dataesr/scientific-paragraphs-categorization huggingface dataset:
Again, we observe that when considering all HVGs we see a larger increase of eGene discoveries from d-mean to dr-mean (19% increase compared to the initial 16%; Table  S4 ). We again validated these results using simulated data based on expression statistics from the neuron differentiation 10X dataset to estimate key parameters used in the simulations (see Methods for details). The simulations confirmed that donor-run level aggregation also resulted in a lower empirical FDR on 10X data (paired t-test: p=0. 002). They also confirmed that median aggregation performs very poorly on 10X data, however there was no significant difference between mean and sum aggregation on power (p=0. 46, Fig. S6 ; see results Table  S2 , see detailed statistical analysis Table  S5 ). Consistent with results from the Smart-Seq2 simulations, eQTL mapping on the 10X datasets with a smaller sample size resulted in a greater difference in performance, however here sum aggregation performed better on the smaller datasets (Fig.
Again, we observe that the replication rates are comparable to the baseline results (kinship/replicate structure only). For both the d-mean and dr-mean results the 1/#cells random effect seems to work better than 1/#reads. Next, we assessed the impact of the inclusion of the SV measure on eQTL mapping in the 10X dataset. Given the lower read-depths per cell in 10X versus Smart-Seq2, we expected a stronger effect in this setting. However, for the d-mean results we observe a slightly smaller (~4%) increase in eGene discovery than was observed in the iPSC Smart-Seq2 results (Table  S7 ). For the drmean based methods, where we include both kinship and SV, we observe again a stronger increase in eGene discovery of around 20%. Like for Smart-Seq2, the SV effect based on 1/#cells allows for the identification of more eGenes than the 1/#reads-based random effect.
The use of the p-values from the same study provided the largest increase in the number of eGenes, but the use of the metasoft p-values from the GTEx tissues is a close second (1,898 vs 1,859 eGenes). Of importance here is that we subsetted down to only consider genes tested in all datasets, to be able to compare between cFDR conditionings. When assessing the impact per reference p-value set, we see that leveraging the joint cell types from the Cuomo et al. study yields a comparable number of discoveries as using the m-bulk data (2,821 vs 2,887 eGenes), again with comparable replication fractions (Table  S10 ).
Finally, we tested median aggregation, but given the sparsity of the single-cell data this method drastically decreases the number of genes that can be assessed for eQTL mapping. Therefore, the use of median aggregation is not a sensible method in most single-cell applications. Next we assessed the effects of both covariate and sampling variation correction approaches. First, the effect of the different methods to identify covariates, i. e. data driven factors that capture unwanted variation in eQTL mapping, is ambiguous. Adding covariate factors of any method increases eGene discovery power substantially, but the results for the different tools when using the optimal number of factors for that tool are broadly comparable both in terms of discovery and number of replicated effects. Correcting for 15 PCs yielded the most eGenes at a high replication rate.
Importantly, the bulk eQTL results we use to assess replication were mapped after correcting the bulk RNA-seq data using 20 PCs, which might bias the replication for the sc-eQTL mapping to be more similar to the bulk PCA based results. However, regardless of the replication rate in bulk, PCA-correction yielded the highest number of eGenes and so remains a sensible "default" choice for defining covariates capturing unwanted variation. Second, including sampling variation as a random effect in the models results in an increase in eGene discovery power on both Smart-Seq2 and 10X datasets, especially when using the 1/#cells-based random effect. Finally, we explored the use of bulk eQTL mapping statistics to guide our multiple testing correction using the conditional false discovery rate (cFDR) in the implementation by Liley et al.
We observe that the cFDR method gives a marked increase in the identified eGenes (between 21% and 40% increase), both when matched bulk data is available and when meta-tissue eQTL summary statistics are used. However, using the larger iPSC dataset we found cFDR had limited impact in terms of replication rates of the eQTL. While cFDR allows us to guide the multiple testing, potential cell type-specific effects that are borderline significant might not be "boosted" in power by cFDR if those cell types are not present or lowly represented in the bulk references. We did, however, observe that all the effects found using standard FDR methods (Storey Q, Benjamini Hochberg) were also deemed significant using cFDR, so no potential effects were lost by using the guided FDR approach. We explored the use of cFDR for other single-cell studies, but more tests are needed for mixed cell types to find the optimal way to use cFDR in sc-eQTL studies.
Single-cell RNA-seq data processing The Cuomo et al  [12]  study describes in detail the differentiation of iPSCs towards definitive endoderm using a three day differentiation protocol. Briefly, the iPSC lines were multiplexed into 24 experimental pools (4-6 lines in each pool) and single cells were sequenced using the Smart-Seq2 protocol  [33] ). Cells were assigned to their donor of origin using Cardelino  [7]. Here, we focus on the day0 cells (i. e. iPSC stage) that were successfully assigned to a donor and passed donor-and cell-level QC described below. First, we quantified gene expression levels in line with the bulk study. We mapped RNA-seq reads to the genome (hg19) using STAR mapping and used ENSEMBL v75 for annotation. Expression levels for each gene were counted using featureCount (subread v1.
After QC and selection the number of cells per donor varied from 5 to 383 iPSCs per donor (mean=84. 8) and the number of reads per donor varied from 1 to 104 million (mean=42 million). For reference, the average number of reads per donor in our bulk data is 44 million, but the range is much smaller (24-98 million reads per person, Fig. S1 ). The two other developmental stages from Cuomo et al. , i. e. mesendoderm and definitive endoderm are used as a reference set for the cFDR analysis. For these two developmental stages and cell-QC, both as described in the original publication. However, gene expression quantification was performed in line with the single-cell iPSC data described above. Bulk RNA-seq data processing The HipSci project generated both chip-genotype and deep bulk RNA-sequencing profiles for 810 iPSC lines corresponding to 527 unique donors.
Briefly, sequencing data generated from Chromium 10X Genomics libraries were processed using the CellRanger software and aligned to the GRCh37/hg19 reference genome. Counts were quantified using the CellRanger "count" command, with the Ensembl 84 reference transcriptome  (32, 738  genes) with default QC parameters. For each of 13 pooled experiments, donors (i. e. cell lines) were demultiplexed using demuxlet  [9] , using genotypes of common (MAF>1%) exonic variants available from the HipSci bank and a doublet prior of 0. 05. The different eQTL mapping methods tested on simulated data were replicated 10 times, with each replicate being an independent splatPop simulation. Due to genetic linkage, the top eSNP mapped for each eGene was not expected to be the simulated eSNP. To account for this, all eQTL hits with an empirical p-value below the empirical p-value corresponding to the multiple testing threshold determined using Storey Q on the top hits for each gene were considered significant.
Sum aggregation was performed directly on the raw counts (Fig. 1 ), and followed by pseudobulk-like TMM normalisation  [24]  and log2-transformation, as implemented in edgeR  [28]. In all cases, aggregation was performed at two levels of batch (Table  1 ). First, we aggregated all cells from each donor (i. e. d-mean, d-median, d-sum). In this setting, one sample corresponds to one donor (n = 87 in the iPSC Smart-Seq2 dataset, n = 174 in the FPP 10X data; samples with > 5 cells only). Next, we aggregated separately across donors and sequencing runs (dr-mean, drmedian, dr-sum). In this second setting, one sample is a unique donor-sequencing run combination (when considering samples with > 5 cells, n = 155 in the iPSC Smart-Seq2 data, n = 702 in the FPP 10X data).
All covariates (except LDVAE, see below) were calculated using the relevant aggregated expression values at the level of donor or donor-sequencing run, for all genes tested (63,678 genes). PCA was computed using the R function prcomp. PEER (https://github. com/PMBio/peer/) was run using the R implementation with the number of factors set to 30 and default parameters. MOFA v2 (https://biofam. github. io/MOFA2/) was run using the R implementation, for n=25 factors. Both default parameters (i. e. MOFA; spikeslab_factors = F, spikeslab_weights = T, ard_factors = F, ard_weights = T) and parameters with the sparsity constraints removed (i. e. MOFA non-sparse; spikeslab_weights = F, ard_weights = F) were used. Finally we included a recent approach using variational auto-encoders: linearly decoded variational autoencoder (LDVAE;  [37] ).
For the cFDR procedure we tested using raw association p-values from 1) the bulk iPSC associations, 2) GTEx v7 association p-values from the EBV transformed lymphocytes, 3) GTEx v7 association p-values from tissue meta-analyses results  [55] , and 4) association p-values from a joint eQTL mapping on the mesendoderm and endoderm data from the Cuomo et al study  (Cuomo et al. 2020). Given that the external datasets have imperfect overlap in terms of both SNPs and genes, we limit our focus to overlapping genes and variants for each respective test, unless otherwise specified. Conditional FDR is a method to condition or transform the p-value from one hypothesis test using an external set of test statistics. For a series of hypothesis tests (i = {1,. , n}), P = {Pi} and Q = {Qi} are two sets of random variables, representing the observed p-values from the internal (i.
Acknowledgements We would like to thank the sc-eQTLGen consortium for their input and useful discussions. We would like to thank  Loukia Georgatou-Politou  and  PuXue Qiao  for their help on applying cFDR in the context of single-cell eQTL mapping. We thank  Oliver Stegle ,  Joseph Powell ,  Monique van der Wijst  and  Urmo Võsa  for useful discussion, comments and edits to the manuscript.
Data and code availability The eQTL mapping pipeline is available via: https://github. com/single-cell-genetics/limix_qtl. splatPop is available in splatter v 1. 14. 1 on Bioconductor. The description of how to implement splatPop is available in the package vignette (http://www. bioconductor. org/packages/devel/bioc/vignettes/splatter/inst/doc/splatPop. html). The HipSci genotype information is made available per sub study and are available via: The single-cell iPSC, mesendoderm and endoderm RNA-seq reads are available via: ERP016000, EGAS00001002278. The bulk iPSC expression data is available per sub-study via: EGAS00001000529, EGAS00001000593, EGAS00001001137, EGAS00001001318, EGAS00001001727, EGAS00001001986, EGAS00001001987, EGAS00001001988, EGAS00001001989, EGAS00001001990, EGAS00001001991, EGAS00001001992, EGAS00001001993, EGAS00001001994, EGAS00001001995, EGAS00001001996, EGAS00001001997,.
ERP007111 The FPP 10X data is available as part of the data published with (Jerber et al) at:https://zenodo. org/record/4333872#. YEJXnC2l2L4. Specifically, we considered the day 11 object only, and subsetted to the cell type of interest: FPP (floor plate progenitors). Finally, bulk and single-cell iPSC count data from this manuscript specifically are available at: https://zenodo. org/record/4585385.
, 2019). As BLAST requires participants to process fast repeating visual stimuli to produce accurate motor responses, we reasoned that the task could be combined with a memory-retrieval task to investigate the neural mechanisms of DT interference in situations that require attention to be split between external and internal information. This division is found in many real-life DT situations, for instance when a driver searches for an answer to a passenger's question. To maximize ecological validity, we recorded untrained human participants as they performed BLAST in isolation or combined with a self-paced Verbal Fluency Task, in direct interaction with an experimenter. We report for the first time in humans how two tasks interfere at the local neural level, through incompatible neural dynamics in cortical regions supporting language, visual perception, and cognitive control.
BLAST (T1) The continuous attention task was adapted from the BLAST paradigm published by our group  (Petton et al. , 2019)  to detect transient failures of executive attention. In short, BLAST repeatedly asks participants to find a target letter (the Target) in a subsequent two-by-two square array of four letters (the Array), with new letters every trial (Target and Array; Figure  1B ). Each trial starts with the central presentation of the Target for 200 ms, followed by a mask (# sign) for 500 ms, until the presentation of the Array which remains on screen until the manual response, or for a maximal duration of 3,000 ms. The next trial starts after an 800 ms pause, with no visual or auditory feedback of any kind. Stimuli were delivered on a PC using the Presentation software (Version 18.
Apart from providing new category names, the experimenter remained still and silent throughout the experiment. VFT performance was analyzed offline from a video recording of the session. To mimic naturalistic conditions, participants received no instruction to prioritize BLAST or the VFT. In addition, this study makes use of several datasets recorded in separate sessions while participants performed short functional localizers designed to identify iEEG sites involved in visual perception, auditory perception, semantic and phonological processing, verbal and visuospatial working memory, as well as visual attention respectively  (Vidal et al. , 2011; Ossandón et al. , 2012). Although those data cannot be fully described in this focused article, we will refer to them each time they provide important insights regarding the function supported by the cortical sites of interest for our study.
Bipolar HFA Extraction The data were recorded with a standard 128-channels acquisition system (Micromed, Treviso, Italy), bandpass filtered online from 0. 1 to 200 Hz, and sampled at 512 Hz in all patients. At the time of acquisition, the data were recorded using a reference electrode located in white matter, and the signal in each recording site was subsequently re-referenced to an adjacent channel on the same electrode (bipolar montage). A bipolar montage reduces signal artifacts common to adjacent electrode contacts (linenoise and distant physiological artifacts) and improves the spatial resolution of the recording to a few millimeters  (Lachaux et al. , 2003; Jerbi et al. , 2009) -slightly from different from subdural grid electrocorticography  (Jerbi et al. , 2009) -by canceling out effects of distant sources that spread equally to both adjacent sites through volume conduction.
It might complicate functional connectivity analysis based on phase estimation  (Arnulfo et al. , 2015) , but no such analysis was performed here. The frequency band of interest [50-150 Hz] was defined from preliminary time-frequency (TF) analyses of the iEEG data using wavelets  (Tallon-Baudry et al. , 1997) , performed with an in-house software package for electrophysiological signal analyses (ELAN) developed at INSERM U1028, Lyon, France  (Aguera et al. , 2011) , and from previous studies by our group  (Jerbi et al. , 2009). Raw data were extracted by using homemade Matlab scripts (The Mathworks, Inc. ) and transformed into HFA time-series with the following procedure  (Ossandón et al. , 2012; Perrone-Bertolotti et al. , 2012). Continuous iEEG signals were first bandpass-filtered in multiple successive 10 Hz wide frequency bands (e.
Anatomical Display The precise anatomical location of the electrodes (and their MNI coordinates) were obtained by aligning each patient's pre and post-implantation MRIs using the NUTMEG toolbox  (Dalal et al. , 2004)  and IntrAnat  (Deman et al. , 2018) , a specific toolbox interfacing with the BrainVisa software (IntrAnat Electrodes, GIN INSERM U1216, Grenoble, available at: https://ftract. eu/index. php/softwares/). Participants' behavior was recorded and inspected using an in-house software-BrainTV Replay-while anatomical representations were made using HiBoP, also developed by our group and available here: https://github. com/hbp-HiBoP/HiBoP/releases/tag/2. 2. 3a. BrainTV Replay is an updated version of our original BrainTV software  (Lachaux et al. , 2007 (Lachaux et al. , , 2012;; Vidal et al.
Precentral Cluster We found effects in the Precentral Gyrus (1 LH, 1 RH, 2 patients) and in the Precentral Sulcus (2 LH, 1 RH, 3 patients) with similar neural responses in all sites. The optimal T1 response was characterized by two separate phases of activation until the manual response (Figure  3 ). In the DT condition, a strong and sustained activation was mostly present during the bad trials (i. e. , slowest reaction time and/or higher error rate). Recordings from the same sites during additional tasks (the functional localizers) revealed a strong activation during a rhyming task emphasizing phonological processing, and during a verbal working memory task (see Supplementary Figure A,  Vidal et al. , 2011). These are strong indications that our electrodes sampled a region specialized in covert speech and verbal rehearsal, used by some patients to encode the target letter in BLAST.
Network Analysis (and the Possibility of Indirect Interference) Our visualization software, BrainTV Replay, allows for the joint review of the HFA time series of two sites, as superimposed curves synchronized with video recordings, which is extremely convenient to detect high-correlation episodes visually (Figure  11 ). During the preliminary visual review of the data, we were immediately struck by extremely high correlations between HFA time fluctuations of specific sites distant from each other. This motivated a quantitative search for significant functional coupling between the sites reported above when several clusters could be recorded in the same patient. The analysis revealed patterns of very high and significative correlation episodes for specific verbal responses and cortical areas (as illustrated between the left Precentral cortex and the left BTC in Figure  11 , left panel); however, few of them were systematic enough to be reported.
Dynamical Incompatibility We could show that the DT cost can be understood as a deviation from an optimal spatio-temporal dynamics, due to neuroelectric interferences in the cortical network supporting the primary task, BLAST. Interferences were observed throughout the broad, domain-general, Multiple-Demand Network (MDN;  Rushworth et al. , 2005; Cole and Schneider, 2007; Dux et al. , 2009; Crittenden and Duncan, 2014) , including the DLPFC (the Inferior Frontal Sulcus and the Middle Frontal Gyrus), the Anterior Insula, the ACC/preSMA, and the Precentral Gyrus, as well as in process-specific areas: in the language network and high-level visual areas such as the Visual Word Form Area or the Fusiform Face Area, respectively. The temporal precision of iEEG revealed a situation of ''dynamical incompatibility'' with the VFT: a concept that we introduce to explain why two tasks can be mutually exclusive in naturalistic conditions despite the theoretical possibility of multiplexing.
This ensures the quality and accuracy of the data. During more than 10 years of AMS construction, a large international group of physicists have developed a comprehensive Monte Carlo simulation program for AMS. This program is based on the geant4 package  [37]  and it is constantly being improved with the AMS data obtained on the ISS. This Monte Carlo program simulates electromagnetic, hadronic, and nuclear interactions of particles and nuclei in the material of AMS, namely: Fig. 1. AMS is a unique precision magnetic spectrometer on the ISS. AMS will operate on the ISS for the Station's lifetime. It is mounted on the ISS with a 12 degree angle to the zenith to prevent that the rotating ISS solar arrays are in the AMS field of view. Fig. 2. The AMS detector showing the main elements and their functions.
Anticoincidence counters (ACC) The Anticoincidence (or veto) counters (ACC)  [50]  surround the tracker inside the magnet bore. Their purpose is to reject particles that enter or exit the tracker volume transversely. As shown in Fig. 23 , the ACC consists of sixteen Fig. 19. Proton rejection power of the TRD measured from ISS data at 90% and 65% electron and positron efficiency. At 90% e ± efficiency, the measured proton rejection is above 1000 in the energy range 2-200 GeV/c. At higher rigidities, the rejection power can be improved by tightening the e ± selection. curved scintillator panels of 800 mm length, instrumented with embedded wavelength shifting fibers to collect the light efficiently. The light from the two ends of the fibers from two adjacent panels are routed to two photo-multiplier tubes.
Note that if saturation effects are not included in the Monte Carlo simulation, its prediction is significantly above the observation  [53]. The techniques outlined above provide AMS with a precision energy measurement of electrons and positrons up to multi-TeV as well as a coordinate resolution of ∼200 µm and an angular resolution of 0. 3 degrees at 1 TeV  [53]. The ECAL energy resolution is measured using the beam test data in the energy range from 10 to 300 GeV and the Monte Carlo simulation beyond this range. Fig. 28  shows the ECAL energy resolution as a function of the e ± energy compared with the Monte Carlo simulation.
Dedicated circuits were implemented for each detector -the tracker, TRD, TOF+ACC, RICH, and ECAL -to collect and digitize the signals. When a trigger occurs, the data volume of ∼3. 6 Mbit per event is processed. On the ISS, the trigger rates vary from 200 Hz near the Equator to a maximum of ∼2000 Hz near the Earth's magnetic poles, see Fig. 31. The dead time due to the digitization leads to an additional inefficiency which is accounted in the analysis. Within the South Atlantic Anomaly (SAA), the trigger saturates and the event rate drops but the detectors are unaffected. These data are not used in the analysis. Overall, the average data acquisition efficiency is 86% resulting in an average event acquisition rate of ∼600 Hz. Fig. 32  shows the data flow between the ISS and the AMS Payload Operation and Control Center (POCC) at CERN and then to the AMS Asia POCC in Taiwan.
Data from AMS are transmitted at an average rate of 10 Mbit/s from the ISS to a geosynchronous Tracking and Data Relay System (TDRS) satellite and down to the White Sands Ground Terminal in New Mexico. To cover the ISS orbit, there are several TDRS satellites in use sequentially. From White Sands the data are directed over NASA networks to dedicated AMS ground support computers located at the Marshall Space Flight Center (MSFC), Huntsville, Alabama, where the raw data are written on disk. Then the data are copied to the AMS POCC at CERN over the Internet. Commands from the POCC to AMS follow the same path in reverse. A dedicated AMS Laptop is located in the astronaut quarters onboard the ISS which archives all AMS data for up to two months. Whenever there is a dropout anywhere along the chain between the ISS and the POCC, the missing data are recovered later from this AMS Laptop.