dataesr/scientific-paragraphs-categorization huggingface dataset:
Differential Expression and Enrichment Analyses The statistical analyses were performed using R v4. 0 (R Core Team 2020). The visualization of the results was performed using the R package ggplot2 v3. 3. 2  (Wickham 2016). The DESeq2 function from the R Bioconductor package DESeq2 v1. 28. 1  (Love et al. 2014 ) was used to normalize read counts, using the default method, and to model the read counts using a negative binomial distribution. For RNAseq, the DESeq2 package was used to identify differentially expressed genes and TE families while performing a Wald test  (Love et al. 2014). The P-values were adjusted for multiple testing using the procedure of Benjamin and Hochberg  (Benjamini et al. 2001 ) with an FDR cutoff of 0. 05, and were obtained using the results function from the DESeq2 package.
The log2FC was shrunken using the default and recommended apeglm algorithm  (Zhu et al. 2019)  of the lfcShrink function. Genes with an adjusted P-value lower than 0. 05 and at least double of expression in one species above the other (absolute shrunken log2FC > 1) were considered as differentially expressed. GO term enrichment analyses of biological processes were performed for the underexpressed and overexpressed significant genes using the topGO R package v. 2. 42. 0. (Alexa and Rahnenfuhrer 2020) ("weight01" algorithm and Fisher's statistic) and the ortholog genes in D. melanogaster obtained from the D. buzzatii Genome Project web page (http://dbuz. uab. cat, last accessed January 7, 2015). The obtained Fisher's exact test P-values were not adjusted but, from the total gene ontologies with P < 0.
05, only the top 1 3 with the lowest P-values were considered as enriched. Regarding the ChIPseq data, a handmade script was used to perform the differential enrichment analysis between hybrids and both parental species. Briefly, the "Regularized log" transformed counts obtained using DESeq2 for each chromatin mark were considered and analyzed using the following linear model for each gene: RldKm $ RldKi þ species (RldKm: log-transformed counts for the considered histone mark, RldKi: log-transformed counts for the input). We considered contrasts between pairs of species modalities (D. buzzatii, D. koepferae, hybrid). Because of the high number of tests and the lack of extreme P-values, the Benjamin and Hochberg  (Benjamini et al. 2001 ) adjustment of the P-values had a high effect in our results.
Allele-Specific Expression Analysis We created a de novo transcriptome with the RNAseq of both parental species, together with the hybrids using Trinity v2. 9  (Grabherr et al. 2011). A SuperTranscript  (Davidson et al. 2017)  was then created as a general reference transcriptome. The GATK pipeline for variant calling (Van der Auwera and O'Connor 2020) was used to detect differences between the general reference transcriptome and each parental species. The VCF files were filtered following the GATK guidelines, including a coverage depth of at least 10, and the exclusion of variants only present in one replicate, which were considered assembly errors. These variants were replaced in the general reference transcriptome using the FastaAlternateReferenceMaker GATK tool (Van der Auwera and O'Connor 2020) to create a reference transcriptome for each parental species.
Acknowledgments The authors wish to thank  Victor G amez  and  Judit Salces-Ortiz  for their contribution to the experimental part of this work. They want to thank as well to  Daniel Siqueira de Oliveira  for his contribution in the DESeq pipeline and  Mariana Galvao Ferrarini  for her advises in GO analyses. Finally, they wish to thank  Lars Ootes  for his advices with the allele-specific expression analyses and two anonymous reviewers for their valuable comments on the manuscript. This work was supported by grants  CGL2017-89160P  from with the  European Union FEDER  funds),  2017SGR 1379  from  Generalitat de Catalunya , and  ANR  ExHyb from the  French National Research Agency. A. B. was supported by a  PIF predoctoral fellowship  from the  Universitat Aut onoma de Barcelona (Spain).
Dataset characteristics The cleaned and pre-processed dataset we consider in this paper contains 254,410 user profiles and 3,656 million measured RSSI for the following 13 countries (ordered from the highest to the lowest number of measurements): France, the United States, Italy, Germany, Canada, the United Kingdom, Switzerland, Belgium, Spain, the Netherlands, India, Australia, and Brazil. Although Brazil accounts for only 0. 5% of all measurements, it still includes 21. 6 million measurements and 2668 unique users. In this dataset, Wi-Fi represents 58. 3% of all measured RSSI, Bluetooth 6. 6%, 2G 10. 5%, 3G 7. 6%, and 4G 17%. This dataset is the result of multiple cleaning and pre-processing steps on the raw data obtained from the ElectroSmart app, which we discuss in Appendix A.
Such a bias exists for all studies recruiting volunteers to perform exposure measurements, as it is impossible to recruit users with their consent without interest in the subject. Nevertheless, our sample is representative of the world-wide population in two aspects: urban/rural ratio and age. We discuss these two aspects in the following. To evaluate the users geographic location, we used OpenStreetMap's Nominatim  (Nominatim, 2021)  to map each user GPS coordinate to a rural or urban area for each of the 13 considered countries. Then we compared the identified location with a ground truth found in the open data project of World Bank  (Worldbank, 2021). First, we observed a great stability over time of the number of users in urban areas in our dataset: for a given country, the evolution of the percentage of 1 EMF stands for ElectroMagnetic Field.
This is the most common term used by the general audience for radio frequencies. population in the urban area between 2017 and 2020 is at most 9%. This stability is confirmed by the World Bank open data. Second, when compared with the World Bank open data, we observed a difference in the percentage of users in an urban area higher than 10% for only five countries: Switzerland (11%), Canada (16%), France (17%), Belgium (23%), the USA (25%). We deem this difference is reasonable for our purpose because we still observe for all 13 countries a majority of users in the urban area, which is confirmed by the World Bank dataset. To evaluate the age distribution of our users we used the Google Firebase Analytics data available for ElectroSmart. Indeed, we embedded the analytics library in ElectroSmart from January 2019.
We collected the age distribution of all our users between January 2019 and December 2020 for each country and compared it to the demographic data we obtained from the Web site population pyramid (Population Pyramids, 2019) that is aggregating data from the United Nations. We considered 6 age groups:  [18, 24] ,  [25, 34] ,  [35, 44] ,  [45, 54] ,  [55, 64] , 65+. Firebase Analytics does not provide any information for ages lower than 18. Then we computed the difference of percentages of the population in each group between our user population and the ground truth provided by population pyramid. We found a difference lower than 10% for most age groups and countries with two notable exceptions. First, for 4 countries the age group  [35, 44]  is more represented: Italy (12%), Australia (13%), the United Kingdom (14%), the USA (14%).
Ethical and legal considerations We submitted the study protocol to our institutional ethical committee (Inria COERLE  (COERLE, 2021). They validated that the present study follows all required ethical and data protection standard as described below. ElectroSmart requires explicit user consent for all information collection and clearly states that all data could be used for scientific research. In particular, we are fully compliant with the European General Data Protection Regulation (GDPR) (GDPR, 2021). In addition, ElectroSmart is used anonymously by default, unless a user decides to provide an email address. The email address field is clearly identified as optional. All scans are associated with a unique user ID. This user ID is randomly generated on our server at the app installation time.
Estimating the electric field Strength from RSSI We obtain the electric field E in V/m from the measured received power RSSI in dBm with the formula: E = 9. 73f ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 50 × 10 P-30 10 √ c ̅̅̅ ̅ G √ (2) where G is the antenna gain, f is the frequency in Hz, P is the power in dBm, and c is the speed of light  (Liao, 1977). The antenna gain of the smartphone is unknown, so we assume an isotropic antenna (i. e. , G = 1). In our dataset, we have access to the cellular frequency f for serving cells only. Therefore, we only keep exposure scans with a serving cell containing a valid frequency (they represent 74. 5% of all exposure scans). We sum all the cellular RSSI 2  in each exposure scan and convert the summed RSSI into V/m using the frequency of the serving cell.
Wi-Fi regulation Wi-Fi is a generic term that gathers together a large number of standards covering a wide spectrum of frequencies in the 2. 4 GHz and 5 GHz bands. For Wi-Fi, the goal of regulation is to reduce interference by limiting the maximum transmission power. This limit might be different for each country and each frequency. Getting a consolidated view of the various international regulations on Wi-Fi is tricky. For this purpose, we rely on the efforts of J. W. Linville and S. Forshee, who maintain a consolidated file containing the Wi-Fi emitting power per country and frequency for the Linux kernel ("Wireless regulatory database for CRDA" 2021). To understand the impact of regulation on exposure, we focus on two frequency bands that include a large enough number of countries using different regulations: 2.
Extracting home location To accurately identify the home location, we limit our analysis to dense user defined as those with at least 14 days of data in a specific month and at least 80% hourly sample density. To calculate sampling density, we count the number of hours between the first and last day we see a user in a given month. An 80% hourly sampling density means that the user has at least one exposure scan for 80% of the counted hours. In our entire dataset, we have 22,907 dense users, which is 9% of all users. Finally, we use the DBSCAN algorithm  (Ester et al. , 1996)  (∊ = 100 m, minPts = 24, distance = haversine) on the GPS coordinates of the dense users for each month, independently. We label the cluster that most frequently appears between 10PM and 8AM as the home cluster. All the other clusters are labeled "out-of-home".
Position with respect to related works Understanding the potential human health impacts of exposure to radio frequencies is a long journey. An important challenge in performing sound epidemiological studies is the complexity of characterizing the real exposure of the population. The methods and dataset we present here offer the first analysis of the evolution of radio frequency exposure at population-scale for 13 countries over four years. This change of paradigm from previous small-scale studies  (Zeleke et al. , 2018; Ramirez-Vazquez et al. , 2019 , 2021; Bhatt et al. , 2018; Birks et al. , 2018; Gallastegi et al. , 2018; Lahham and Ayyad, 2019)  has direct consequences for the current debate on population exposure and the impact of this exposure on health. Importantly, we do not oppose our work to related works, but argue it comes in complement.
Beyond these direct implications, we envision our work and dataset providing a foundation for future epidemiological studies. Upon publication, all data used in this paper will be available online for scientific exploitation. The data consists of timestamped measurements of RSSI for each of the five types of signals considered in this paper (Wi-Fi, Bluetooth, 2G, 3G, 4G). All user IDs have been anonymized (using a salted hash), and all GPS locations have been replaced by one of the 13 countries we consider. When required to preserve user anonymity, we provide aggregated data using pre-processing steps. For instance, we provide the identification of the unique physical sources using our own anonymous source counter. A detailed description of the format of the data will be available on the online publication site upon publication.
Limitations We performed all scans with a vanilla version of Android using the regular Android API. That is, we did not have access to low-level data available from rooted smartphones or customized drivers. This approach is beneficial for targeting a large-scale audience, but it limits what we can measure, as elaborated below. First, we only measured the downlink received by the measuring smartphone. Therefore, the contribution of the uplink to the exposure, that is, the emission of the measuring smartphone, is not considered in this study. Also, we did not measure the uplink of surrounding devices. This is an important limitation, but we are not aware of any technical solution to measure the uplink at scale. We argue that having a consistent view of the downlink at scale is already an important and significant step forward.
Acknowledgement The authors wish to thank  Sandra Matz  for feedback on this work, as well as  Mondi Ravi  and  David Migliacci  for their central work on the ElectroSmart app. This work has been supported by the  French government , through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01 , and based upon work supported by the  National Science Foundation  under Grant No. 1761810.
With the recent surge of single cell RNA sequencing datasets (scRNAseq) the extent of cellular heterogeneity has become apparent, yet it remains poorly characterized on a protein level in brain tissue and induced pluripotent stem cell (iPSC) derived brain models. With this in mind, we developed a highthroughput, standardized approach for the reproducible characterization of cell types in complex neuronal tissues. We designed a flow cytometry (FC) antibody panel coupled with a computational pipeline to quantify cellular subtypes in human iPSC derived midbrain organoids. Our pipeline, termed CelltypeR, contains scripts to transform and align multiple datasets, optimize unsupervised clustering, annotate cell types, quantify cell types, and compare cells across conditions. We identified the expected brain cell types, then sorted neurons, astrocytes, and radial glia, confirming these cell types with scRNAseq.
FC is a fast, quantitative, and robust method, used widely in immunology and cancer research  [12] [13] [14]  but to date only sparsely in neuroscience. Typically in neurobiology, only two or three antibodies are used to distinguish between pairs of cell types  15, 16  or to enrich one cell type  17, 18. Traditional FC analysis methods using FlowJo, the commercial FC analysis software package that is currently the standard in the field, are time consuming and subject to user error. Methods to standardize data preprocessing and analyze combinations of more than 3 antibodies in one experiment are starting to emerge  19, 20. However, no methods are available to automate cell type annotation in FC from complex tissues such as brain or 3D brain organoids using a large antibody panel. To create such an analysis framework, we produced an experimental dataset using cultured hMOs differentiated from human iPSCs  1, 21, 22.
Comparison of cell types between iPSC lines and hMO batches After annotating a subset of 9000 cells from each of the nine hMO samples, we next analyzed the total available cells (Table  S1 ). We again followed the CelltypeR workflow, but now we used the labelled subset of cells to annotate the full dataset. Using the subset of annotated cells, we trained a random forest classifier model (RFM) (Figure  S11 ) and then applied those labels to the complete dataset to predict the type of each cell. We clustered the full dataset and visualized the predicted cell type annotations using RFM, CAM and Seurat 27 label transfers (Figure  S12 ). To annotate the cells in the full dataset from the nine MOs, we applied the CelltypeR tools using three methods of cell type prediction and inspection of expression levels in each cluster in UMAP and heatmap visualization (Figure  S13 ).
We observe the same cell types in the full dataset as in the subset of data; however, we now identify a tiny cluster of OPCs, and more stem cell-like cells (Figure  4A ). Using the cell type predictions from the RFM and the Seurat label transfers we now have an indication of the radial glia cells within the 'unknown' and 'mixed' clusters (Table  S4 ). Based on the visualization of markers, the 'mixed' cluster has cells of glial lineage, and the unknown cluster has cells with a neuronal lineage (Figure  4A ). Subgroups of neurons and glia are clearly defined by different expression patterns of the antibody panel (Figure  4B ). In the full dataset we observe more subgroups of the main cell types (Figure  S14 ). Visualizing the distribution of cell types in hMOs derived from each cell line, we can see there are some differences in the proportion of cell types (Figure  4C, D ).
Isolating populations of interest identified by CelltypeR clustering analysis After annotating the dataset, we could plot the proportion and mean expression of each antibody marker in each group to try and define the relative marker expression of a given cell group and then isolate that population by FACS. However, manually reverse engineering a gating strategy is difficult with more than a few cell type markers. Thus, we defined cell types using CelltypeR, applied the package hypergate  28  to identify which combinations of antibody markers clearly define a given cell population, and then manually gated these cells in FlowJo (Figure  5A ). The gating accuracy for all cell types is above 95% (Table  S8  and S9 ). We next followed the CelltypeR workflow using the newly generated gated files to annotate the cells in the FlowJo gated populations (Figure  5B ).
Astrocytes. Cells were fixed 15 minutes at room temperature with 4% formaldehyde in PBS, followed by 3 washes of 5 minutes in PBS. Cells were permeabilized for 10 min at RT in blocking solution: 5% normal donkey serum (JacksonImmunoResearch Laboratories, West Grove, PA), 0. 1% Triton-X-100, and 0. 5 mg/ml bovine serum albumin (Sigma-Aldrich) in PBS. Cells were incubated for 1h at RT before overnight incubation at 4°C with primary antibodies: Glial Fibrillary Acidic Protein (GFAP) (1/500 Dako Cat. Number Z0334); AQP4 (1/500, SIGMA, cat# HPA014784). Secondary antibodies were incubated 2h at, followed by Hoechst 33258 (1/5,000, Sigma) for 5 min, mounted with Fluoromount-G, and examined by fluorescence microscopy.
Data analysis and CelltypeR R library All computations were performed in R. We created a R library of functions to perform the analysis, CelltypeR. Our functions required functions from multiple other R libraries referenced in descriptions to follow. The R library can be found, along with workbooks for the complete workflow and generation of each figure, at https://github. com/RhalenaThomas/CelltypeR_single_cell_flow_cytometry_analysis Computational Workflow: 1. Data preprocessing: a. Read FlowJo files into R. b. Create a data frame with intensity measurements for each marker for all samples within the experiment to be analyzed. c. Harmonize data if desired. d. Create a Seurat single cell object for further analysis. 2. Creation of cell type clusters a. Clustering optimization to compare clustering methods and parameters and visualize results.
Data preprocessing The. fcs files without dead cells, debris, and doublets created in FlowJo are read into R and processed. The. fsc files contain area, width, and height of the fluorescence signal for each marker as well as the forward and side scatter of the light. Then R using the flowCore package is used (Hahne et al. , 2009). The area values for each channel are selected to represent the expression intensity for each antibody. All the. fsc files within one folder are read into into one R data object. A dataframe is created with the channels and saved for further use. Individual cell cultures and hMO organoid samples for testing the pipeline and gating were used in this raw format to create a Seurat single cell data object. For the hMO samples, the data was aligned to remove batch effects and technical variability.
Phenograph functions well, however we saw little difference to the Louvain using the Seurat library and proceeded to use the Seurat package for Louvain network detection to obtain clusters for ease of use with the overall workflow. We then proceeded to test the cluster stability at different resolutions, calculating the RAND Index and standard deviation of the number of clusters across 100 iterations of clustering with different random start points. The results informed the choice of cluster numbers to annotate.
Creation of the predicted expression matrix for antigen proteins in the antibody panel Astrocytes, oligodendrocyte precursors (OPCs), oligodendrocytes (Oligo), radial glia (RG), endothelial cells, epithelial cells and pericytes are all expected to be present in hMO tissue. Microglia are found in brain tissue but are not expected to be present in MBOs and thus were not included in the reference matrix. In our early tests we found that pericytes were highly overpredicted. We would expect very few if any of these cell types based on previous scRNAseq experiments. Pericytes are not well defined by the FC panel, and we decided to remove these from the reference matrix. We selected expression values for the 13 antigens target by the FC antibody panel. Not all antigens were available from all cell types or databases.
Input data was taken from the following public sources: protein expression data scored from the Human Protein Atlas (https://www. proteinatlas. org), bulk RNAseq from human  37  , scRNAseq data from human fetal midbrain and other brain tissue from the Human Cell Landscape  38  and cerebral organoids and primary human cells from the UCSC Cell Browser  39. For the antibody O4, the epitope is a glycoprotein, and the specific corresponding gene is unknown, however the gene NKX6. 2 is a marker of mature oligodendrocytes, with expression highly correlated to O4 protein detection. 40  Finally, the FC data acquired in this study from 2D cell cultures, iPSC, neural precursor cells, neurons, astrocytes, and oligodendrocytes. For each data set the values were z-scored then minmax normalized marker by marker to fit between 0 and 1.
Random Forest Model A data frame was created from cell type from the 9000 cells per sample subset of hMO data and the matching expression. The data was split into 50/50 into test and training data. The training data was input into the function RFM_train which uses the randomForest and the function caret for optimization. A range of number of variables randomly sampled in each split (mtry) from 1 to 10, the best mtry was 6. Ranges of other parameters were tested, and the optimal values were used in to train the final model: max nodes = 30, node size = 25 and number of trees = 1000. The trained model was then used to predict the cell type of each cell in the full data set and the new flow sorted data. The topmost predicted cell type for each cluster was used as the cluster prediction.
Label transfer using seurat We made a function that follows the Seurat workflow for label transfer combined into one function. The annotated Seurat object from the 9000 cells per sample subset of hMO data was used as the reference data and the full dataset and FACS sorted datasets were used as the query objects. Anchors were found between the two objects using 25 principal components to predict the cell types, the max prediction was selected for each cell in the query data. No threshold for predictions were set. The most frequently predicted cell types within each cluster were used as the cluster predictions.