dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, only 15 of the 39 aberration hubs, which were connected to breast cancer based on previous literature, have significant differential expression at mRNA level or a mutation. This further illustrates that AbHAC is capable of identifying relevant factors that cannot be identified directly by interrogating mutational or expression patterns of genes. Our further analysis showed that 53 of the 74 breast cancer aberration hubs are specific to one of the PAM50 subtypes in a given aberration category (Table  2 , Supplementary Table  9  and Supplementary Figure  2 ). Pathway analysis of these proteins, using KEGG datasets, revealed that aberration hubs found in luminal breast tumors are enriched in PI3K-Akt pathway and FOXA1 regulatory network (Supplementary Table  10 ), in line with a high prevalence of PIK3CA and FOXA1 mutations in this subtype  [22, 23].
In addition, hubs of basal-like subtype are enriched in DNA replication and cell cycle pathways (FDR < 8. 75 × 10 -5 ), which confirms previous reports on importance of these pathways in basal-like tumors  [24]. Specifically, we observed several components of "origin recognition complex" (ORC) including ORC1, 2, 3, 5 and 6, and "mini-chromosome maintenance" (MCM) such as MCM3, 6 and 7, which are important factors for initiation of genome   10 ). Among the 74 aberration hubs we identified in TCGA breast cancer datasets, 45 were not affected by somatic mutations or significant changes at mRNA levels in any of the PAM50 subtypes. We hypothesized that at least some of these factors may be deregulated at translational or post-translational levels. Therefore, we investigated the status of these aberration hubs at protein level using the TCGA proteome and phosphoproteome data for breast cancer  [25].
Aberration hubs in renal cancer We applied the same analysis to ccRCC using information about somatic non-silent mutations and abnormal gene expression patterns that have been recently identified through the CAGEKID program  [17]  (Supplementary Tables  7 8 ; see Methods for details). AbHAC analysis of these datasets identified 47 aberration hubs in renal tumors (FDR < 0. 05; Supplementary Table  12 ). We noticed that, although identified independently, several of these aberration hubs were connected together through protein-protein interactions and formed a protein network that included several known cancer driver factors such as MYC, EGFR, PIK3C2B, CDK1, and KDR (Supplementary Figure  3 ). Further analysis of this network revealed 25 inter-connected aberration hubs that were enriched in up-regulated genes among their interacting partners.
This permutation step provides a statistical confidence for the data generated by AbHAC by accounting for complex dependencies of any input dataset, and thereby distinguishes our method from the existing ones. For example, the DIAMOnD algorithm  [33]  follows a similar procedure to connect known disease genes in the protein interaction network by identifying disease modules; however, it lacks a statistical component to assign the level of significance for each observation, and to measure the false discovery rate associated with each analysis. We analyzed the same interactome and genesets (aberrantly expressed or mutated) with both AbHAC and DIAMOnD. For AbHAC we used FDR < 0. 05 to identify aberration hubs, and for DIAMOnD we chose the first 100 top hits, which is larger than the maximum number of significant aberration hubs identified by AbHAC in any of our analyses.
The correction for multiple testing provided a high confidence for the data generated by AbHAC. For example, only four proteins (INCENP, MIS12, CDK1, COL5A1) were common in the list of aberration hubs identified through the same analysis applied to breast cancer (with total of 74 aberration hubs) and renal carcinoma datasets (with total of 47 proteins aberration hubs), indicating that AbHAC is capable of finding proteins that are specifically deregulated in a given cancer. The above molecules identified in both breast and renal cancers have already been implicated in multiple cancers based on previous literature. For example, CDK1 is shown to be an inhibitor of FOXO1, which is known as a common tumor suppressor in different types of human cancer  [34]. Accordingly, the additional factors identified in this study may present novel relevant players in breast cancer.
Our results showed that several proteins with key roles in the initiation of genome replication are significant aberration hubs of basal-like tumors, shedding light on the underlying molecular mechanisms of this aggressive tumor entity. Importantly, the careful selection of genes as input for AbHAC analysis assists in the identification of functional-relevant aberration hubs. Applying analytical methods such as MuSiC  [35]  or MutSigCV  [36] , which identify significantly mutated genes, to cancer sequencing data is an appropriate approach to exclude genes affected with passenger alterations from the input list for AbHAC analysis. To assess the reproducibility of AbHAC results, we applied this method to an additional independent genomic datasets of renal (TCGA) cancer and compared the results to our preceding findings in CAGEKID datasets.
This analysis showed a strong correlation (Pearson R > 0. 7, p < 10 -16 ) between results of AbHAC analysis of these datasets (Supplementary Figure  6 ), suggesting that AbHAC results are not database-specific. Furthermore, we systematically assessed the extent of possible dependency between the deregulated neighborhood of an aberration hub and deregulation of the hub itself. We compared the AbHAC p-value of proteins and the differential gene expression level of their corresponding genes in the above-mentioned datasets. We did not observe a significant correlation between AbHAC p-value and the extent of deregulation at mRNA level for a given molecule (Supplementary Figure  7 ). Overall, the AbHAC p-values are not significantly influenced by expression levels of the corresponding genes. In other words, aberration hubs are not necessarily affected by extreme abnormal gene expression themselves.
According to the close cross-talk between VEGF receptor and Src signaling  [39] , inhibition of aberrantly regulated proteins of the Src pathway may provide an additional route to control of tumor angiogenesis. Notably, Src signaling is also involved in the regulation of PI3K-Akt-mTOR pathway, which has been recognized as a relevant therapeutic target for ccRCC  [40]. Collectively, our results from analyzing two cancer datasets highlighted proteins whose relevance to the studied cancers is supported by previous literature, and provided a list of potentially novel relevant factors for further investigation in future studies. Similarly, AbHAC can be applied to analyze molecular aberrations of other cancers, and other diseases in general. Likewise, the method can be used to study proteome profiles or combinations of different layers of genome and proteome information as well.
Software packages CRAN project R software  [41]  has been used for most of the analysis presented here. Some of the analysis and figures have used the following CRAN and Bioconductor packages; ggplot2  [42] , limma  [43] , edgeR  [44] , PSICQUIC  [18]  and Uniprot. ws [45]. Figures of interaction networks were generated using cytoscape  [46].
Protein interaction network Using PSICQUIC  [18]  and UniProt. ws Bioconductor packages, we acquired the protein interaction network of all the Uniprot accessions from the following databases for Homo sapiens: DIP, InnateDB, IntAct, MatrixDB, MINT, I2D-IMEx, InnateDB-IMEx, MolCon and BindingDB. We filtered out interactions that had been reported only based on colocalization experiments. This left us with 11,851 proteins in total that have an average of 10 interaction partners (median of 3). All duplicated interaction partners were removed in this undirected protein interaction network.
Datasets For the breast cancer analysis, we used the publicly available TCGA breast cancer datasets of somatic mutations and microarray expression profiles for 497 patients grouped into the 4 major PAM50 subgroups (basal-like, HER2-enriched, luminal A and luminal B)  [20]. We did not include the normal-like subtype because of its significantly lower number of samples compared to other subtypes. Original PAM50 class of these patients was adopted from Supplementary Table  1  of the TCGA paper  [16]. We only included mutational data for the 54 genes reported as being significantly mutated in supplementary information of the TCGA paper  [22]  in our analysis (Supplementary Table  1 ). For the renal cancer analysis we used CAGEKID datasets  [17]  generated from genome-sequencing of 94 patients and RNA-Seq of 61 tumors (45 with matched normal kidney tissue control samples).
Differential gene expression analysis For mRNA differential expression analysis, we first converted gene symbol IDs to Uniprot accession IDs and only kept those that had any level of information in the interactome. To identify differentially expressed genes between groups of samples (all tumors or a class of tumors compared to non-tumor samples) we used limma  [47]  package with TREAT  [48]  for differential expression analysis of TCGA breast cancer normalized Agilent microarray data (level 3). The lfc parameter was set to 1, with Benjamini Hochberg (BH) FDR  [49]  cutoff of 0. 05 in order to identify genes that are significantly differentially expressed in tumors with a minimum of 2-fold change difference compared to 22 nontumor breast tissue samples (Supplementary Table  2 ). This procedure was also performed for each PAM50 group of patients (except Normal-Like tumors as discussed above in the Datasets) independently (Supplementary Tables  3 4 5 6 ).
To define genes differentially expressed in a given patient, we calculated a log2FC by dividing the expression of each gene in the patient's tumor by the average of expression values for that gene across nontumor control samples. For each patient, we considered the top 5% of genes for each direction (down or up) as differentially expressed. Differential gene expression of renal cancer samples was performed using edgeR Bioconductor package using the classic edgeR design for comparing selected tumors to all nontumor tissues. We used Benjamini and Hochberg method of multiple testing correction with a 5% cutoff of FDR. We also filtered out genes with absolute log2FC values below 1 (genes with differential fold-changes below 2) (Supplementary Table  8 ).
Proteome and phosphoproteome data analysis We obtained information about protein and phosphopeptide assembly and their relative abundance for TCGA breast cancer samples from clinical proteomic tumor analysis consortium (CPTAC; https://cptacdata-portal. georgetown. edu/cptac/s/S015). For each aberration hub, we examined if the protein or any of its phosphopeptides had a significantly different level in comparison of one subtype against other subtypes (t-test p-value < 0. 05 & |fold change| > 0. 5).
EFTCAMB has been used to place constraints on f (R) grav-ity  [70] , Hořava-Lifshitz  [38]  and specific dark energy models  [91]. It has also been used to explore the interplay between massive neutrinos and dark energy  [92] , the tension between the primary and weak lensing signal in CMB data  [93]  as well as the form and impact of theoretical priors  [94, 95]. An up to date implementation can be downloaded from http://eftcamb. org/. The JBD EFT-CAMB solver is based on EFTCAMB Oct15 version, while the others are based on the most recent EFTCAMB Sep17 version. B. hi_class hi_class (Horndeski in the Cosmic Linear Anisotropy Solving System) is an implementation of the evolution equations in terms of the α i (τ)  [19]  as a series of patches to the CLASS EB solver  [12, 13]  (coded in C). hi_class solves the modified gravity equations for Horndeski's theory in the synchronous gauge (CLASS also incorporates the Newtonian gauge) starting in the radiation era, after checking conditions for the stability of the perturbations (both on the scalar and on the tensor sectors).
The hi_class code has been used to place constraints on the α i (τ) with current CMB data  [96] , study relativistic effects on ultra-large scales  [97] , forecast constraints with stage 4 clustering, lensing and CMB data  [98]  and constraint Galileon Gravity models  [59]. The current public version of hi_class is v1. 1  [19]. The only difference between this version and the first one (v1. 0) is that v1. 1 incorporates all the parametrizations used in this paper. This guarantees that the results provided in this paper are valid also for v1. 0. Lagrangian-based models, such as JBD and Galileons, are still in a private branch of the code and they will be released in the future. The hi_class code is available from www. hiclass-code. net.
H. Hořava-Lifshitz gravity code CLASS-LVDM This code was developed in order to test the model of dark matter with Lorentz violation (LV) proposed in Ref. [82]. The code is based on the CLASS code v1. 7, and solves the Eqs. (  16 )-(  23 ) of Ref. [79]. The absence of instabilities is achieved by a proper choice of the parameters of LV in gravity and dark matter. All the calculations are performed in the synchronous gauge, and if needed, the results can be easily transformed into the Newtonian gauge. Further details on the numerical procedure can be found in Ref. [84]  where a similar model was studied. The code is available at http://github. com/Michalychforever/CLASS_LVDM. Compared to the standard CLASS code, one has to additionally specify four new parameters: α, β , λ -parameters of LV in gravity in the khronometric model, described in Sec.
ACKNOWLEDGEMENTS EB and PGF are supported by  ERC   H2020 693024  Grav-ityLS project, the  Beecroft Trust and STFC. EC is supported by an  STFC Rutherford Fellowship. BH is partially supported by the  Chinese National Youth Thousand Talents Program , the  Fundamental Research Funds for the Central Universities  under the reference No. 310421107  and  Beijing Normal University Grant  under the reference No. 312232102. The work of MI was partly supported by the  Swiss National Science Foundation  and by the  RFBR  grant  17-02-01008. The research of NF is supported by  Fundação para a Ciência e a Tecnologia (FCT)  through national funds ( UID/FIS/04434/2013 ) and by  FEDER  through  COMPETE2020  ( POCI-01-0145-FEDER-007672 ). NF, SP and AS acknowledge the  COST Action  ( CANTATA/CA15117 ), supported by  COST (European Cooperation in Science and Technology).
UC has been supported within the  Labex ILP  (reference  ANR-10-LABX-63 ) part of the Idex SUPER, and received financial state aid managed by the  Agence Nationale de la Recherche , as part of the  programme Investissements d'avenir  under the reference  ANR-11-IDEX-0004-02. SP and AS acknowledge support from The  Netherlands Organization for Scientific Research (NWO/OCW) , and from.
Cognitive neuroscience questions are commonly tested with experiments that involve a cohort of subjects. The cohort can consist of a handful of subjects for small studies to hundreds or thousands of subjects in open datasets. While there exist various online resources to get started with the analysis of magnetoencephalography (MEG) or electroencephalography (EEG) data, such educational materials are usually restricted to the analysis of a single subject. This is in part because data from larger group studies are harder to share, but also analyses of such data often require subject-specific decisions which are hard to document. This work presents the results obtained by the reanalysis of an open dataset from  Wakeman and Henson (2015)  using the MNE software package. The analysis covers preprocessing steps, quality assurance steps, sensor space analysis of evoked responses, source localization, and statistics in both sensor and source space.
Results with possible alternative strategies are presented and discussed at different stages such as the use of high-pass filtering versus baseline correction, tSSS vs. SSS, the use of a minimum norm inverse vs. LCMV beamformer, and the use of univariate or multivariate statistics. This aims to provide a comparative study of different stages of M/EEG analysis pipeline on the same dataset, with open access to all of the scripts necessary to reproduce this analysis.
, 2011; Tadel et al. , 2011) , each of which is different. Even though these packages provide tutorials for single subject data analysis, it is typically left up to the investigator to coordinate and implement multi-subject analyses. Here, we try to address this gap by demonstrating a principled approach to the assembly of group analysis pipelines with publicly available code 1 and extensive documentation. As members and maintainers within the MNE community, we will present analyses that make use of the MNE software suite  (Gramfort et al. , 2014). Historically, MNE was designed to calculate minimum-norm estimates from M/EEG data, and consisted in a collection of C-routines interfaced through bash shell scripts. Today, the MNE software has been reimplemented in  Gramfort et al. (2013a)  and transformed into a general purpose toolbox for processing electrophysiology data.
Built on top of a rich scientific ecosystem that is open source and free, the MNE software now offers stateof-the-art inverse solvers and tools for preprocessing, timefrequency analysis, machine learning (decoding and encoding), connectivity analysis, statistics, and advanced data visualization. The MNE software, moreover, has become a hub for researchers who use it as a platform to collaboratively develop novel methods or implement and disseminate the latest algorithms from the M/EEG community  (Maris and Oostenveld, 2007; Kriegeskorte et al. , 2008; Rivet et al. , 2009; Gramfort et al. , 2010 Gramfort et al. , , 2013b;; Hauk et al. , 2011; Khan and Cohen, 2013; Larson and Lee, 2013; Schurger et al. , 2013; Haufe et al. , 2014; King and Dehaene, 2014; Engemann and Gramfort, 2015; Smith and Kutas, 2015a,b).
With this work, we not only share good practices to facilitate reproducibility, but also present these latest advances in the MNE community which enable automation and quality assessment. Here, we demonstrate how to use MNE to reanalyze the OpenfMRI dataset ds000117 by  Wakeman and Henson (2015). This requires setting the objectives for the data analysis, breaking them down into separate steps and taking a series of decisions on how to handle the data at each of those steps. While there may be several interesting scientific questions that have not yet been addressed on this dataset, here we confine ourselves to the analysis of well-studied time-locked event-related M/EEG 1 https://github. com/mne-tools/mne-biomag-group-demo components, i. e. , event-related fields (ERF) and event-related potentials (ERP).
PRELIMINARIES In this work, we describe a full pipeline using MNE to analyze the OpenfMRI dataset ds000117 by  Wakeman and Henson (2015). The data consist of simultaneous M/EEG recordings from 19 healthy participants performing a visual recognition task. Subjects were presented images of famous, unfamiliar and scrambled faces. The dataset provides a rich context to study different neuroscientific and cognitive questions, such as: Which brain dynamics are characteristic of recognizing familiar as compared to unfamiliar faces? How do commonly studied face-responsive brain regions such as the Superior Temporal Sulcus (STS), the Fusiform Face Area (FFA), and the Occipital Face Area (OFA) interact when processing the familiarity of the face? At the same time, it presents a wellstudied paradigm which can be particularly beneficial for the development of methods related to connectivity and source localization.
Data Description The subjects participated in 6 runs, each 7. 5 min in duration. In the original study, three subjects were discarded due to excessive artifacts in the data. To produce comparable results, the same subjects are also discarded from the group results in this study. The data were acquired with an Elekta Neuromag Vectorview 306 system consisting of 102 magnetometers and 204 planar gradiometers. In addition, a 70 channel Easycap EEG system was used for recording EEG data simultaneously.
Reading Data MNE supports multiple file formats written by M/EEG hardware vendors. Apart from Neuromag FIF files, which are the default storage format, MNE can natively read multiple other formats ranging for MEG data including 4D Neuroimaging BTI, KIT, and CTF, and for EEG data B/EDF, EGI, and EEGLAB set 2. Despite this heterogeneity of systems, MNE offers a coherent interface to the metadata of the recordings using the so-called measurement info 3. Regardless of the input format, all processed files can be saved as FIF files or in the HDF5 format  4. MNE can handle multimodal data containing different channel types, the most common being magnetometer, gradiometer, EEG, electrooculogram (EOG), electrocardiogram (ECG), and stimulus trigger channels that encode the stimulation paradigm. MNE also supports electromyogram (EMG), stereotactic EEG (sEEG), and electrocorticography (ECoG), functional near-infrared spectroscopy (fNIRS) or miscellaneous (misc) channel types.
Declaring and renaming channel types is a common step in the preparation of M/EEG datasets before analysis. In our case, once the files were read in, some of the channels needed to be renamed and their channel types corrected in the measurement info (see  Wakeman and Henson, 2015) : the EEG061 and EEG062 electrodes were set as EOG, EEG063 was set as ECG, and EEG064 was set as a miscellaneous channel type as it was a free-floating electrode. If this step is omitted, some preprocessing functions may fall back to potentially less optimal defaults, for example, using the average of the magnetometers instead of the ECG channel when searching for cardiac events.
Maxwell Filtering (SSS) Neuromag MEG recordings are often preprocessed first using the Signal Space Separation (SSS) method  (Taulu, 2006) , otherwise known as Maxwell filtering. SSS decomposes the data using multipole moments based on spherical harmonics and removes the component of magnetic field originating from outside the MEG helmet. SSS is therefore useful for removing environmental artifacts, and can also be used to compensate for head movements during the recording. In this study, movement compensation is not strictly necessary as the participants managed to stay predominantly still. The data provided by OpenfMRI  (Poldrack and Gorgolewski, 2017)  already contain files processed using the proprietary Elekta software MaxFilter, which is what we use in our analysis for the sake of reproducibility.
However, MNE offers an open source reimplementation and extension of SSS as well. Before running SSS, it is crucial that bad channels are marked, as otherwise SSS may spread the artifacts from the bad channels to all other MEG channels in the data. This step is preferably done manually with visual inspection. When using the MNE implementation of Maxwell filtering, we reused the list of bad channels available from the Elekta MaxFilter logs in the dataset. Results comparing raw data, data processed by Elekta MaxFilter, and data processed by the MNE maxwell_filter function are provided in Figure  1. While the unprocessed data do not show a clear evoked response, the Maxwell filtered data do exhibit clear event-related fields with a clear peak around 100 ms post-stimulus. Note that the results obtained with Elekta implementation and the MNE implementation have minimal differences due to slight differences in computation of component regularization parameters.