dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In this study, we analyzed a data set of 433 cancer patients with various tumor types. The patients underwent genomic testing of tumor DNA and ctDNA at different points of time in their disease course. Mutations in TP53 gene were found in 49. 7% tissue biopsies, and in 36. 7% ctDNA analysis. The lower frequency of TP53 mutations in ctDNA is expected since the yield of ctDNA is lower compared to tissue DNA. We focused our analysis on characterized mutations in the TP53 gene (those that are no VUS). Specifically, we investigated the relationship between TP53 mutation state in the tumor DNA and ctDNA. Accordingly -we defined 6 mutational categories describing this relationship: (i) exactly the same TP53 mutation in tissue DNA and ctDNA; (ii) different TP53 mutations in tissue DNA and ctDNA; (iii) TP53 mutation/s only in tissue DNA, (iv) TP53 mutation/s only in ctDNA; (v) no TP53 mutation in both tissue DNA and ctDNA; and (vi) exactly the same + othersTP53 mutations in tissue DNA and ctDNA.
Strategy We examined circulating NETest levels from GNENs (n = 46) and compared these to controls (n = 63) using the STARD approach  [25]  (Fig. 1 ). The diagnostic accuracy and metrics (AUROC, sensitivity, specificity) for the NETest were calculated. Cohorts: histologically confirmed GNENs, n = 46; GNETs Type 1: 42 (32 NET G1, 10 NET G2), a GNET Type 3: 1 well-differentiated NET G3, poorly differentiated neuroendocrine carcinomas (NECs) (n = 3), and controls (n = 63). Disease status at sampling was assessed by gastroscopy and histology (resection margin [R] positivity of polypectomy or biopsy), EUS, CT or MRI, and/or 68 Ga-DOTA-TATE PET/CT. Subjects were divided into groups: image modality (endoscopy/radiology)-positive (IPD) or image modality negative (IND) disease. The clinical data were collected retrospectively.
Statistical analysis The required total sample size (NETs and controls, power 0. 8 and α = 0. 05, two independent study groups, continuous primary endpoint = based on published NETest results in NETs (mean/SD) vs. controls (mean)  [11, 18, 30]  was calculated to be a minimum of 40 patients/subjects in each group. Intergroup analyses were undertaken using 2-tailed non-parametric tests (Mann-Whitney U test). Area under the Receiver Operator Characteristic (AUROC) analysis was used to determine the diagnostic accuracy of the NETest  [24, 31, 32]. Metrics calculated included sensitivity and specificity. Prism 7. 0 for Windows (GraphPad Software, La Jolla California USA, www. graphpad. com) and MedCalc Statistical Software version 16. 2. 1 (MedCalc Software bvba, Ostend, Belgium; http://www. medcalc.
These data are consistent with the known sensitivity of molecular genomic analysis in the identification of microscopic recurrent or residual NET disease  [45]. Identifying patients with residual disease is a critical clinical issue since under such circumstances, wide local excision or partial gastrectomy should be considered  [1]. After excision, the endoscopic follow-up is recommended, but the most appropriate timing has never been defined, although most consider annual or biannual reendoscopy as prudent  [1, 38, 39]. When mucosectomy techniques and follow-up programs are undertaken, a recurrence free survival of 24 months can be attained, with the overall excellent prognosis of Type 1 GNETs  [1]. In our cohort, thirteen subjects underwent GNEN surgery (median 2015 [range 2006-2017]) for, either Type 1 or 3, prior to tertiary medical center referral.
The phenotypic diversity of cells is governed by a complex equilibrium between their genetic identity and their environmental interactions: Understanding the dynamics of gene expression is a fundamental question of biology. However, analysing time-course transcriptomic data raises unique challenging statistical and computational questions, requiring the development of novel methods and software. This workflow provides a step-by-step tutorial of the methodology used to analyse time-course data: (1) quality control and normalization of the dataset; (2) differential expression analysis using functional data analysis; (3) clustering of time-course data; (4) interpreting clusters with GO term and KEGG pathway enrichment analysis. As a case study, we apply this workflow to time-course transcriptomic data from mice exposed to four strains of influenza to showcase every step of the pipeline.
However, with long time course datasets, particularly in developmental systems, a massive number of genes will show some change  (Varoquaux et al. , 2019). For example, in a study of mice lung tissues infected with influenza that we consider in this workflow, we show that over 50% of genes are changing over time. The task in these settings is often not to detect changes in genes, but to categorize them into biologically interpretable patterns. We present here a workflow for such an analysis that consists of 4 main parts (Figure  1 ): • Quality control and normalization; • Identification of genes that are differentially expressed; • Clustering of genes into distinct temporal patterns; • Biological interpretation of the clusters. This workflow represents an integration of both novel implementations of previously established methods and new methodologies for the settings of developmental time series.
Installation and setup moanin and timecoursedata are available from bioconductor, and can be installed using the install function in the package BiocManager, along with the corresponding package that contains time course datasets we will use: library (BiocManager) # Need to use the development version for now. BiocManager::install(version="devel") BiocManager::valid() BiocManager::install("timecoursedata") BiocManager::install("moanin") The following additional packages are needed for this workflow: # From Github library(moanin) library(timecoursedata) # From CRAN library(NMF) library(ggfortify) # From Bioconductor library(topGO) library(biomaRt) library(KEGGprofile) library(BiocWorkflowTools) #Needed for compiling the. Rmd script If Bioconductor is installed, the CRAN and Bioconductor packages above can be installed via BiocManager::install( c("NMF", "ggfortify", "topGO", "biomaRt", "KEGGprofile", "BiocWorkflowTools", "timecoursedata", )).
Quality control and normalization The first steps of analysis of gene expression data is always to do normalization and quality control checks of the data. However, in what follows, we do not show the steps for normalization, as these are specific to the platform (microarray); the code for the normalization is available from GitHub  (Abrams et al. , 2019; Park et al. , 2003b). Instead, we focus on common steps for exploratory analysis of the data, including for the purpose of quality control. These steps are not specific to time course data, but could be applied for any gene expression analysis. For this reason, we will not print the detailed code that is needed for this part of the analysis; interested readers can examine the rmarkdown code that accompanies this workflow.
, 2005) , and then use that spline model to test for different kinds of differential expression across time. The per-time point analysis uses classical differential expression approaches and is often the approach advocated when dealing with small time-course datasets, where there are only a few time points  (Love et al. , 2014; Ritchie et al. , 2015; Robinson et al. , 2010). For long time-course datasets, however, a separate test for each time point results in creating many different tests, for example one for every time point, the results of which are difficult to integrate. We find in practice that the global analysis simplifies analysis and interpretation of longer time courses data, with per-time point analysis reserved for particularly interesting comparisons of individual time-points.
0-VL. 0" "M. 3-VL. 3" "M. 6-VL. 6" "M. 9-VL. 9" "M. 12-VL. 12" Then moanin will run the differential expression analysis on all of those timepoints jointly using the function DE_timepoints. weekly_de_analysis = DE_timepoints(moanin_model, contrasts, use_voom_weights=FALSE) The output is a table of results, where each row corresponds to a gene and the columns correspond to the p-value (pval), log-fold change (lfc) and adjusted p-value (qval) of the sets of contrasts; the order of the genes in the table is the same as the input data. Table  2  contains the results for the first timepoint (i. e. first three columns of the output) and the first ten genes. Additional timepoints are in the additional columns of the output. We will repeat this, comparing each of the remaining three treatments to the control ("M") (code not printed here, as it is a replicate of the above, see accompanying rmarkdown document).
Methods that rely on the stability of the clustering results to choose k thus ensure that the biological interpretation of the clusters holds after perturbations to the data. In addition, simulation where the data is generated with a well defined k show that the clustering is more stable for the correct number of the clusters. Most methods to find the number of clusters with stability measures only provide visual aids to guide the user. The first element often visualized is the consensus matrix: the consensus matrix is an n x n matrix that stores the proportion of clustering in which two items are clustered together. A perfect consensus matrix ordered such that all elements that belong to the same cluster are adjacent to one another which show blocks along the diagonal close to 1. To perform such analysis, the first step is run the clustering several times on a resampled dataset--either using bootstrap or subsampling.
Using the bootstrapping strategy, we sample with replacement a sample of the same size as our original clustering (i. e. 5521 genes): n_genes = dim(de_moanin_model)[1] indices = sample(1:dim(de_moanin_model)[1], n_genes, replace=TRUE) bootstrapped_y = de_moanin_model[indices, ] Using the subsampling strategy, we take a unique subset of the genes, keeping 80% of the genes: We run the bootstrap method on all genes differentially expressed and with a log-fold-change higher than 2 (computed with the lfc_max method), and do it B = 20 times for each of k = 2 -10. We show the code below, but because of the time the computations take, we have evaluated these values separately and provided the results in separate files (one per k) for users to explore more quickly. For now, we bring in the results for k = 5 and k = 20.
We will leverage the packages biomaRt  (Durinck et al. , 2005)  and KEGGprofile  (Zhao et al. , 2017)  for this step. KEGGprofile is a package that facilitates enrichment analysis on a set of genes labeled with the Ensembl annotation  (Yates et al. , 2019)  based on the set of biological pathways described in the KEGG database  (Kanehisa et al. , 2015). We thus need to convert the gene names, which are given in the Refseq annotation, into the corresponding Ensembl name. This is where biomaRt comes in handy: it enables easy conversion from one gene annotation to another. Here, we will use the function getBM in biomaRt to convert the gene names from cluster 8. ## Set up right ensembl database ensembl = useMart("ensembl") ensembl = useDataset("mmusculus_gene_ensembl", mart=ensembl) ## Get gene names of genes in Cluster 8 cluster = 6 gene_names = names(labels) genes = gene_names[labels == cluster] ## Convert gene names genes = getBM(attributes=c("ensembl_gene_id", "entrezgene_id"), filters="refseq_mrna", values=genes, mart=ensembl)["entrezgene_id"] Then we use the function find_enriched_pathway in the KEGGprofile package to determine whether any KEGG pathways are enriched in cluster 6, i.
Finding enriched GO terms The Gene Ontology (GO) database (Consortium, 2018), also categorizes genes into meaningful biological ontologies and can be used for enrichment analysis via the package topGO (Alexa & Rahnenfuhrer, 2016). We again use biomaRt to find the mapping between genes and the GO terms to which they match. genes = getBM(attributes=c("go_id", "refseq_mrna"), values=gene_names, filters="refseq_mrna", mart=ensembl) The biomaRt query results in a matrix with two columns: gene names and GO term ID. The package topGO (Alexa & Rahnenfuhrer, 2016) expects the GO term to gene mapping to be a list where each item is a mapping between a gene name and a GO term ID vector, for example:.
Software availability Source code is available from GitHub: https://github. com/NelleV/2019timecourse-rnaseq-pipeline. Archived source code at the time of publication: https://doi. org/10. 17605/OSF. IO/U2DQP  (Varoquaux & Purdom, 2020)  License: BSD-3 All packages used in the workflow are available on GitHub, CRAN, or Bioconductor. Finally, we use sessionInfo() to display all packages used in this pipeline and their version numbers. Major comments: Having communicated with a number of bioinformatic analysts working with time course data, I would say the most common approach is a LRT of ~condition + time + condition:time compared to ~condition + time to obtain a single p-value for the inclusion of the interaction terms. The current article implies that the most common approach is to perform pairwise DE at every time point, but in my experience this is not the case.
Materials and Methods Participants. The study was approved by the University College London (UCL) Research Ethics Committee (Project ID: 8231/001). We recruited 47 human subjects who provided written informed consent. This sample size aimed at providing sufficient power to study interindividual differences in metacognitive ability. Eligibility criteria included an absence of neurological or psychiatric conditions, normal or corrected-to-normal vision, and an absence of color blindness. Subjects were instructed that they would receive a £28 base payment and up to £2 bonus according to their performance. All subjects were eventually paid £30 for their participation. To ensure data quality, a series of exclusion criteria were applied. Six participants were excluded for responding at chance level, missing more than 5% of trials and/ or selecting games purely according to their favorite colors, leaving n = 41 participants (24 females [f]/17 males [m]; aged 19 to 40; mean, 24 y old) for behavioral data analysis.
At the end of blocks, task choice trials were unspeeded. After subjects responded, a border around the chosen task was illuminated for 1,500 ms (Fig. 1A ). Within scanning runs, blocks were separated by a 10-s break. Longer breaks were provided between scanning runs. Metacognition Task. After the scanning session, subjects performed a metacognition task in order to allow 1) estimation of individuals' metacognitive abilities and 2) build a confidence model for use in the analysis of in-scanner data (Behavioral analyses). Subjects performed 200 trials of the same perceptual discrimination task as in the calibration phase, but instead of receiving feedback, they were asked to give a trial-by-trial confidence rating in the correctness of their response, using a scale from 1 (relatively low) to 6 (relatively high confidence).
Missed trials and trials with log(RT) outside of 3 SDs from the mean were excluded from subsequent analyses (mean = 3. 5% of trials across subjects). To establish whether end-of-block task choices, our index of global SPEs, were related to difficulty level, we examined how often subjects chose the easiest of both tasks. Furthermore, to investigate whether participants took into account fluctuations in performance, we split task choice data in blocks with a larger (respectively smaller) difference in performance between tasks and tested the difference in task choice frequency using paired t tests (note that in this analysis two subjects were removed for having no blocks in which they performed better on the more difficult task). We further examined whether accuracy, difficulty level, and their interaction influenced task choice using a logistic regression.
We first computed meta-d′, a metric based on signal detection theory (SDT) that evaluates the extent to which a subject's confidence ratings discriminate between their correct and incorrect responses  (50). Subject-specific meta-d′ parameters were obtained by fitting confidence rating data using the HMeta-d toolbox  (51). By dividing meta-d′ by d′, we obtain metacognitive efficiency, our measure of interest, that represents metacognitive ability corrected for differences in performance. Because SDT assumes a constant stimulus strength, we estimated two metad' values separately for the two difficulty levels. We also computed a modelfree index of metacognitive ability, the type-2 area under the receiver operating curve (AUROC2) per difficulty condition  (50). Metacognitive efficiency and AUROC2 values were then each averaged across easy and difficult conditions to obtain two indices per subject.
34 ms; and field maps with 64 slices (ascending slice order, slice thickness, 2 mm and 1-mm gap), TE1 = 10 ms, TE2 = 12. 46 ms, and in-plane field of view = 192 mm 2 with resolution = 3 mm 2. fMRI Preprocessing. We preprocessed MRI data using SPM12 (https://www. fil. ion. ucl. ac. uk/). Images were first reoriented, and four dummy volumes were discarded at the beginning of each run. Standard preprocessing included slice-timing, realignment and unwarping using the field maps, coregistration, segmentation, normalization to MNI template with voxel interpolation = 2 mm 3 , and Gaussian spatial smoothing with full width at halfmaximum = 8 mm. Motion parameters from the realignment and their first-order temporal derivatives were subsequently used as regressors of no interest in first-level analyses.
Physiological Monitoring inside the Scanner. Peripheral measurements of cardiac pulse and breathing were made together with scanner slice synchronization pulses using a Spike2 data acquisition system (Cambridge Electronic Design). The cardiac pulse signal was measured using an MRIcompatible pulse oximeter (model 8600 F0; Nonin Medical) attached to the subject's finger. The respiratory signal (thoracic movement) was monitored using a pneumatic belt. A physiological noise model was constructed to account for artifacts related to cardiac and respiratory phase and changes in respiratory volume using an in-house MATLAB toolbox  (53). Models for cardiac and respiratory phase and their aliased harmonics were based on RETROICOR (54) and a similar, earlier method  (55). Basis sets of sine and cosine Fourier series components extending to the third harmonic were used to model physiological fluctuations.
We focused on brain areas where activity was sensitive to global SPE level. Using correlation analyses, we tested two complementary hypotheses (Fig. 4  A and D). First, we examined whether a change in slope (difference in the modulation of local confidence-related signals in vmPFC and PRECU by global SPEs; Fig. 2 ), or a change in baseline activity (strength of encoding of global SPEs in ventral striatum; Fig. 3 ) was related to global SPE sensitivity, defined as the frequency of choosing the easier task at the end of blocks (Fig. 1B ). Data and Code Availability. MATLAB code and behavioral data for reproducing the main figures and statistical analyses of the study are freely available at https://github. com/marionrouault/LocalGlobal. Second-level maps for the contrast images of the study are freely available at https:// neurovault.
Trihalomethane Data Collection We designed a questionnaire to collect routine monitoring data on the concentration of total and individual THMs (chloroform, bromodichloromethane, dibromochloromethane, and bromoform) in drinking water (in micrograms per liter) at the tap, distribution network, or water treatment plant from the latest year(s) available. We requested information on the annual average concentration, standard deviation, median, range, and the number of measurements at national or regional levels. The questionnaire also ascertained the institution/person providing the information, reporting year and geographic region, population served, main disinfectants used, and maximum permissible level for THMs according to the country's legislation. In addition, the corresponding raw THM data were requested.
We sent this questionnaire between May 2016 and April 2019 to the national contact people in the organizations maintaining water quality data, including public health institutes and universities. We explored other data sources (e. g. , open data online, reports, scientific literature, etc. ) in order to complement the information provided by the questionnaires. Due to the ecological design of the study and the anonymity of data, ethics approval was not sought. Table  1  describes the countries that completed the questionnaire and provides drinking water information available from other sources. For Croatia, Finland, Hungary, Lithuania, and Malta, we received only the completed questionnaire, while for 11 countries (Belgium, Cyprus, Czech Republic, Estonia, Greece, Italy, Latvia, Poland, Portugal, Slovenia, and the United Kingdom), we also received raw monitoring data at different reporting levels (tap, city/village, water zone, province, and region).
For Germany, Greece, and Luxemburg, we used municipal and water authorities' online data and reports. For Italy, we obtained partial data provided directly from participating municipalities, complemented by online data. For Cyprus, Denmark, the Netherlands, Slovakia, and Sweden, we directly obtained data after personal communication with the respective authorities or researchers. For France, Ireland, and Spain, recent country-level THM information was published  (Water_Team 2014; Corso et al. 2017; Palau and Guevara 2014) ; hence, reference people were not contacted. Nine countries reported nonweighted THM measurements or means (Croatia, Denmark, Finland, Germany, Hungary, Malta, the Netherlands, Slovakia, and Sweden), while for the rest, population-weighted THM measurements were reported or calculated.
In Austria, Bulgaria, and Romania, key people were not identified or did not participate, or recent THM data were not available. For these three countries, we performed an online literature review to identify recent scientific and gray literature in English or in the national language using Google Translate. We used PubMed, Google Scholar, Mendeley (www. mendeley. com), and official websites using the following keywords in the Google search engine: (country name) AND [(drinking water) OR (potable water)] AND [(trihalomethanes) OR (THMs) OR (disinfection byproducts) OR (chlorination byproducts)]. The literature search identified reports of THM levels measured before the year 2000 for Austria  (Premazzi et al. 1997) , where expected THM levels are very low, since 99. 7%% of drinking water is from underground sources and ultraviolet (UV) radiation is the predominant disinfection method (A Indra, personal communication).
Hence, we included Austria in the estimation of the EU average. We could not find THM data for Bulgaria. In Romania, we obtained published THM data from 2006-2015 for 8 individual cities and for small supply areas in 10 counties  (Cohl et al. 2015; Dirtu et al. 2016; Kovacs et al. 2007; Thach et al. 2012). We obtained additional data (mean, minimum, study area, population coverage, collection points, and disinfection methods) related to the study of  Dirtu et al. 2016  after personal communication with the researcher (D. Dirtu, personal communication). We calculated the population-weighted average considering the population covered by these studies (7. 4% of the total population in Romania) and assigned this weighted THM mean estimate for Romania as a whole. Because we had limited data for Romania and no data for Bulgaria, we did not include these countries when estimating the average annual THM exposure for the European Union as a whole.