Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Effects of nutrient enrichment on freshwater macrophyte and invertebrate abundance: A meta-analysis

<p>The zip-file contains the data and code accompanying the paper 'Effects of nutrient enrichment on freshwater macrophyte and invertebrate abundance: A meta-analysis'. Together, these files should allow for the replication of the results.</p> <p>The 'raw_data' folder contains the 'MA_database.csv' file, which contains the extracted data from all primary studies that are used in the analysis. Furthermore, this folder contains the file 'MA_database_description.txt', which gives a description of each data column in the database.</p> <p>The 'derived_data' folder contains the files that are produced by the R-scripts in this study and used for data analysis. The 'MA_database_processed.csv' and 'MA_database_processed.RData' files contain the converted raw database that is suitable for analysis. The 'DB_IA_subsets.RData' file contains the 'Individual Abundance' (IA) data subsets based on taxonomic group (invertebrates/macrophytes) and inclusion criteria. The 'DB_IA_VCV_matrices.RData' contains for all IA data subsets the variance-covariance (VCV) matrices. The 'DB_AM_subsets.RData' file contains the 'Total Abundance' (TA) and 'Mean Abundance' (MA) data subsets based on taxonomic group (invertebrates/macrophytes) and inclusion criteria.</p> <p>The 'output_data' folder contains maps with the output data for each data subset (i.e. for each metric, taxonomic group and set of inclusion criteria). For each data subset, the map contains random effects selection results ('Results1_REsel_<subset>.csv'), the fixed effects selection results ('Results2_FEsel_<subset>.csv'), the random variance components and R^2 values for the best models subset ('Results3_BestModels_<subset>.csv'), the parameter value estimations for the fixed effects ('Results4_Parameters_<subset>.csv'), the standard errors for the estimated parameter values ('Results5_SE_<subset>.csv'), and the consensus model parameter values ('Results6_ConsensusModel_<subset>.csv'). Furthermore, each map contains a file with the best-selected random effects model structure ('BestRanEf_<subset>.RData'), the model with the best-selected random effects structure without moderators (only for IA) ('BestRanEfModel_<subset>.RData'), and a file with the consensus model ('ConsensusModel_<subset>.RData').</p> <p>The 'scripts' folder contains all R-scripts that we used for this study. The 'PrepareData.R' script takes the database as input and adjusts the file so that it can be used for data analysis. The 'PrepareDataIA.R' and 'PrepareDataAM.R' scripts make subsets of the data and prepare the data for the meta-regression analysis and mixed-effects regression analysis, respectively. The regression analyses are performed in the 'SelectModelsIA.R' and 'SelectModelsAM.R' scripts to calculate the regression model results for the IA metric and MA/TA metrics, respectively. These scripts require the 'RandomAndFixedEffects.R' script, containing the random and fixed effects parameter combinations, as well as the 'Functions.R' script. The 'CreateMap.R' script creates a global map with the location of all studies included in the analysis (figure 1 in the paper). The 'CreateForestPlots.R' script creates plots showing the IA data distribution for both taxonomic groups (figure 2 in the paper). The 'CreateHeatMaps.R' script creates heat maps for all metrics and taxonomic groups (figure 3 in the paper, figures S11.1 and S11.2 in the appendix). The 'CalculateStatistics.R' script calculates the descriptive statistics that are reported throughout the paper, and creates the figures that describe the dataset characteristics (figures S3.1 to S3.5 in the appendix). The 'CreateFunnelPlots.R' script creates the funnel plots for both taxonomic groups (figures S6.1 and S6.2 in the appendix) and performs Egger's tests. The 'CreateControlGraphs.R' script creates graphs showing the dependency of the nutrient response to control concentrations for all metrics and taxonomic groups (figures S10.1 and S10.2 in the appendix).</p> <p>The 'figures' folder contains all figures that are included in this study.</p>

opencc-by-4.0Apr 2024View details →

Data from: Transcriptome analysis of apical meristem enriched bud samples for size dependent flowering commitment in Crocus sativus reveal role of sugar and auxin signalling

<p><strong>Background</strong></p> <p>Cultivation of <em>Crocus sativus</em> (saffron) faces challenges due to inconsistent flowering patterns and variations in yield. Flowering takes place in a graded way with smaller corms unable to produce flowers. Enhancing the productivity requires a comprehensive understanding of the underlying genetic mechanisms that govern this size based flowering initiation and commitment. Therefore, samples enriched with non-flowering and flowering apical buds from small (<6g) and large (>14g) corms were sequenced. </p> <p><strong>Methods and Results</strong></p> <p>Apical bud enriched samples from small and large corms were collected immediately after break of dormancy in July. RNA sequencing was performed using Illumina Novaseq 6000. <em>De-novo</em> transcriptome assembly and analysis using flowering committed buds from large corms at post-dormancy and their comparison with vegetative shoot primordia from small corms pointed out the major role of Auxin and ABA hormonal regulation. Many genes with known dual responses in flowering development and circadian rhythm like Flowering locus T and Cryptochrome 1 along with a transcript showing homology with small auxin upregulated RNA (SAUR) exhibited induced expression in flowering buds. Thorough prediction of <em>Crocus sativus</em> non-coding RNA repertoire has been carried out for the first time. Enolase was found to be acting as a major hub with protein-protein interaction analysis using Arabidopsis counterparts.</p> <p><strong>Conclusion</strong></p> <p>Transcripts belong to key pathways including phenylpropanoid biosynthesis, hormone signaling and carbon metabolism were found significantly modulated. KEGG assessment and protein-protein interaction analysis confirm the expression data. Findings unravel the genetic determinants driving the size-dependent flowering in <em>Crocus sativus</em>.</p>

opencc-zeroAug 2022View details →

Matlab example for Local Enrichment Analysis (LEA) analysis with real data

<p>Phenotypic plasticity is essential to the immune system, yet the factors that shape it are not fully understood. Here, we comprehensively analyze immune cell phenotypes including morphology across human cohorts by single-round multiplexed immunofluorescence, automated microscopy, and deep learning. Using the uncertainty of convolutional neural networks to cluster the phenotypes of 8 distinct immune cell subsets, we find that the resulting maps are influenced by donor age, gender, and blood pressure, revealing distinct polarization and activation-associated phenotypes across immune cell classes. We further associate T-cell morphology to transcriptional state based on their joint donor variability, and validate an inflammation-associated polarized T-cell morphology, and an age-associated loss of mitochondria in CD4+ T-cells. Taken together, we show that immune cell phenotypes reflect both molecular and personal health information, opening new perspectives into the deep immune phenotyping of individual people in health and disease.</p>

opencc-by-4.0May 2018View details →

Example Datasets for Functional Enrichment Analysis

<p>This dataset contains a set of example data for a functional enrichment tutorial.</p>

opencc-zeroJul 2021View details →

Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis

<p><b>Background</b> </p> <p>RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.</p> <p><b>Results</b></p> <p>With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.</p> <p><b>Conclusion</b></p> <p>RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.</p>

opencc-by-4.0May 2023View details →

Pan-cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics

<p>CPTAC PAN-cancer Data Repository</p> <p>Welcome to the CPTAC PAN-cancer Data Repository! This repository serves as a data repository for the CPTAC PAN-cancer effort, which focuses on cancer target discovery. It contains various data sets related to protein abundance estimation, derived TMT-TPA, iBAQ, iBAQ-derived copy number, and differential protein expression for CPTAC ten indications.</p> <p>## Contents</p> <p>The repository includes the following data:</p> <p>- FragPipe Output: Protein abundance estimation data generated using the FragPipe software.<br> - Derived TMT-TPA: Data derived from Tandem Mass Tag (TMT) based Total Protein Approach (TPA).<br> - iBAQ: Data representing intensity-based absolute quantification (iBAQ) of proteins.<br> - iBAQ-derived Copy Number: Data derived from iBAQ analysis for copy number estimation.<br> - Differential Protein Expression: Data indicating differential expression of proteins between tumor and NAT.</p> <p>## Data Organization</p> <p>The data in this repository is organized in a structured manner to facilitate easy access and navigation. The repository structure is as follows:</p> <p>FragPipe/<br> [fragpipe_data_files]<br> Derived_TMT_TPA/<br> [derived_tmt_tpa_data_files]<br> iBAQ/<br> [ibaq_data_files]<br> iBAQ-derived_copy_number/<br> [ibaq_copy_number_data_files]<br> Differential_protein_expression/<br> [differential_expression_data_files]</p>

publicAug 2022View details →

Matlab example for Local Enrichment Analysis (LEA) analysis with real data

Open the record for dataset details and reuse information.

publicJul 2021View details →

Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis

Open the record for dataset details and reuse information.

dryad36/100

Trophic regulation of soil microbial biomass under nitrogen enrichment: A global meta-analysis

<p>Eutrophication, including nitrogen (N) enrichment, can affect soil microbial communities through changes in trophic interactions. However, a knowledge gap still exists about how plant resources ('bottom-up effects') and microbial predators ('top-down effects') regulate the impacts of N enrichment on microbial biomass at the global scale.</p> <p>To address this knowledge gap, we conducted a global meta-analysis using 2885 paired observations from 217 publications to evaluate the regulatory effects of plant biomass and soil nematodes on soil microbial biomass under N enrichment across terrestrial ecosystems.</p> <p>We found that the effects of N enrichment on soil microbial biomass strongly varied across ecosystems. N enrichment decreased the soil microbial biomass of natural grasslands and forests due to soil acidification and the subsequent losses of predatory and microbivorous nematodes stimulating microbial growth. By contrast, N enrichment increased the microbial biomass of managed croplands mainly via increasing plant biomass production. The short-term of N enrichment (experimental duration ≤ 5 years) could reduce microbial biomass via decreasing nematode abundance across diverse ecosystems, whereas the long-term of N enrichment (experimental duration > 5 years) mainly promoted microbial biomass via increasing plant biomass.</p> <p>These findings highlight the critical roles of microbial predators and plant input in shaping microbial responses to N enrichment, which are highly dependent on ecosystem type and the period of N enrichment. Earth system models that predict soil microbial biomass and their linkages to soil functioning should consider the variations in plant biomass and soil nematodes under future scenarios of N deposition.</p>

opencc-zeroJan 2024View details →

opencc-by-4.0Jan 2024View details →

PPARδ dataset curated and enriched using the Enalos tools and Enalos KNIME nodes for machine learning analysis (SCENARIOS project)

<p><span>A curated and enriched dataset for PPAR</span>δ<span>, suitable for in silico model development, was obtained from PubChem BioAssay under the numeric identifier AID 469785 using Enalos tools and Enalos KNIME nodes. This dataset comprises 136 compounds that induce luciferase activity, serving as an indicator of agonist activity against the human PPAR</span>δ<span> ligand-binding domain. These molecules were tested in a human embryonic kidney cell line (293T), co-transfected with a chimeric plasmid containing the yeast GAL4 DNA-binding domain (DBD). All 136 oxazole-based compounds retrieved from the dataset are accompanied by their half-maximal effective concentration (EC50) and enriched with 777 molecular descriptors extracted from their 2D structure using EnalosMold2 KNIME nodes</span></p>

openapache2.0Nov 2024View details →

SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis

<h1>SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis</h1> <div> </div> <div>SaintGSE is an artificial intelligence model designed to predict human gene-pathway relationships using large-scale differentially expressed gene (DEG) datasets. By leveraging an autoencoder and the SAINT transformer model, SaintGSE overcomes challenges in gene expression analysis, such as data scarcity, model compatibility, and interpretability. This project fine-tuned codes from the SAINT project (https://github.com/somepago/saint), licensed under the Apache License 2.0. </div> <div> </div> <div> </div> <h2>Key Features</h2> <div> </div> <div>  * AI-Driven Pathway Prediction: Uses autoencoders and the SAINT model to analyze gene expression data and predict related signaling pathways.</div> <div> </div> <div>  * Osteoarthritis Study: Applied to osteoarthritis (OA) to identify key pathways and potential therapeutic targets.</div> <div> </div> <div>  * Explainability: Utilizes Shapley additive explanations (SHAP) to interpret model predictions and identify influential genes.</div> <div> </div> <div> </div> <h2>Installation</h2> <div> </div> <div>Before installation, we recommend to build a conda environment from the attached yml file and activate it.</div> <div>Our code has been tested with python=3.8 on linux.</div> <div> </div> <div>```</div> <div>$ cd /path/to/SaintGSE</div> <div>$ conda env create -f saintgse_env.yml</div> <div>$ conda activate saintgse_env</div> <div>```</div> <div> </div> <div>After downloading all the files, please extract the contents of all compressed directories by running the following command in your terminal:</div> <div> </div> <div>```</div> <div>$ find . -name "*.tar.gz" -exec tar -xzvf {} \;</div> <div>$ rm *.tar.gz</div> <div>```</div> <div> </div> <div>Once the file structure is formed as follows, the preparation for using SaintGSE is complete.</div> <div> </div> <div>```</div> <div>.</div> <div>├── datasets</div> <div>│   ├── AE_100cycle_model.pth</div> <div>│   ├── AE_enrichment.tsv</div> <div>│   ├── AEshap</div> <div>│   │   ├── shap_values_latent_dim_1.csv</div> <div>│   │   ├── shap_values_latent_dim_2.csv</div> <div>│   │   ├── shap_values_latent_dim_3.csv</div> <div>│   │   ├── ...</div> <div>│   │   ├── ...</div> <div>│   │   └── shap_values_latent_dim_256.csv</div> <div>│   ├── bestmodels</div> <div>│   │   └── binary</div> <div>│   │       ├── Chronic_Myeloid_Leukemia</div> <div>│   │       │   └── testrun</div> <div>│   │       │       └── saint_gse_model.pth</div> <div>│   │       │── ...</div> <div>│   │       └── Selencompound_Biosynthesis</div> <div>│   │           └── testrun</div> <div>│   │               └── saint_gse_model.pth</div> <div>│   ├── gene_list.pkl</div> <div>│   ├── MGI_Gene_Model_Coord.tsv</div> <div>│   └── pathway_list_in_DEG.txt</div> <div>```</div> <div> </div> <div> <p>The code in this dataset is also accessible via GitHub. You can find the GitHub repository at the following link:</p> <p><a href="https://github.com/MSjeon27/SaintGSE" target="_new" rel="noopener">https://github.com/MSjeon27/SaintGSE</a></p> </div> <div> </div> <h2>DEG dataset preparation</h2> <div>Prior to SaintGSE analysis, prepare DEG data to be used as input in .tsv format as follows. In the column, the official gene symbol of DEGs is located, and the row adds the log2 fold change value in each DEG group. An example is as follows.</div> <div> </div> <div>```</div> <div>LAP3 CD99 HS3ST1 MAD1L1 LASP1 SNX11</div> <div>'mock-6' vs 'LPS-6' -1.3 0 2.4 0 0.7 0</div> <div>'mock-6' vs 'EBOV-6' -1.3 0 2.3 0 0.6 0</div> <div>```</div> <div> </div> <h2>Usage</h2> <div> </div> <h3>Step 0. Preprocessing the input DEG (from pyDESeq2 result)</h3> <div> </div> <div>Currently, SaintGSE has the function of converting mouse genes into human genes. The preprocessing code serves to change the human or mouse DEG data into the format used for SaintGSE.</div> <div> </div> <div>* human DEGs</div> <div>```</div> <div>$ preprocessing.py --query_fc /path/to/your/DEGs.tsv --out Preprocessed_fc.tsv</div> <div>```</div> <div> </div> <div>* mouse DEGs</div> <div>```</div> <div>$ preprocessing.py --query_fc /path/to/your/DEGs.tsv --org mouse --out Preprocessed_fc.tsv</div> <div>```</div> <div> </div> <div> </div> <h3>Step 1. Training SaintGSE for a target pathway</h3> <div> </div> <div>SaintGSE can be used to analyze new gene expression datasets for pathway prediction:</div> <div> </div> <div>```</div> <div>$ SaintGSE.py --pathway 'Proteins Involved in Osteoarthritis' --pretrain</div> <div>```</div> <div> </div> <div> </div> <h3>Step 2. Prediction through SaintGSE</h3> <div> </div> <div>```</div> <div>$ SaintGSE.py --predict Preprocessed_fc.tsv --pathway 'Proteins Involved in Osteoarthritis'</div> <div>```</div> <div> </div> <div>The results of the predictions are as follows.</div> <div> </div> <div>```</div> <div>tensor([[1.]], device='cuda:0')</div> <div>```</div> <div> </div> <div>This indicates that your DEG data is related to the target signaling path.</div> <div> </div> <div> </div> <div> </div> <h3>Step 3. Interpretation the result of SaintGSE (Get Relative SHAP contribution for each DEGs)</h3> <div>```</div> <div>$ Interpret.py -d Preprocessed_fc.tsv -p 'Proteins Involved in Osteoarthritis'</div> <div>```</div> <div> </div> <div>The result of interpretation produces the following files for each sample.</div> <div> </div> <div>```</div> <div><Sample_name>_<target_pathway>_significant_gene_shap.csv</div> <div>```</div> <div> </div> <div>This represents the relative SHAP contribution for each gene in the DEG data. In the subsequent analysis, it is recommended to focus on the genes with the relative SHAP contributions in the top 35% to 50% as we suggested in the paper, depending on the number of DEGs.</div> <div> </div> <div> </div> <h2>How to Cite</h2> <div> </div> <div>If you use this model or repository in your research, please cite it as follows:</div> <div> </div> <div>```</div> <div>Jeon, MS & Nam, JH et al., "SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis," 2024. GitHub repository. Available at: https://github.com/MSjeon27/SaintGSE</div> <div>```</div> <div> </div> <div>For more information or any questions regarding citation, feel free to contact us (msjeon27@cau.ac.kr).</div> <p> </p>

opencc-by-4.0May 2024View details →

Gene Enrichment Map Data from gProfiler Analysis - Selected MPK Interactions of Arabidopsis thaliana

<p>Gene enrichment analysis results for the selected predicted MPK interactions are included in the supplementary materials.</p>

opencc-by-4.0Jul 2024View details →

MUSE Analysis of Gas around Galaxies (MAGG) -- VI. The cool and enriched gas environment of z≳3 Lyα emitters

<p>Full sample of MgII absorption-line systems identified at z>3 in the MUSE Analysis of Gas around Galaxies (MAGG) survey presented in Galbiati et al. 2024.</p> <p>Each absorber has been modeled by a combination of Voigt profiles which are shown on top of the NIR quasar spectra obtained with X-shooter. </p>

opencc-by-4.0Jul 2019View details →

Supplementary tables for "Theme Enrichment Analysis: A Statistical Test for Identifying Significantly Enriched Themes in a List of Stories with an Application to the Star Trek Television Franchise"

<p>Supplementary tables for the manuscript "Theme Enrichment Analysis: A Statistical Test for Identifying Significantly Enriched Themes in a List of Stories with an Application to the Star Trek Television Franchise".</p> <p>Supplementary Information File 1 contains a table of Star Trek TOS/TAS/TNG television series episodes featuring the Klingon alien race. The criterion for inclusion is that the Klingons were deemed by the authors to have been featured throughout the episode in a way that is central to the story plot.</p> <p>Supplementary Information File 2 contains tables of over-represented Literary Theme Ontology version 0.1.1 literary themes in Star Trek TOS/TAS/TNG television series storysets as identified by the hypergeometric test.</p> <p>Supplementary Information File 3 contains tables of over-represented Literary Theme Ontology version 0.1.1 literary themes in Star Trek TOS/TAS/TNG television series storysets as identified by the TF-IDF statistic.</p>

opencc-by-4.0Sep 2021View details →

Transcriptome analysis of T47D cells and H2A.J-KO derivatives for the paper entitled: The histone variant H2A.J is enriched in luminal epithelial gland cells