dataesr/scientific-paragraphs-categorization huggingface dataset:
FASTQ files from all bulk RNA-seq datasets were read-trimmed using TrimGalore (v0. 6. 6) on default settings. Trimmed, high-quality reads were pseudo-aligned, and transcript-level abundances were quantified using Salmon 120 (v1. 3. 0) with a decoy-aware transcriptome index for the Homo sapiens GRCh38 genome built from human GENCODE v35 annotation. Read quality, trimming performance and alignment statistics were assessed for each sample using FastQC (v0. 11. 9) and MultiQC (v1. 9)  121. The average number of mapped reads across all bulk RNA-seq samples was 28. 3 million (minimum 14. 6 million), with 78 of 80 samples having >75% mapped reads (average 85%, minimum 57%). Quality control metrics for each sample are detailed in Supplementary Table  3. Single-cell 10x Chromium RNA-seq data. Transcript quantification from sampledemultiplexed 10x Chromium FASTQ files (dataset sc-iPS-10XSeq  38  ) was performed with Salmon's single-cell processing module Alevin 122 using the same GRCh38 (GENCODE v35)-based transcriptome index as used for the bulk RNA-seq datasets.
e. , more than 8000 genes or 37,500 unique molecular identifiers detected). This left us with a total of 5,315 QC-passed cells for further analysis. Single-cell PatchSeq data. For the PatchSeq dataset (dataset sc-iPS-PatchSeq), FASTQ reads were adapter and quality trimmed with Trimmomatic 126 (v0. 39) using a custom list of adapter/primer sequences and the following parameters: ILLUMINACLIP:path/to/adapters. fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36. Transcript quantification was carried out using Salmon as performed for bulk RNA-seq data, but with the invocation of the --gcBias argument to correct for fragment-level GC biases in the paired-end input data, and using a decoy-aware index that included sequences for added RNA spike-ins (ERCCs and ArrayControl spikes) and used fluorescent reporters in addition to the human GRCh38 (GENCODE v35) reference.
Clustering analysis Principal components analysis (PCA) and hierarchical clustering of reprogrammed neuron and GTEx (post-mortem adult substantia nigra and striatum tissue) samples passing quality control was performed using R (v4. 0. 2) and RStudio (v1. 3. 1073) software based on normalized expression values. PCA (Fig. 2A ): For proper comparison with bulk RNA-seq data, we generated artificial bulk samples for the two single-cell RNA-seq datasets by summing the read counts across all cells sequenced from each subject. PCA analysis of reprogrammed neuron (n = 80 bulk and 6 artificial bulk) and GTEx substantia nigra (n = 19 bulk) samples was performed on variance-stabilized transformed counts using the plotPCA function in DESeq2 130 (v. 1. 28. 1) using all genes expressed at a minimum of 10 counts across all samples.
Hierarchical clustering (Fig. 2B ): Salmon TPM (Transcripts Per Million) values for all samples were imported into R using tximport  123 (v1. 16. 1). For the heatmap clustering of datasets based on an Euclidean distance metric, TPM values were averaged across all cells or samples in each respective dataset, log-transformed and Z-scaled, and computed distances were clustered by average-linkage hierarchical clustering. Only genes expressed at greater than or equal to 1 TPM across the total number of samples were used to compute Euclidean distances. Heatmap visualization was performed using the pheatmap 131 package (v1. 0. 12) in R.
Differential gene expression analysis Differential gene expression analyses were performed on Salmongenerated gene counts and TPM values using slightly different methods to identify genes dysregulated in each dataset of PD versus healthy control reprogrammed neurons. Single-cell RNAseq datasets were tested for differentially expressed genes using both (i) Seurat  124, 125  (v4. 0. 0)'s standard Wilcoxon rank sum test applied to count data, and a (ii) Wilcoxon rank sum test applied to raw TPM data within R (wilcox. test function). For bulk RNA-seq datasets, differential expression analysis on raw counts was performed using the DESeq2 130 's (v1. 28. 1) Wald test, and raw TPM data were analysed by Wilcoxon rank sum test as performed for single-cell RNA-seq data. Non-expressed genes had their p values set to 1.
Calculation of per-dataset gene dysregulation scores (D dataset ) For all genes expressed (≥1 TPM in at least 10% of cells or samples) in each dataset d, the combined gene fold changes and combined p-values were each mapped to a continuous 0. 01-1 scale using desirability functions provided by the desiR package  48. Specifically, the gene with the lowest (most significant) p value received maximum desirability of 1, whereas genes with values >0. 05 all received low desirability of 0. 01. Genes with pvalues in between these cut-offs received intermediate value desirability values. Desirability values for fold changes were computed in a similar way. We considered genes with a large fold change (>2) maximally desirable (value of 1), and genes with a small fold change (<1. 25) minimally desirable (value of 0.
01); intermediate desirability values were assigned to genes with a fold change in between these cut-offs. Additionally, we calculated a midbrain expression desirability score for each gene, where genes with transcript detection in the adult midbrain above cut-off (NX ≥ 1; as per The Human Brain/Protein Atlas 132 ) were considered maximally desirable (score of 1), and genes not detected in midbrain received a low score of 0. 25. A value of 0. 01 instead of zero was chosen as the minimum desirability score for each dataset's gene fold change and p-value as we did not want to exclude any genes, only make them least important. For each gene in each dataset, the desirability scores computed for p-value, fold change and midbrain expression were subsequently combined into a dysregulation score (D dataset ), using a weighted geometric mean with weights of 1, 0.
5 and 0. 01, respectively. The midbrain expression desirability score was integrated at a low weight of 1/100th that of the p value desirability score and 1/50 th that of the fold change score to ensure the D dataset scores were predominantly driven by the differential testing results, with midbrain gene expression level only serving as a soft adjustment variable to the final ranking of the genes. Genes not expressed in the dataset were given a minimal D dataset score of 0. 01. All source data are in Supplementary Data 1.
Calculation of multiple-dataset gene dysregulation scores (D overall ) To identify a common signature of genes perturbed in PD reprogrammed neurons across multiple datasets, we combined the individual dataset dysregulation scores into a higher-level overall dysregulation score (D overall ). Score D overall is calculated by geometric averaging of all individual (n = 6) dataset dysregulation scores (at equal weights) with a factor that adjusts for concordance of directionality of fold change values between datasets (at one-tenth of the total weight); i. e. , D overall score is proportionally reduced with the degree of discordance in fold change directionality between the datasets in which the gene is expressed. Refer to Supplementary Data 1 for source data.
Functional enrichment analysis Functional enrichment analysis of the top 200 genes with the highest D overall score was performed using ToppFun (https:// toppgene. cchmc. org/, database March 2021) to test for overrepresentation in Gene Ontology (GO) biological process (Fig. 3B ) and cellular component (Fig. 3C ) terms, as well as gene-chemical interactions curated by the Comparative Toxicogenomics Database  67  (CTD; Fig. 3D ). Benjamini-Hochberg method was used to adjust P-values for multiple testing, with enriched terms being considered significant for adjusted P-values < 0. 05. Detailed enrichment results are provided as Supplementary Table  4.
Gene Set Enrichment Analysis Gene Set Enrichment Analysis (GSEA)  133  was performed to explore whether the multiple dataset dysregulated genes showed a significant correlation with genes involved in Parkinsonism, dementia and schizophrenia (Fig. 3E ), PD symptoms (Fig. 3F ) and abnormal central nervous system electrophysiology (Fig. 5A ). Input data were (1) our list of genes (n = 24,693 unique expressed in ≥1 dataset) rank-ordered by decreasing D overall score, and (2) one of several phenotype gene sets retrieved from the Human Phenotype Ontology (HPO) database  131  : "Parkinsonism"/HP:0001300 (n = 74 genes), "Dementia"/HP:0000726 (n = 132 genes), "Schizophrenia"/ HP:0100753 (n = 51 genes); "Akinesia"/HP:0002304 (n = 19 genes), "Bradykinesia"/HP:0002067 (n = 68 genes), "Dyskinesia"/HP:0100660 (n = 164 genes), "Postural instability"/HP:0002172 (n = 42 genes), "Rigidity"/HP:0002063 (n = 131 genes), "Resting tremor"/HP:0002322 (n = 28 genes); "Abnormality of CNS electrophysiology"/HP:0030178 (n = 358 genes).
Pathway enrichment and network analysis Pathway enrichment analysis was performed (Fig. 4 ) to explore whether the genes commonly dysregulated between datasets showed significant correlation with genes annotated to known biological pathways  83. We rank-ordered the entire list of genes (n = 24,693 unique genes expressed in ≥1 dataset) by decreasing D overall score and statistically tested for overrepresentation of a broad collection of pathway gene sets at the top of the list using Gene-Set Enrichment Analysis (GSEA;  83, 133  ). The collection of 18,684 pathway gene sets was downloaded as a single GMT file from download. baderlab. org/EM_Genesets (Human_GOBP_All-Pathways_no_GO_iea_February_05_2021_symbol. gmt) and filtered for gene set size (min = 15 and max = 250 features after restricting to dataset).
Drug prediction based on dysregulated transcriptomic pathways DrugBank's database of n = 5,724 drug-target gene associations  90  was obtained as a single GMT file from download. baderlab. org/ EM_Genesets (Human_DrugBank_all_symbol. gmt). To test for overrepresentation of DrugBank therapeutic candidates amongst the rank-ordered list of PD-dysregulated genes, a GSEA was performed using GSEAPreranked (v4.
ACKNOWLEDGEMENTS We thank  Clive Svendsen ,  Ritchie Ho  and  Alexander Laperle  for kindly supplying helpful information to support the analysis, along with access to raw sequencing data for the Bulk-iPS-Mixed dataset and  Hugo Fernandes, Nikolai's Patikas ,  Emmanouil Metzakopian  and colleagues for providing providing access to raw sequencing data for the sc-iPS-10Xseq dataset. We thank  Ben Lewis  and  M. L. Gage  for editorial comments. We thank the DNA and cell bank of the  Institut du Cerveau  for some of the human samples preparation. We are grateful for the generous support from The  Flinders Foundation  (CB), The  Australian Medical Research Future Fund (CB) ,  Rebecca L Cooper Foundation (CB) , the  Neurological Research Foundation (CB) ,  The Boileau Family (CB) ,  The Grosset Gaia Fund (CB) ,  The Hospital Research Foundation Parkinson's SA (CB) ,  The Shake it Up Foundation (CB) ,  Michael J Fox Foundation (CB).
DATA AVAILABILITY The following sequencing datasets available under NCBI GEO or ArrayExpression accession codes were used in this study: "ArrayExpress E-MTAB-9154" (10x Chromium single-cell RNA-seq data of iPSC-derived dopamine neurons; dataset sc-iPS-10XSeq  38  , "GEO GSE120746" (transcriptome data from midbrain dopaminergic mixed neural cultures; dataset bulk-iPS-Mixed 31 , and "ArrayExpress E-MTAB-3037" (transcriptome data from PSA-NCAM-positive iNs sorted from mixed cultures; dataset bulk-iN-Mixed  39. Datasets sc-iPS-PatchSeq, bulk-iPS-Dopa and bulk-iN-Dopa are not publicly available due to specific consents of individuals who donated biological material to conduct this study but are available on reasonable request from the corresponding author to qualified researchers to the extent permitted by the Research Ethics Committee.
D Fig. 2Identification, scoring and ranking of genes dysregulated in Parkinson's disease reprogrammed neurons. A Principal component analysis (PCA) of the transcriptome data from PD patient-and control-reprogrammed neurons collected across six datasets, post-mortem adult human substantia nigra samples (GTEx), and highly pure populations of human floor plate-derived midbrain dopaminergic neurons (iCell® DopaNeurons). In vitro-engineered neuronal tissue clusters separately from post-mortem substantia nigra tissue (GTEx) and groups by method of derivation (iPSC reprogramming or direct iN conversion) irrespective of the laboratory of origin. Each data point represents a bulk or artificial bulk (for single-cell RNA-seq datasets) tissue transcriptome. Artificial bulk samples were generated by summing up the gene counts from all cells of the same subject.
Color indicates dataset of origin (annotation shown in (B)). B Heatmap clustering of the average transcriptomes of the six reprogrammed neuron datasets, GTEx substantia nigra and striatum tissues, and iCell® DopaNeurons using all expressed genes (≥1 transcript per million [TPM] across all samples). Hierarchical clustering is based on average linkage and Euclidean distance-based similarity. The darker shade denotes higher similarity. C Pipeline for computation of a per-gene dysregulation score (D) based on individual-dataset differential expression (DE) analysis results. DE analysis was performed on each dataset independently on both read counts and TPM values, and results were combined using logitp method (for a combination of P-values) and arithmetic mean averaging (for a combination of log2 fold changes).
Combined P-value and log2 fold change measures were mapped to a continuous (0. 01-1) scale using desirability functions 48  , and integrated by weighted geometric averaging to obtain an overall dysregulation score (D) for each gene. Information about whether the gene was expressed in the adult human midbrain was used as a soft filter at 1% of the total weight to prioritize the ranking of relevant genes. Dysregulation scores are integrated across multiple datasets, weighting for cross-dataset similarity in log2 fold change directionality, to obtain an overall dysregulation score per gene (D overall ; refer to Methods for details). D Number of genes (P < 0. 05 and |fold change | ≥ 1. 25) up-and downregulated in each dataset of PD versus healthy control reprogrammed neurons. E Volcano plot of differentially expressed genes between PD and control reprogrammed neurons (shown for dataset bulk-iN-Mixed).
Genes with greater statistical significance and/or greater fold change in expression have a larger dysregulation score (D; color-coded in plot). F Dataset expression levels of housekeeping genes GAPDH and ACTB are very similar between patients and controls and are associated with a low overall (multipledataset) dysregulation score. G Relative expression levels of the top 15 differentially expressed genes in PD versus control neurons in each of the six analyzed RNA-seq datasets. Each heatmap cell represents a single cell (single-cell datasets) or bulk tissue sample (bulk datasets). Disease phenotype and subject ID are annotated horizontally, and gene function is annotated vertically according to the legend. A subset of individuals (N = 17) was selected to generate both IPSC and iN models, and three neuronal lines were profiled using both single-cell and bulk RNA-sequencing (see Supplementary Table1for subject details).
Computational Workflows are widely used in data analysis, enabling innovation and decision-making for the modern society. In many domains the analysis components are numerous and written in multiple different computer languages by third parties. These polylingual workflows are common in many industries and dominant in fields such as bioinformatics, image analysis, and radio astronomy. However, in practice many competing workflow systems exist, severely limiting portability of such workflows, thereby hindering the transfer of such workflows between different systems, between different projects and different settings, leading to vendor lock-ins and limiting their generic re-usability. Here we present the Common Workflow Language (CWL) project which produces free and open standards for describing command-line tool based workflows.
Sub-Workflow Figure  1 : Excerpt from a large microbiome bioinformatics CWL workflow  [26]. This part of the workflow (which is interpretable/executable on its own) has the aim to match the workflow inputs of genomic sequences to provided sequencemodels, which are dispatched to four sub-workflows (e. g. , find_16S_matches); the sub-workflows not detailed in the figure. The sub-worklow outputs are then collated to identify unique sequence hits, then provided as overall workflow outputs. Arrows define the connection between tasks and imply their partial ordering, depicted here as layers of tasks that may execute concurrently. Workflow steps (e. g. , mask_rRNA_and_tRNA) execute command line tools, shown here with indicators for their different programming languages (e. g. , [Py] for Python, [C] for the C language).
(Diagram adapted from https://w3id. org/cwl/view/git/7bb76f33bf40b5cd2604001cac46f967a209c47f/workflows/rna-selector. cwl , which was originally retrieved from a corresponding CWL workflow of the EBI Metagenomics project, itself a conversion of the "rRNASelector"  [24]  program into a well structured workflow allowing for better parallelization of execution and provenance tracking. ) Bridges 8. The flexibility of the CWL standards enabled, for example, rapid collaboration on and prototyping of a COVID-19 public database and analysis resource  [16]. The separation of concerns proposed by the CWL standards enable diverse projects, and can also benefit engineering and large industrial projects. Likewise, users of Docker (or other software container technologies) that distribute analysis tools can use just the CWL Command Line Tool standard for providing a structured workflow-independent description of how to run their tool(s) in the container, what data is required to be provided to the container, and what results to expect and where to find them in the container.
A more explicit workflow structure can be achieved by using a workflow library focusing on a specific programming language. For example, in Parsl  [3] , the workflow constructs ("this is a unit of processing", "here are the dependencies between the units") are made explicit and added by the developer to a Python script, to upgrade it to a scalable workflow. (While we list Parsl here as an example of a monolingual workflow system, it also contains explicit support for executing external command-line tools. ) Two approaches can accommodate polylingual workflows where the components are written in more than one programming language, or where the components come from third-parties and the user does not want to or cannot modify them: use of per-language add-in libraries or the use of the Portable Operating System Interface command-line interface (POSIX CLI)  [14].
The use of per-language add-in libraries entails either explicit function calls (e. g. , using ctypes in Python to call a C library  12  ) or the addition of annotations to the user's functions, and requires mapping/restricting to a common cross-language data model. Essentially all programming languages support the creation of POSIX CLIs are familiar to many Linux and macOS users; scripts or binaries which can be invoked on the shell with a set of arguments, reading and writing files, and executed in a separate process. Choosing the POSIX command-line interface as the point of coordination means the connection between components is done by an array of string arguments representing program options (including paths to data files) along with a string-based environment variables (key-value pairs).
FEATURES OF THE COMMON WORKFLOW LANGUAGE STANDARDS The Common Workflow Language standards aim to cover the common needs of users and the commonly implemented features of workflow runners or platforms. The remainder of this section presents an overview of the CWL features, how they translate to executing workflows in CWL format, and where the CWL standards are not helpful. The CWL standard support polylingual and multi-party workflows, for which they enable computational reuse and portability (see also the CACM Box for main features). To do so, each release of the CWL standards has two 13  main components: (1) a standard for describing command line tools; and (2) a standard for describing workflows that compose such tool descriptions. The goal of the CWL Command Line Tool Description Standard 14  is to describe how a particular command line tool works: what are the inputs and parameters and their types; how to add the correct flags and switches to the command line invocation; and where to find the output files.
As shown in Figure  2B , item 3, these tool descriptions can contain well-defined hints or mandatory requirements such as which software container to use or how much compute resources are required (memory, number of CPU cores, disk space, and/or the maximum time or deadline to complete the step or entire workflow. ) The CWL execution model is explicit: Each tool's runtime environment is explicit and any required elements must be specified by the CWL tool-description author (in contrast to hints, which are optional)  16. Each tool invocation uses a separate working directory, populated according to the CWL tool description, e. g. , with the input files explicitly specified by the workflow author. Some applications expect particular filenames, directory layouts, and environment variables, and there are additional constructs in the CWL Command Line Tool standard to satisfy their needs.
Execution of workflows in CWL format CWL is a set of standards, not a particular software product to install, purchase, or rent. The CWL standards need to be implemented to be useful; a list of some implementations of the CWL standards is in Table  1. Workflow/tool runners that claim compliance with the CWL standards are allowed significant flexibility in how and where they execute a user's CWL documents as long as they fulfill the requirements written in those documents. For example, they are allowed (and encouraged) to distribute execution of a workflow 18 https://pypi. org/project/cwl-upgrader/ across all available computers that can fulfill the resource requirements specified by the user. Aspects of execution not defined by the CWL standards include (web) APIs for workflow execution and real-time monitoring.
Thermopetrological modeling of present-day subduction zones We model the thermal state of 56 subduction transects  [22] , representing all present-day subduction zones (see Methods). The thermodynamic database ref. [23]  and code Perple_X  [24]  are used to build phase diagrams, assuming bulk compositions relevant for the crustal and mantle layers at the subduction trenches ("basalt", Fig  S3a, " gabbro" and "simple peridotite model" Fig. 1a, b ). However, to enhance the accuracy of the modeled mineralogical evolution of hydrated peridotites up to mid-mantle depths, we build a hybrid phase diagram that combines experimentally derived assemblages from naturally complex systems and thermodynamic modeling ("complex peridotite model" Fig. 1c , see Methods). At shallow depths, water subduction in the basaltic and the gabbroic layers occurs via amphibole, and at subarc and postarc depths via lawsonite  [25].
Acknowledgments The authors would like to thank  Andrea Tommasi  and  Serge Lallemand  for their very useful suggestions, and  Stéphane Arnal  and  Fabien Rétif  for their assistance in installing the code on the clusters. This study was publicly funded by through  ANR  under the " Investissements d'avenir, Initiative Sciences Innovation Territoires-MUSE" program  with the reference  ANR-16-IDEX-0006. The work has been realized with the support of the  HPC Platform MESO@LR , financed by the  Occitanie / Pyrénées-Méditerranée Region ,  Montpellier Mediterranean Metropole  and the  University of Montpellier.