six cell types. Comparisons were then made between pairs of datasets (denoted in the graph with the format 'X versus Y').
The difference in specificity for a set of dendrite-enriched genes is calculated between the datasets. Differences in specificity are also calculated for random sets of genes selected from the background gene set. The probability and z score for the difference in specificity for the dendritic genes is thus estimated. Dendritically enriched transcripts were obtained from Supplementary Table  10  of Cajigas et al. 77. For the KI dataset  11  , we used S1 pyramidal neurons. For the Zeisel 2018 dataset  23  , we used all ACTE* cells as astrocytes, TEGLU* as pyramidal neurons, TEINH* as interneurons, OPC as oligodendrocyte precursors and MGL* as microglia. For the Saunders dataset  34  , we used all Neuron. Slc17a7 cell types from FC, HC or PC as pyramidal neurons; all Neuron. Gad1Gad2 cell types from FC, HC or PC as interneurons; Polydendrocye as OPCs; Astrocyte as astrocytes, and Microglia as microglia.
The Lake datasets both came from a single publication  36  that had data from the frontal cortex, visual cortex and cerebellum. The cerebellum data were not used here. Data from frontal and visual cortices were analyzed separately. All other datasets were used as described in our previous publication  11. The code and data for this analysis are available as an R package (see 'Code availability' below). GO term enrichment. We tested whether genes that were highly specific to a trait-associated cell type (top 20% in a given cell type) and highly associated with the genetics of the traits (top 10% MAGMA gene-level genetic association) were enriched in biological functions using the topGO R package  78. As background, we used genes that were highly specific to the cell type (top 20%) or highly associated with the trait (top 10% MAGMA gene-level genetic association).
Parkinson's disease rare variant enrichments. We searched the literature for genes associated with parkinsonism on the basis of rare and familial mutations. We found 66 genes (listed in Supplementary Table  10 ). We used linear regression to test whether the z-scaled specificity metrics (per cell type) of the 66 genes were greater than 0 in the different cell types. Parkinson's disease post-mortem transcriptomes. The Moran dataset  38  was obtained from GEO (accession code GSE8397). Processing of the U133a and U133b Cel files was performed separately. The data were read in using the ReadAffy function from the R affy package  79  ; then robust multi-array averaging was applied. The U133a and U133b array expression data were merged after applying robust multi-array averaging. Probe annotation and mapping to HUGO Gene Nomenclature Committee symbols were performed using the biomaRt R package  80.
NatUrE GENEtics (Vetenskapsrådet, award:  538-2013-8864 ) and the  Klarman Family Foundation. This work is supported by the  UK Dementia Research Institute , which receives its funding from  UK DRI Ltd , funded by the  UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. We thank the research participants from 23andMe and other cohorts for their contribution to this study.
Acknowledgements J. B. was funded by a grant from the  Swiss National Science Foundation  ( P400PB_180792 ). N. G. S. was supported by the  Wellcome Trust  ( 108726/Z/15/Z ). N. G. S. and L. B. performed part of the work at the  Systems Genetics of Neurodegeneration summer school  funded by  BMBF  as part of the e:Med programme ( FKZ 01ZX1704 ). J. H. -L. was funded by the  Swedish Research Council (Vetenskapsrådet , award  2014-3863 ), StratNeuro, the  Wellcome Trust  ( 108726/Z/15/Z ) and the  Swedish Brain Foundation (Hjärnfonden). P. F. S. was supported by the  Swedish Research Council (Vetenskapsrådet , award  D0886501 ), the  Horizon 2020 Program  of the  European Union (COSYN, RIA  grant agreement no. 610307 ) and US  NIMH  ( U01 MH109528  and  R01 MH077139 ). E. A. was supported by the  Swedish Research Council  ( VR 2016-01526 ),  Swedish Foundation for Strategic Research  ( SLA SB16-0065 ),  Karolinska Institutet  (SFO Strat.
Data availability All single-cell expression data are publicly available. Most summary statistics used in this study are publicly available. The migraine GWAS  60  can be obtained by contacting the authors of that study. The full Parkinson's disease summary statistics from 23andMe can be obtained under an agreement that protects the privacy of 23andMe research participants (https://research. 23andme. com/ collaborate/#publication). The 10,000 most associated SNPs from the 23andMe cohort are available in Supplementary Table  12.