dataesr/scientific-paragraphs-categorization huggingface dataset:
We corrected this gene expression dataset for the principal components significantly correlated with GTEx samples covariates using the Swamp R package. WGCNA gene modules were functionally annotated with gProfileR [99] R software package using GO database, accounting for multiple testing with gSCS's gProfiler test. Background genes used were all genes in the substantia nigra GCN. Cell type enrichment analysis was performed with the userListEnrichment function with brain specific enrichment, implemented in the WGCNA R package. Preservation analysis of UKBEC GCN in GTEx's substantia nigra profiles was performed with WGCNA's preservation analysis. Results are reported with the Z. summary statistic  [100]. Graphical representation of the GCN subnetworks were constructed by using the 27 candidate genes and known PD genes (ATP13A2, FBXO7, LRRK2, PARK2, PARK7, PINK1, RAB39B, SNCA, and VPS35) as seed genes.
Image acquisition and analysis Image acquisition was carried out using the automated confocal imaging system, Cell Voyager CV7000 (Yokogawa, Tokyo, Japan). The mitochondrial morphology assay involved a total of 60 fields per well using a 60× water immersion objective lens for improved resolution. Nuclei were imaged utilizing the 405 nm laser, Mitotracker CMXros utilizing the 561 nm laser, and mitotracker DeepRed utilizing the 640nM laser. For the translocation assay, a total of 60 fields per well were taken using a 20× objective lens. Nuclei were imaged utilizing the 405 nm laser, Parkin-GFP utilizing the 488 nm laser, and mitotracker DeepRed utilizing the 640 nm laser. Images were stored and analyzed by the Columbus Image Data storage (PerkinElmer, Waltham, MA, USA). Image quality control: only well-segmented interphase cells were included.
Mitotic, apoptotic badly segmented, and out-of-focus cells were excluded. Cells touching the border of the image were removed to avoid analysis of artificially cropped cells. All wells where the perturbation strongly decreased cell number were disregarded. Morphological characteristics and signal intensities were quantified and results exported to R package CellHTS2. To quantify mitochondrial morphology, the median mitochondrial number per object, roundness, axial length ratio, and intensity of mitorackerCMXros (mitochondrial potential) were calculated. To differentiate between CCCP-treated Parkin stable cell lines and untreated cells, the number of spots formed on mitochondria was calculated. Cells containing more than two spots were considered positive for Parkin translocation. The ratio of cells positive for translocation versus the number of cells negative for translocation was calculated per well to give a cell number independent measure of Parkin translocation.
CCCP-treated cells transduced with a scrambled shRNA and CCCP-treated cells transduced with shRNA targeting PINK1 were included on each plate. An average Z' of 0. 61 was calculated for the entire screen, with a minimum Spearman's Rank correlation between replicates of 0. 8. Data from high content imaging assays were analyzed using the BioConductor CellHTS2 package for the R software environment (R version 2. 11. 1, BioConductor version 2. 6). Data were normalized to negative controls on a per-plate basis to minimize plate-to-plate variation. For the Parkin-translocation screen, negative controls were considered as wells which had been transduced with lentivirus encoding a scrambled sequence and had been treated with CCCP. For the remaining screens, negative controls were considered as wells that had been transduced with lentivirus encoding a scrambled sequence.
Retinae from at least three animals were examined and quantified per genotype. Enhancement of α-synucleininduced retinal degeneration was quantified based on the severity of retinal vacuolar changes seen in stained histologic sections. We examined representative photographs taken with a 40× objective from well-oriented, intact tangential sections at a depth in which the retina achieves maximal diameter. Using ImageJ software  [107] , we recorded the area occupied by all vacuoles with a diameter greater than 4 μm and divided by the total retinal area to compute a percentage. Statistical comparisons were implemented using a two-tailed student's t-test. α-synuclein expression levels were determined by immunoblot (clone 42, 1:1000, BD Transduction Laboratories, San Diego, CA, USA).
High-throughput RNAi screenings (HTS) allow quantifying the impact of the deletion of each gene in any particular function, from virus-host interactions to cell differentiation. However, there has been less development for functional analysis tools dedicated to RNAi analyses. HTS-Net, a network-based analysis program, was developed to identify gene regulatory modules impacted in high-throughput screenings, by integrating transcription factors-target genes interaction data (regulome) and protein-protein interaction networks (interactome) on top of screening z-scores. HTS-Net produces exhaustive HTML reports for results navigation and exploration. HTS-Net is a new pipeline for RNA interference screening analyses that proves better performance than simple gene rankings by z-scores, by re-prioritizing genes and replacing them in their biological context, as shown by the three studies that we reanalyzed.
Gonzalez & Zimmer  [11]  were among the pioneers of network-based approaches for RNAi screenings analyses. They developed a method based on co-clustering that was applied to the discovery of host factors of the Hepatitis C Virus (HCV). They identified deregulated modules by calculating a distance matrix among all genes, combining the RNAi score and the shortest path that separates them in an interactome map. They then performed average linkage clustering. Clusters were formed and their size was controlled using an FDR approach. The advantage of their method is that it avoids interactome search heuristics. However, it uses p-values from the RNAi score, which are not always available. Also, there is a difficulty in finding modules of interest in the clustering tree. Since this study, other algorithms were also proposed for network analysis of RNAi screen  [12, 13, 14, 15, 16, 17, 18, 19]  and recently, two programs were developed, that optimize RNAi screen analysis: HotNet2 and CARD.
HotNet2 (HotNet diffusion-oriented subnetworks)  [20]  is a network analysis program initially designed for discovery of genes and pathways significantly mutated in cancer. The authors performed a pan-cancer analysis using a huge dataset from The Cancer Genome Atlas, and found 16 mutated pathways that comprise known cancer signaling pathways as well as pathways that are less known for their involvement in cancer. This network approach helps identify rare combinations of mutations. HotNet2 uses a heat diffusion process to assess simultaneously the local genes mutations and local topology. Hot-Net2 starts with initial mutation frequency represented under the form of heat. It then undergoes a heat diffusion process where the heat is diffused among neighbors in the network. An exchanged heat matrix is computed and then so-called 'hot' subnetworks are identified.
It is also possible to provide the program with a gene score of any type (such as Z-score from RNAi screens) that will be converted into heat and therefore use it for other analysis type. The statistical test provided determines the significance on the combined number and size of subnetworks found. Dutta  [21]  proposed CARD, an interactive web-based application dedicated specifically to the analysis of RNAi screenings. It features an extended pipeline, from fully featured data normalization with per-plate quality control, to advanced functional network analysis. CARD features a separate program for each functionality, which potentially allows to process quality control, basic functionalities, and high-level network analyses separately. The program RNAi-Cut is the first step for network analyses.
We then merge the obtained modules to form meta-subnetworks that incorporate regulation information and PPIs. This approach allows reprioritizing genes by replacing hits into their biological context, which includes their physical interactors and their regulators. We performed our analysis on state-of-the-art PPI and regulation databases, and proved its usefulness on three RNAi screen datasets. In each analysis, we reported newly found markers, GO enrichments and comparison with the original analyses. We also compared our method to other existing algorithms. The program was made available to the community through the HTS-Net (High-Throughput Screening-Network) Mobyle server (http://htsnet. marseille. inserm. fr/).
Network Data Collection The discovery of new pathways heavily relies on the coverage, quality, and nature of the network map. We decided to process protein-protein interaction data and TF-gene regulation data separately. Thus, we built two networks by aggregation of several databases: an interactome and a so-called "regulome" (Fig  1). PPI databases mainly contain data generated by high-throughput methods (Y2H, mass spectrometry, protein microarray) aggregated to "low throughput" data (manually curated data from the literature). In order to build our human interactome, we downloaded, parsed and merged all of the interactions found in major databases: the Database of Interacting Proteins (DIP)  [22] , the Human Protein Reference Database (HPRD)  [23] , the Interologous Interaction Database (I2D)  [24] , IntAct  [25] , the Molecular INTeraction database (MINT)  [26]  and Human ProteinPedia  [27].
A total of 14,423 genes and 171,146 interactions were gathered in the interactome network. TF-TF interactions were gathered from TRANSFAC, from the atlas of combinatorial transcriptional regulation by Ravasi and colleagues  [28] , the interactions predicted by Mys ˇičkova ánd Vingron  [29] , and those predicted by Yu and colleagues  [30]. These were included in the interactome. Large-scale regulation databases contain predicted human transcription factors (TF) regulation for tissue-specific sets of genes. ITFP (the Integrated Platform of mammalian Transcription Factors) reports 4,105 putative TFs and 69,496 potential TF-target pairs for human  [31]. TRED (the Transcriptional Regulatory Elements Database) reports 7,479 targets genes  [32]. TRANSFAC, a commercial database, reports 707 TF and 8,712 TF-genes pairs  [33].
ORe-gAnno, the open-access community-driven resource for regulatory annotation, reports 465 TFs and 3,853 genes using a community annotation system  [34]. The PAZAR database of gene regulatory information reports 1,284 regulated genes for 708 TFs  [35]. A total of 9,755 genes and 68,703 interactions were mapped in the regulome network. For both networks, all protein identifiers were mapped to Entrez GeneID accession numbers using correspondence tables downloaded from NCBI FTP site (date: November 5th, 2014). The transcription factors from these databases were annotated as such in both the interactome and the regulome.
Subnetworks detection with HTS-Net HTS-Net subnetwork scores are formally defined as the average z-score value for all genes belonging to this subnetwork, as follows: SðsnÞ ¼ X g2sn z À scoreðgÞ where sn is the current subnetwork, S(sn) its score, g the current gene, z-score(g) the z-score corresponding to the current gene. For each studied dataset, the complete set of z-scores measured from the screening was investigated by HTS-Net as shown in Fig 1. In each network (interactome or regulome, Fig  1A ) every node is tested as a putative seed (Fig  1B ). We decided to use identical algorithm for the two networks. Even if only a part of the regulome is acting as regulators we test all genes, even regulated genes, for potential seeds. This allows the detection of subnetworks with regulated genes having high z-scores, that will include transcription factors that may be characterized by a lower score.
It is caused by the fact that every node in the interactome and the regulome is used as a seed by the algorithm: two distant seeds can have a pool of high-scoring genes in common. Before processing with statistical validation, redundant subnetworks should be filtered out, depending on their respective sizes and scores. All the subnetworks are compared by pairs. We compute the proportion of nodes in subnetwork A that are also found in subnetwork B (first overlap), and vice versa (second overlap). If the two overlaps are above a specific rate (80% by default), the Analysis of HTS-Net parameters impact. We analyzed the impact of several parameters on the number of subnetworks, genes and meta-subnetworks in Chia's dataset  (Chia et al. , 2010). We tested the impact of minimum score improvement threshold th over the number of subnetworks and genes, the impact of a type II p-value on the number of subnetworks, and the impact of the interactome-regulome common protein threshold on the number of meta-subnetworks.
This second p-value enforces the link between the subnetworks and the global network. The p-value III shows the statistical significance of the algorithm by comparing the subnetworks against randomly generated subnetworks of the same size, regardless of the RNAi scores and network topology. These three distributions are fitted with a Gaussian mixture distribution (Fig  3 ) (R package nor1mix). The selection of the subnetworks is then made by thresholding all p-values at a statistically significant level, after Bonferroni correction has been applied for multiple testing. In both the interactome and the regulome, final subnetworks are retained if the three p-values are significant. The p-values retained for the three analyses are shown in Table  1. We decided to set these thresholds so that we have a similar order of magnitude for the number of subnetworks to process in each dataset and network.
Regulome and interactome subnetworks integration In order to identify pathways of regulation involving multiple regulation levels (for instance regulome subnetworks regulating several PPI subnetworks), HTS-Net features an integration step to combine interactome-based subnetworks with regulome-based subnetworks by finding common proteins among the two subnetworks types. We examined individually all regulome subnetworks and all interactome subnetworks and connected subnetworks that presented a minimum of n common nodes, yielding meta-subnetworks (Fig  1 ). This value depends on the convergence of subnetworks and is dataset-specific. We studied the variation of the number of obtained meta-subnetworks versus the common protein threshold (Fig  2D ). The number of meta-subnetworks is greatly affected by the number of common proteins n between regulome and interactome, from around 200 meta-subnetworks obtained for 1 common protein to 0 for 6 common proteins.
Online reports and subnetworks databases The heterogeneous nature of data sources, the complex conversion of gene IDs and the overall structure complexity of the newly generated data necessitate an advanced visualization system in order to properly interpret the nature of subnetworks. In particular, several interaction types as well as several network types (interactome, regulome) must be accordingly visualized, and compared to known biology. The HTS-Net screening pipeline features an advanced reporting mechanisms allowing subnetworks visualization while taking into account several network types and various interaction types (PPI, gene regulation). The columns are describing the following sets of parameters: th is the improvement score threshold. It was set to 0. 05 for each dataset. P-values of type I,II and III are described in the 2.
4 section. The same value was chosen for the three p-values in the interactome and in the regulome, independently. The range of interactome and regulome scores corresponds to the maximum and minimum subnetwork scores obtained after statistical validation for both networks. n is the threshold of common nodes that was used for combining regulome and interactome networks The corresponding number of meta subnetworks is shown within parenthesis. https://doi. org/10. 1371/journal. pone. 0185400. t001 All detected subnetworks are plotted using GraphViz package (AT&T Research) and the web report design was done with KickStart 0. 99 (HTML5+CSS3). Using this interface, all subnetworks can be investigated and compared. All genes are also listed and connected to their respective subnetworks. NCBI links and GO enrichment analyses are provided.
GO enrichment statistics Genes were annotated with Gene Ontology (GO). GO enrichment analysis was performed for each dataset by standard hypergeometric testing associated with a Benjamini-Hotchberg multiple testing correction (FDR = 5%)  [36]. Gene Ontology enrichment of all subnetworks for the three tested datasets are available as an Excel file made available as S1 File and from the HTS-Net web site (http://htsnet. marseille. inserm. fr/supplementary-data. html). Results from the GO enrichment analysis are discussed in the results section for the three analyzed datasets.
Public data sources In order to evaluate the capacity of our HTS-Net pipeline to identify gene networks of interest, we reanalyzed three published RNAi screenings datasets: the dataset published by Chia and colleagues  [37] , the dataset published by Wolf and colleagues  [38] , and the dataset published by Tai and colleagues  [39]. Chia and colleagues  [37]  set up an RNAi screen to determine genes that sign for human embryonic stem cell (hESC) identity. First, they engineered a cell line model, H1 hESC  [40] , by introducing a GFP reporter construct driven by the POU5F1 upstream regulatory region. POU5F1 expression can be considered as a marker of undifferentiated hESC. This cell line model was screened against a library of 21,212 human genes in duplicate. The mean of the two z-scores for GFP fluorescence reduction (Fav) and Nuclei number reduction (Nav) were calculated.
The authors found all genes with a Fav_score >2 to be potential candidate determinants of hESC identity, which gave a total of 566 genes. They then performed a secondary screening on the 200 most highly ranked genes. The second dataset reanalyzed concerns a study by Wolf and colleagues  [38]  where they tested the ability of breast cancer stem cell (CSC) population (CD44+CD24-/low) to form mammospheres (a functional surrogate marker of the CSC self-renewal ability) after shorthairpin RNA interference assay (shRNAi) to identify breast CSC key regulators on 5,045 genes. They compared this analysis with the same cells cultured in adherent conditions (culture condition allowing cell differentiation) to eliminate shRNAi without breast CSC-specific effect. Wolf et al. initially selected 1,051 genes with a p-value <0.
01 on the adherent z-score to assess genes impairing cell growth. Then they performed an enrichment analysis on a set of 392 genes specific to mammosphere formation, to verify a p-value <0. 01 on the mammosphere z-score and a p-value >0. 1 on the adherent z-score to enforce CSC specificity. They then focused the analysis on autophagy genes ATG4A and ATG4B, with respective z-scores of 0. 51 and 0. 11. The last dataset reanalyzed is the one from Tai et al. [39] , who screened for host proteins involved in HCV replication using the Huh7/Rep-Feo subgenomic genotype replicon cell line. This replicon encodes the HCV non-structural proteins that mediate viral transcription as well as a firefly fusion protein used for signal quantification. This cell line was screened against 21,094 siRNAs targeting the whole human genome in duplicate.
Comparison with Hotnet2 and CARD programs on the Tai dataset We compared HTS-Net with three other programs, Hotnet2, CARD and Gonzales & Zimmers's method on Tai's dataset. The choice for the Tai's dataset was motivated by the fact that Gonzales & Zimmer do not provide source code or program but published an analysis of Tai's dataset in their publication, so this was a good basis of comparison. Hotnet2 is provided under source code. We therefore installed the code in a Linux machine, and compiled it after installing along all dependencies. We then ran it with the HPRD  [23]  network after generating corresponding influences matrices for the networks (matrices that measures how the heat will propagate within the network). The HPRD network was chosen since it is distributed along with the program source code and is well integrated with it.
The program runs with two parameters, Beta and Delta. Beta is a network heat diffusion parameter that sets how a node value interferes with its neighbors. We left it at its default value. Delta is the edge weight and has an impact on the detected subnetwork sizes. After running, the program proposes several values on the range [2,45x10 -3 -4,92x10 -3 ] and we chose Delta = 2,44x10 -3 which gave reasonable subnetworks number and size (288 subnetworks, p = 0. 46). We used the subnetwork export function to obtain a gene list comparable with other pipelines. CARD being accessible as a web service, we registered an account and formatted Tai's data to the CARD Format. Chrome 44 is officially recommended, but we were able to run it with the more recent version 55. There was no way to directly provide normalized RNAi data, so we provided randomized identifiers for plate, as this information was not available for the dataset analyzed, and loaded the data by specifying no normalization, so that we kept the previously normalized data.
Then, we identified hits using RNAiCut to determine optimal level of significance for z-score. The maximum log(p-value) occurs at 2,786 (p-value = 0,004). We then started the network analysis with this setting. We selected all networks available in the system, HPRD, Bind and BioGRID. We downloaded the resulting gene list as a CSV file in order to compare results with other pipelines. For Gonzales & Zimmer's method, the code was not available, so we re-used the analysis of Tai data provided in the corresponding article.
A new pipeline for deciphering regulation and interactions impacted by HT screens We are proposing the HTS-Net pipeline for the network analysis of large-scale (up to genomewide) RNAi screenings adapted to various experimental settings. The pipeline is maintained and accessible through a locally-deployed Mobyle portal  [41]  available at http://htsnet. marseille. inserm. fr/. A tutorial is available from the same URL. Analyses can be conducted as soon as the data is normalized and properly formatted to HTS-Net, under the form of a 2-column tab-delimited file: the first column contains gene identifiers (Entrez Gene), and the second column contains associated z-scores from the screening. If multiple replicates exist, they must be averaged or combined to a single score, as HTS-Net is not designed to work with multiple scores simultaneously.
Additional information must be provided, that is, the organism studied and the p-values for scoring. An archive is then generated that contains the analysis report in HTML format compatible with any standard web browser. In order to understand the results obtained from the HTS-Net pipeline, we compared genes identified by HTS-Net with genes originally published. We also produced GO enrichment analysis for genes that are in common between HTS-Net and the original publication and genes that have been specifically identified with one of the two methods (see support data on companion web site). Fig  5  shows the distribution of z-scores for the Tai Luciferase dataset along the distribution of score for genes identified with HTS-Net. These distributions show that HTS-Net is less stringent with genes that have a low score but connected to important networks, and that would have been discarded by a classical analysis based solely on z-score statistics.