dataesr/scientific-paragraphs-categorization huggingface dataset:
The configuration file is openly available 1 and fully customizable, so it can be adapted to different datasets and expanded to include more variables and conversion rules. To conclude, we identified processing strategies to maximise the consistency across two large datasets, Whitehall II and UK Biobank, for the study of WMHs. We harmonised non-imaging variables and proposed a processing pipeline to minimise the effect of non-biological sources of difference in the imaging data. The main recommendations emerging from this work are the following: • Use WMH manual masks generated from the same rater whenever possible and establish guidelines to maximise consistency of the manual masks; • Perform bias field correction; • Carefully consider the trade-off between improving segmentation performance with additional modalities (e.
g. , FA) and using a smaller set of modalities (T1-weighted and FLAIR), which are more reliably present across studies and provide better dataset harmonisation; • Train BIANCA on data coming from a mix of different scanners/studies when working with more than one dataset. We showed that with these steps, and appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables, we can provide highly consistent WMH measures. These results open up a wide range of applications for the study of WMHs and potentially other neuroimaging markers across extensive databases of clinical data.
Acknowledgments We thank all Whitehall II participants for their time, the  Whitehall II  staff at the  University College London ,  Mandy Pipkin  and  Barbora Krausova  for assisting with recruitment and data collection, the FMRIB Radiographers team for data acquisition,  Dr. Christoph Arthofer  for the helpful discussions, IT and support teams at the  Wellcome Centre for Integrative Neuroimaging  for their helpful collaboration.
L. G. was also supported by the  Oxford Parkinson's Disease Centre (Parkinson's UK Monument Discovery Award ,  J-1403 ) and the  MRC Dementias Platform UK  ( MR/L023784/2 ). E. Zs, K. P. E. and S. S. were supported by the  European Union 's  Horizon 2020 programme 'Lifebrain'  ( 732592 ). S. S. was also supported by an  Alzheimer 's  Society Junior Research Fellowship (Grant ref :  441 ). V. S. and M. J. were supported by the  Wellcome Centre for Integrative Neuroimaging , which has core funding from the  Wellcome Trust  ( 203139/Z/16/Z ). G. Z. was supported by the  Italian Minister of Education (MIUR). M. J. was supported by the  National Institute for Health Research (NIHR)  Oxford  BRC , and this research was funded by the  Wellcome Trust  ( 215573/Z/19/Z ). A. S. M. receives research support from the  US National Institutes of Health  ( R01AG056477 ).
Data/code availability statement The Whitehall study follows MRC data sharing policies (https://www. mrc. ac. uk/research/policies-and-guidance-forresearchers/data-sharing/). Data will be accessible via the Dementias Platform UK (https://portal. dementiasplatform. uk/) after 2020. This research has been conducted using the UK Biobank Resource (applications number 42596 and 8107). The FUNPACK configuration file and FSL-BIANCA training sets are openly available (https://issues. dpuk. org/eugeneduff/wmh_ harmonisation).
Introduction The development of next generation sequencing (NGS) methods has boosted the rapid generation of large datasets and RNA sequencing (RNA-seq) has become the standard for performing robust transcriptional profiling and thus quantifying gene expression in various contexts. Next to the comparison of two conditions, the generation of time-series RNAseq data has become amenable and popular, allowing to monitor the gene expression dynamics over a process such as development, ageing or cancerogenesis. While web-based, user-friendly R shiny apps have become available recently for differential expression analysis and data visualization of RNA-seq data,  [1] [2] [3] [4] [5] [6] [7]  the analysis of time-series data within R remains largely command-line based and therefore challenging for bench scientists without programming knowledge.
We here present RNfuzzyApp, a user-friendly, web-based R shiny app with an intuitive user interface for the full workflows of differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp provides an interface for easy and fast data normalization and differential analysis using several methods, a variety of interactive plots for a quick overview of data and results, and an easy-to-use interface for the complete pipeline of time-series expression analysis using the fuzzy clustering algorithm Mfuzz. 8 In addition, RNfuzzyApp offers ID conversion, orthology assignment and enrichment analysis using gprofiler2. 9 We show the usability of RNfuzzyApp on two examples: an RNA-seq dataset of the ageing limb muscle of mouse, as well as developmental time-series RNA-seq data of the Drosophila melanogaster leg.
Implementation RNfuzzyApp was built in R (V. 4. 0. 4) using the Shiny framework. The app currently depends on the following R packages: shiny, shinydashboard, shinycssloaders, shinythemes, shinyWidgets, shinyBS, rmarkdown, plotly, dplyr, RColorBrewer, utils, tidyr, devtools, cluster, DESeq2, 10 edgeR,  11  TCC,  12  including baySeq,  13  heatmaply, 14 gprofiler2,  9  Mfuzz,  8  as well as the package e1071. As a basic feature and to allow users to upload any identifier for analysis, ID conversion is included, using the gprofiler2 package.
Differential expression analysis workflow of RNfuzzyApp The differential expression analysis workflow of RNfuzzyApp can be divided in three main parts: the upload and visualization of the raw data; the normalization of the data and the differential expression analysis; and finally, enrichment REVISED Amendments from  Version 1  We have tried to clarify a few points that seemed unclear in the paper. Furthermore, we have created a detailed user manual, including detailed instructions how to install the software and how to run its different functions. We now also provide test data files (which are used both, in the paper, as well as the manual) so that researchers can test our software and also verify the correctness of the format of their data. We have meanwhile also updated RNfuzzyApp, and added new functionalities.
1) It is now possible to compare two datasets, even if more than 2 datasets were initially uploaded. For this, the Filter data function has been introduced. 2) Groups are now assigned automatically. The user has to follow the guideline on how to format the column names (condition1_replicate1, condition1_replicate2, etc. ). 3) It is now possible to directly load DEG gene lists into the enrichment function of RNfuzzyApp, making the analysis more streamlined. Any further responses from the reviewers can be found at the end of the article analysis of results and orthology assignment (Figure  1a ). In each part, several options exist for visualizing the data and thus getting a first-hand impression of the quality of the data, as well as the filters that are applied.
Data normalization and differential expression analysis RNfuzzyApp offers several packages for data normalization, as well as for differential expression analysis. Normalization can be done using DESeq2; TMM (trimmed mean of M values), RLE (relative log expression) or upperquantile offered by edgeR; finally the TCC package providing TMM or DESeq2 normalization. As for raw read counts, the count distribution, a heatmap for clustering samples (Figure  3b ), as well as PCA analysis with a 2D as well as 3D PCA plot (Figure  3c ) is available for visualising normalized data. Differential expression analysis can be done using DESeq2, edgeR and bayseq. If more than two conditions are uploaded, normalization and initial differential expression analysis will be done over the entire data set. However, it is often useful to perform pairwise comparison of two conditions or timepoints.
Enrichment analysis and orthology assignment. For enrichment analysis of Gene Ontology (GO-) terms, 16 pathways (Wikipathways,  17  Reactome 18 and KEGG  19  ), Human Protein Atlas,  20  CORUM data on protein complexes,  21  and TRANSFAC,  22  the gprofiler2 package is included in RNfuzzyApp. Results are displayed as an image of overall enrichment, as well as a results table. The table together with a bar plot of the enriched term names sorted according to p-value are downloadable by the user (Figure  4b , bar plot of enrichments generated from downloaded table). Gprofiler2 is also used to find orthologs in another species of a userprovided list of genes. To this end, the user simply needs to upload a list of genes, and select the original and the target species.
Data preparation Tabula muris senis  15  limb muscle raw read data (data accessible at NCBI GEO database,  23  accession GSE132040) were taken as is and read into RNfuzzyApp for data processing and differential expression analysis. Raw read data from developing leg muscle  24  (data accessible at NCBI GEO database, accession GSE143430) were first averaged over replicates before reading them into RNfuzzyApp, as Mfuzz does not accept replicates (available as Habermann, Bianca; Haering, Margaux (2021), extended Datatables). In the parameters box, settings have to be chosen, such as the cluster number submitted for enrichment, the number of results to show, the organism, as well as the databases used for enrichment analysis. The plot on the right-hand side shows the enrichment of cluster 9. (c) Enrichment of the top 10 processes from cluster 10 and cluster 12.
RNA-seq analysis of Tabula muris senis bulk RNA-seq data on the ageing limb muscle We used data from the ageing limb muscle from the Tabula muris senis project (GSE GSE132040 15 ). We selected three time-points: 3 months, 12 months and 27 months. We only used samples from male mice. After data upload, we filtered for lowly expressed genes with less than 50 read counts. We then visualized raw read counts of all samples (Figure  3a ). After normalization using DESeq2, we compared samples using hierarchical clustering (Figure  3b ), which showed that replicates cluster together. We also performed PCA analysis and could confirm that samples from the same time-point cluster together (Figure  3c ). We next subjected samples to differential expression analysis using DESeq2, comparing all time-points against each other (see Extended data: Tables  1a-c ).
Mfuzz soft clustering of a time-series of RNA-seq data from the developing Drosophila leg We used RNA-seq data from a developmental time course of Drosophila leg for soft clustering using the Mfuzz pipeline included in RNfuzzyApp. We used normalized read counts from GEO  25  gene expression dataset GSE143430  24  and uploaded it to RNfuzzyApp. In brief, leg samples had been collected at three stages during pupal development (30, 50 and 72 h APF) and had been subjected to RNA-sequencing. We wanted to analyse the wild-type expression profiles of genes during these three developmental stages and to identify potentially enriched terms and pathways. We first checked with the inertia method the ideal number of clusters (Figure  5a ). We chose 12 clusters, as we found no significant change with cluster numbers higher than that.
We chose to offer several packages for normalization, as well as differential expression analysis. This allows the user to exploit several possible combinations of tools for differential expression analysis. Our choice for enrichment analysis in this version of RNfuzzyApp fell on gprofiler2. There are many software tools available for enrichment analysis. Gprofiler2, however, is available also for non-standard model organisms. Therefore, our app can be used for organisms other than human, mouse, Drosophila, C. elegans or yeast. Moreover, gprofiler2 allows in addition ID conversion, as well as ortholog assignment and both these functions were made available in RNfuzzyApp. In future releases of RNfuzzyApp, we consider including more enrichment tools, providing a broader spectrum of data to include, such as EnrichR.
28  conclude, RNFuzzyApp is an intuitive and easy to use R shiny app that was designed for experimental biologists to enable them to perform RNA-seq and time-series RNA-seq analysis without the need of coding to get a fast overview of their data, results and figures. • Table  3a : Haering_etal_extendedDatatable_3a_DmLeg_cluster1_gpofiler. txt: gprofiler resuts of mfuzz clustering of Drosophila leg dataset (GSE143430), cluster 1.
Data availability • Table  3b : Haering_etal_extendedDatatable_3b_DmLeg_cluster2_gpofiler. txt: gprofiler resuts of mfuzz clustering of Drosophila leg dataset (GSE143430), cluster 2. • Table  3c : Haering_etal_extendedDatatable_3c_DmLeg_cluster3_gpofiler. txt: gprofiler resuts of mfuzz clustering of Drosophila leg dataset (GSE143430), cluster 3. • Table  3d : Haering_etal_extendedDatatable_3d_DmLeg_cluster4_gpofiler. txt: gprofiler resuts of mfuzz clustering of Drosophila leg dataset (GSE143430), cluster 4. • Table  3e : Haering_etal_extendedDatatable_3e_DmLeg_cluster5_gpofiler. txt: gprofiler resuts of mfuzz clustering of Drosophila leg dataset (GSE143430), cluster 5. • Table  3f : Haering_etal_extendedDatatable_3f_DmLeg_cluster6_gpofiler. txt: gprofiler resuts of mfuzz clustering of Drosophila leg dataset (GSE143430), cluster 6.
• Table  3g : Haering_etal_extendedDatatable_3g_DmLeg_cluster7_gpofiler. txt: gprofiler resuts of mfuzz clustering of Drosophila leg dataset (GSE143430), cluster 7. • Table  3h : Haering_etal_extendedDatatable_3h_DmLeg_cluster8_gpofiler. txt: gprofiler resuts of mfuzz clustering of Drosophila leg dataset (GSE143430), cluster 8. • Table  3i : Haering_etal_extendedDatatable_3i_DmLeg_cluster9_gpofiler. txt: gprofiler resuts of mfuzz clustering of Drosophila leg dataset (GSE143430), cluster 9. • Table  3j : Haering_etal_extendedDatatable_3j_DmLeg_cluster10_gpofiler. txt: gprofiler resuts of mfuzz clustering of Drosophila leg dataset (GSE143430), cluster 10. • Table  3k : Haering_etal_extendedDatatable_3k_DmLeg_cluster11_gpofiler. txt: gprofiler resuts of mfuzz clustering of Drosophila leg dataset (GSE143430), cluster 11.
• Table  3l : Haering_etal_extendedDatatable_3l_DmLeg_cluster12_gpofiler. txt: gprofiler resuts of mfuzz clustering of Drosophila leg dataset (GSE143430), cluster 12. • Table  4  Source code available from: https://gitlab. com/habermann_lab/rna-seq-analysis-app. Archived source code as at time of publication: Zenodo: RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis. https://doi. org/10. 5281/ zenodo. 5084275. 29 cense: GNU public license 3. that it will be used frequently. Haering and Habermann have clearly stated why the software is developed. RNAseq analyses has become a standard analytical approach. This software will give non-computational scientists to explore RNAseq data on several levels.
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Department of Biostatistics, University of Florida, Gainesville, FL, USA This manuscript presents an R shiny application called RNfuzzyApp. The app is designed for easeof-use for visualizing and analyzing RNA-seq datasets. The app is very nicely designed and has the potential to be very useful to scientists. However, a number of clarifications are needed. What is orthology assignment mean? This should be defined more clearly in the manuscript 1. It is not clear how to install or run the app. This took me a few tries to figure out. I finally just downloaded the folder from gitlab and then did: > library(shiny) > runApp("~/Downloads/rna-seq-analysis-app-master-App/App/").
Our response: We now provide test files for both functions of RNfuzzyApp , differential expression analysis, as well as Mfuzz clustering, on our gitlab account (in App/test_files). The "maximum upload size exceeded error" could be due to size limits within R shiny, which we have tried to circumvent. The size limit of R shiny can be changed with the following command: options(shiny. maxRequestSize = n*1024^2). We have changed this to 100MB, which should be sufficient for most datasets. In case of larger datasets, this would have to be set individually by the user.
Improve error handling, I tried uploading data with genes on the rows and the app crashed saying it had duplicate row names. The app also crashed or would freeze when trying to upload a dataset that was a 5. 5MB csv (8 samples). Are there limitations to the upload size? Our response: See our response above: it seems that R shiny has a rather low size limit for data upload, which can be changed with the command: options(shiny. maxRequestSize = n*1024^2). We have set it to 100MB, which should suffice for most datasets.
I highly suggest finding someone to try the app who was not involved in the development to 'try to break it' to identify issues like these. The authors re-analyzed two time course datasets from mouse and fruitfly to demonstrate the basic steps and workflows that can be executed with RNAfuzzy App. The package is distributed as a single R shiny app, that the authors design with the intent for easy installation and setup. The demonstrated workflows encompass known steps in RNA-seq analysis including filtering, quality control (via PCA/hierarchical clustering), normalization with several state-of-the-art methods and differential expression involving the accepted software packages Deseq2 and edgeR. The authors highlight the implementation of a soft-clustering workflow involving iterative runs of the Mfuzz algorithm (for analysis of time course trajectories), as well as diagnostic plots that are aiding the user towards correct selection of adjustable workflow parameters.
Finally, the authors use the clustering results as input for a functional enrichment workflow, that includes reference data from a range of pathway and transcription factor databases. The authors have pursued the development of a highly relevant software package that provides a very comprehensive set of functions and tools. Current RNA-seq data is generated at impressive scale yet remains frequently under-analyzed due to limited coding experience of the experimenters that generate the data. The implementation of elaborative clustering methods and diagnostic plots to aide the user in deciding for the right settings is particularly commendable. Similarly relevant is the implemented functional enrichment workflow, that is well-suited within this package, instead of just finishing the analysis with a set of gene lists that needs to be interpreted elsewhere.
The workflows are based on accepted practices and are in agreement with the field's 'standard' analyses. This is important and commendable as several other tools provide misleading workflows that experimental biologists are unaware of. However, the software package is currently challenging to install, several functions throw error messages or cause a crash of the app altogether. There is -at least as far as I can tell -very limited documentation (none regarding installation) on how to prepare input data or operate individual workflows. I provide specific details below. I consider myself proficient in bioinformatics in general and RNA-seq data analysis in R in particular, and I struggled and ultimately failed to operate this app. While I do acknowledge that the authors are not primarily responsible for the individual challenges the user may face -there are always unforeseeable issues -it needs to be noted that a significant emphasis of the manuscript is on the 'user-friendly' (mentioned at least six times in the manuscript) features of RNfuzzy.
It also makes the software, which is the heart of this manuscript, difficult to review. There is no link to an already-running 'reviewer web version' or a built-in function that loads a tutorial dataset (like the ones used in the manuscript). I have tried to load both the published datasets and a bulk RNA-seq dataset of my own but could not progress beyond loading and quality control due to frequent errors or crashes. That was probably due to some misformatting of the input data but without clear guidance on how to prepare the count matrix, or at least an already-formatted dataset, I do not see how untrained experimental biologists will be able to efficiently interact with this app. If the authors can improve on these aspects and after I have been able to verify the functionality of the software (at least with data provided by the authors or within a 'reviewer version'), I would support the indexing of the manuscript.
Major concerns: Currently there is no guidance on how to install and launch the app (at least to my knowledge). The authors state on page 3 that 'Installation instructions are also available. ' This is not correct -the adjacent github provides no instructions on how to install 1. against any prior filtering or data manipulations. Is the PCA plot calculated on the whole count matrix, or only the filtered one? Is the data somewhat transformed (rlog, varianceStabilizing transformation?) before running the PCA? The PCA function is currently running very slowly and may benefit from some sort of feature selection (e. g. top 10,000 expressed genes). 2. Why do the authors limit their time course analysis to just three timepoints of the published tabula muris datasets? The dataset contains up to 10 timepoints and it may be more convincing to demonstrate the usefulness of RNfuzzy on as many datapoints as possible.
So this input step is critical. To prevent failures here, the authors could provide a) a detailed description on the input format and/or b) the option to load a template/tutorial dataset Our response: a) We now provide all sample data with the App, in a folder called test_files that can be found at the RNfuzzyApp gitlab site and is downloaded when the software is downloaded. b) Covariates was a misleading term, in fact, we only allow read counts (from any type of experiment). We have removed it from the Input File Description. c) The genes can be provided as any valid identifier, e. g. Symbols, ENSEMBL IDs etc.
Our response: In fact, in the DESeq2 vignette, filtering is suggested (see the DESeq2 vignette, which states (http://bioconductor. org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2. html#pr e-filtering): 'Pre-filtering While it is not necessary to pre-filter low count genes before running the DESeq2 functions, there are two reasons which make pre-filtering useful: by removing rows in which there are very few reads, we reduce the memory size of the dds data object, and we increase the speed of the transformation and testing functions within DESeq2. Here we perform a minimal pre-filtering to keep only rows that have at least 10 reads total. Note that more strict filtering to increase power is automatically applied via independent filtering on the mean of normalized counts within the results function.
' However, the user can choose whether they want to do low read count filtering or not. 2. Is the PCA plot calculated on the whole count matrix, or only the filtered one? Is the data somewhat transformed (rlog, varianceStabilizing transformation?) before running the PCA? The PCA function is currently running very slowly and may benefit from some sort of feature selection (e. g. top 10,000 expressed genes). Our response: a) PCA analysis of raw read counts is done using all the genes. b) PCA analysis of normalized data can be done once differential expression analysis has been performed. In this case, the top genes, as well as the FDR-cutoff can be chosen by the user. In both cases, the data are log transformed using the log1p() function. 3. Why do the authors limit their time course analysis to just three timepoints of the published tabula muris datasets? The dataset contains up to 10 timepoints and it may be more convincing to demonstrate the usefulness of RNfuzzy on as many datapoints as possible.