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193 results for “co-expression”
Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine
<p>Supplementary Table Captions:</p> <p>Supplementary Table S9. Evaluation and parameter determination of host and microbiome RNA read classification using simulation datasets</p> <p>Supplementary Table S10. 40 pathways significantly upregulated in the cecum as compared to the transverse colon</p> <p>Supplementary Table S11. Host-microbiome gene co-expression network edges</p> <p>Supplementary Table S12. Host-host gene co-expression network edges</p> <p>Supplementary Table S13. Microbiome-microbiome gene co-expression network edges</p> <p>Supplementary Table S14. List of genes included in each gene module identified from the gene co-expression network</p> <p>Supplementary Table S15. Results of enrichment analysis for each gene module identified from the gene co-expression network</p> <p>Supplementary Table S16. The top 32 bacterial species in terms of expression abundance based on metatranscriptome profiles</p> <p>Supplementary Table S17. Number of microbiome RNA reads annotated by the KEGG database</p> <p>Supplementary Table S18. Results of enrichment analysis of gene modules for each parameter</p> <p>Supplementary Table S19. Evaluation of modules in each parameter of Newman algorithm</p> <p>Supplementary Table S20. Evaluation of modules in each parameter of Louvain algorithm</p> <p>Supplementary Table S21. Evaluation of modules in each parameter of Leiden algorithm</p> <p>Supplementary Table S22. Evaluation of modules in each parameter of WGCNA</p>
Data and trained models for: Human-robot facial co-expression
<p>Large language models are enabling rapid progress in robotic verbal communication, but nonverbal communication is not keeping pace. Physical humanoid robots struggle to express and communicate using facial movement, relying primarily on voice. The challenge is twofold: First, the actuation of an expressively versatile robotic face is mechanically challenging. A second challenge is knowing what expression to generate so that they appear natural, timely, and genuine. Here we propose that both barriers can be alleviated by training a robot to anticipate future facial expressions and execute them simultaneously with a human. Whereas delayed facial mimicry looks disingenuous, facial co-expression feels more genuine since it requires correctly inferring the human's emotional state for timely execution. We find that a robot can learn to predict a forthcoming smile about 839 milliseconds before the human smiles, and using a learned inverse kinematic facial self-model, co-express the smile simultaneously with the human. We demonstrate this ability using a robot face comprising 26 degrees of freedom. We believe that the ability co-express simultaneous facial expressions could improve human-robot interaction.</p>
Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across age and brain regions
<p>This is the online data repository accompanying the following manuscript:<br><strong>Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions</strong></p> <p><em>Giulio Pergola<sup>1,2,3,*</sup>, Madhur Parihar<sup>1</sup>, Leonardo Sportelli<sup>1,2</sup>, Rahul Bharadwaj<sup>1</sup>, Christopher Borcuk<sup>2</sup>, Eugenia Radulescu<sup>1</sup>, Loredana Bellantuono<sup>2,5</sup>, Giuseppe Blasi<sup>2,4</sup>, Qiang Chen<sup>1</sup>, Joel E. Kleinman<sup>1,3</sup>, Yanhong Wang<sup>1</sup>, Srinidhi Rao Sripathy<sup>1</sup>, Brady J. Maher<sup>1,3,7</sup>, Alfonso Monaco<sup>5,9</sup>, Fabiana Rossi<sup>1,2</sup>, Joo Heon Shin<sup>1</sup>, Thomas M. Hyde<sup>1,3,6</sup>, Alessandro Bertolino<sup>2,4,*</sup>, Daniel R. Weinberger<sup>1,7,8,*</sup></em></p> <p> </p> <p><strong>Affiliations:</strong></p> <p><em>1)Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (USA)<br>2)Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy<br>3)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>4)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy<br>5)Istituto Nazionale di Fisica Nucleare (INFN), Bari, Italy<br>6)Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>7)Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>8)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>9)Dipartimento Interateneo di fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy</em></p> <p> </p> <p><strong>Abstract:</strong></p> <p><em>Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.</em></p> <p> </p> <p><strong>Citation:</strong> <em>Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812</em></p> <p> </p> <p><strong>Data Files:<br>DLPFC hit.genes_kb_200__online.version.zip: </strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For Sankey plots, hover mouse over the links to see the list of genes. Also supports zoom, drag and selection.<br><strong>DLPFC hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>DLPFC all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes<br><strong>DLPFC all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP hit.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only<br><strong>HP hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes<br><strong>HP all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>Modulewise SCZ enrichment(1.0).xlsx:</strong><br>Excel file contains module level SCZ enrichment results for all networks<br><strong>wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx:</strong><br>Excel file contains WGCNA output for sliding window networks<br><strong>wide_form_WGCNA(v3.7.1)_final.xlsx:</strong><br>Excel file contains WGCNA output for our generated networks and from previously published networks<br><strong>libdnetworks(NC).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for age-parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study.<br><strong>libdnetworks(SCZ).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study.<br><strong>sample_matched_HP_DG_qsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study.<br><strong>sample_matched_HP_DG_noqsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study.<br><strong>stemcell.preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”.<br><strong>SCZ.ref.list.sciadv.ade2812.rds</strong>: List of All Biotypes/ Protein Coding Schizophrenia reference genelist for following bins: PGC3, 0 kbp, 20 kbp, 50 kbp, 100 kbp, 150 kbp, 200 kbp, 250 kbp, 500 kbp.</p> <p> </p> <p>Accompanying code can be found at: <a href="https://github.com/LieberInstitute/Brain_WGCNA">https://github.com/LieberInstitute/Brain_WGCNA</a><br>Data from this repository is also available at: <a href="https://nets.libd.org/age_wgcna/">https://nets.libd.org/age_wgcna/</a></p> <p> </p> <p>For any data inquiries please contact:<br><strong>Giulio Pergola: </strong><a href="mailto:Giulio.Pergola@libd.org"><strong>Giulio.Pergola@libd.org</strong></a></p> <p> </p>
Data and trained models for: Human-robot facial co-expression
Open the record for dataset details and reuse information.
Mouse and Human Co-expression maps and supplementary material for: "A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels"
<p>Co-expression maps of the human and mouse species derived from microarray data for the first release of the GeneFriend tool.</p> <p>The two co-expression maps have been compared in order to discern similarities and differences between the two species. The results have been described in the manuscript titled: "A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels".</p> <p>The supplementary material of the manuscript have also been included in this repository.</p> <p> </p>
Dataset for a Mouse and Rat heart trancriptomic and co-expression network analysis
<ul> <li>mouse_heart_data and rat_heart_expression contain GEO expression matrix for mouse and rat experiments.</li> <li>gse_gsm_mouse.txt and gse_gsm_rat.txt contain experiment IDs and series IDs from GEO.</li> <li>heart_quantNormData_mouse.tsv and heart_quantNormData_rat.tsv contain normalised expression matrices.</li> <li>heart_quantNormData_mouse_1sd.tsv and heart_quantNormData_rat_1sd.tsv contain the normalised expression matrices restricted to genes with a standard deviation higher than 1.</li> <li>fileForSCHypeThreshold0.5_heart.txt and fileForSCHypeThreshold0.75_heart.txt are the input for SCHype. schype_output_0.5th_heart.nodes.txt, schype_output_0.5th_heart.edges.txt, schype_output_0.75th_heart.nodes.txt and schype_output_0.75th_heart.edges.txt are the outputs.</li> <li>geneLists.zip contains gene lists used for ontology analysis (ENSEMBL gene id)</li> </ul>
Supplemental Information on the Weighted Gene Co-expression Network Analysis performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"
<p>Supplemental Information on the Weighted Gene Co-expression Network Analysis (WGNCA) performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"</p> <p>The results are sorted by the three species analysed: the two algae <em><span>Zygnema circumcarinatum</span></em><span> SAG 698-1b (<em>Zygnema</em>) and <em>Mesotaenium endlicherianum </em></span><span>SAG 12.97 (<em>Mesotaenium</em>); and the bryophyte <em>Physcomitrium patens</em></span><span><em> </em>strain Gransden 2004 (<em>Physcomitrium</em>).</span></p>
Robustness of organ morphology is associated with modules of co-expressed genes related to plant cell wall
<p>Reproducibility in organ size and shape is a fundamental trait of living organisms. The mechanisms underlying such robustness remain, however, to be elucidated. In the manuscript <a href="https://www.biorxiv.org/content/10.1101/2022.04.26.489498v1"><strong>"Robustness of organ morphology is associated with modules of co-expressed genes related to plant cell wall", </strong>doi: https://doi.org/10.1101/2022.04.26.489498</a>, we took the sepal of Arabidopsis as a model, and we investigated whether variability of gene expression plays a role in variation of organ morphology.</p> <p>To address this question, we produced a dataset composed of both transcriptomic and morphological information obtained from 27 individual sepals from wild-type plants.</p> <p>This repository contains the raw confocal image of 30 sepals used as starting point for the analysis, as well as their extracted contours as binary images. These images were used to recover the 3D shape of the sepals.</p> <p>The 30 abaxial sepals were collected at early stage 11, from three different Col-0 wild-type plants, labeled D, E and F, grown simultaneously in experimentally controlled standard conditions. Each sepal was imaged under a confocal microscope using autofluorescence. Immediately following imaging, the sepal was frozen in liquid nitrogen for RNA extraction, on which an RNA-seq analysis was performed.</p> <p><strong>Related informations :</strong></p> <ul> <li>The repository of the numerical tools used for 3D shape extraction as well as the results of geometrical measurements is <a href="http://forge.cbp.ens-lyon.fr/redmine/projects/florivar">here</a>.</li> <li>The repository of RNA-Seq analysis results of these same sepals is here.</li> <li>And the analysis tools used to relate geometrical measurements to RNA-seq data are here.</li> </ul>
LC-MS/MS data for WT human PANX1 with or without co-expressing Src-Y529F mutant
<p>Protein phosphorylation is one of the major molecular mechanisms regulating protein activity and function throughout the cell. Pannexin 1 (PANX1) is a large-pore channel permeable to ATP and other cellular metabolites. Its tyrosine phosphorylation and subsequent activation have been found to play critical roles in diverse cellular conditions, including neuronal cell death, acute inflammation, and smooth muscle contraction. Specifically, the non-receptor kinase Src has been reported to phosphorylate Tyr198 and Tyr308 of mouse PANX1 (equivalent to Tyr199 and Tyr309 of human PANX1), resulting in channel opening and ATP release. Although the Src-dependent PANX1 activation mechanism has been widely discussed in the literature, independent validation of the tyrosine phosphorylation of PANX1 has been lacking. Here, we show that commercially available antibodies against the two phosphorylation sites mentioned above—which were used to identify endogenous PANX1 phosphorylation at these two sites—are nonspecific and should not be used to interpret results related to PANX1 phosphorylation. We further provide evidence that neither tyrosine residue is a major phosphorylation site for Src kinase in heterologous expression systems. We call on the field to re-examine the existing paradigm of tyrosine phosphorylation-dependent activation of the PANX1 channel.</p>
Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks
<p>Data and Inferred Networks accompanying the manuscript entitled - “Aggregation of recount3 RNA-seq data improves the inference of consensus and context-specific gene co-expression networks” </p> <p>Authors: Prashanthi Ravichandran, Princy Parsana, Rebecca Keener, Kaspar Hansen, Alexis Battle </p> <p>Affiliations: Johns Hopkins University School of Medicine, Johns Hopkins University Department of Computer Science, Johns Hopkins University Bloomberg School of Public Health</p> <p>Description: </p> <p>This folder includes data produced in the analysis contained in the manuscript and inferred consensus and context-specific networks from graphical lasso and WGCNA with varying numbers of edges. Contents include:</p> <ul> <li> <p>all_metadata.rds: File including meta-data columns of study accession ID, sample ID, assigned tissue category, cancer status and disease status obtained through manual curation for the 95,484 RNA-seq samples used in the study. </p> </li> <li> <p>all_counts.rds: log2 transformed RPKM normalized read counts for 5999 genes and 95,484 RNA-seq samples which was utilized for dimensionality reduction and data exploration </p> </li> <li> <p>precision_matrices.zip: Zipped folder including networks inferred by graphical lasso for different experiments presented in the paper using weighted covariance aggregation following PC correction.</p> </li> <ul> <li> <p>The networks can be found as follows. First, select the folder corresponding to the network of interest - for example, Blood, this will then include two or more folders which indicate the data aggregation utilized, select the folder corresponding appropriate level of data aggregation - either all samples/ GTEx for blood-specific networks, this includes precision matrices inferred across a range of penalization parameters. To view the precision matrix inferred for a particular value of the penalization parameter X, select the file labeled lambda_X.rds</p> </li> <li> <p>For select networks, we have included the computed centrality measures which can be accessed at centrality_X.rds for a particular value of the penalization parameter X. </p> </li> <li> <p>We have also included .rds files that list the hub genes from the consensus networks inferred from non-cancerous samples at “normal_hubs.rds”, and the consensus networks inferred from cancerous samples at “cancer_hubs.rds”</p> </li> <li> <p>The file “context_specific_selected_networks.csv” includes the networks that were selected for downstream biological interpretation based on the scale-free criterion which is also summarized in the Supplementary Tables. </p> </li> </ul> <li> <p>WGCNA.zip: A zipped folder containing gene modules inferred from WGCNA for sequentially aggregated GTEx, SRA, and blood studies. Select the data aggregated, and the number of studies based on folder names. For example, blood networks inferred from 20 studies can be accessed at blood/consensus/net_20. The individual networks correspond to distinct cut heights, and include information on the cut height used, the genes that the network was inferred over merged module labels, and merged module colors. </p> </li> </ul>
Network analysis reveals rare disease signatures across multiple levels of biological organization - Co-expression dataset
<p>The GTEx-derived co-expression data in 38 tissues generated in Buphamalai et.al., Network analysis reveals rare disease signatures across multiple levels of biological organization, Nature Communications 2021. Please see the publication's Methods section for details.</p>
Additional raw data in `Cell-type-specific co-expression inference from single cell RNA-sequencing data'.
<p>This repository holds the additional raw data used to generate figures in the publication "<strong><em>Cell-type-specific co-expression inference from single cell RNA-sequencing data</em></strong>" (preprint version: <a href="http://source%20code%20repo%20for%20%60cell-type-specific%20co-expression%20inference%20from%20single%20cell%20rna-sequencing%20data%27./">https://www.biorxiv.org/content/10.1101/2022.12.13.520181v1</a>).</p> <p>Table of contents:</p> <ul> <li>Figure_1B.rds: <ul> <li>raw data of Figure 1B </li> <li>co-expression estimates of 500*499/2 gene pairs across 100 replicates for 7 methods under two settings of sequencing detph variations</li> </ul> </li> <li>Supplementary_Figure_1B.rds: <ul> <li>raw data of Supplementary Figure 1B </li> <li>co-expression estimates of 500*499/2 gene pairs across 100 replicates for 7 methods under two settings of sequencing detph variations</li> </ul> </li> <li>Supplementary_Figure_2.rds: <ul> <li>raw data of Supplementary Figure 2 </li> <li>empirical power evaluated for 4999 gene pairs and 6 methods</li> </ul> </li> <li>Figure_3B.rds: <ul> <li>raw data of Figure 3B</li> <li>co-expression estimates of a network of 500 genes for 9 methods across 100 replicates</li> </ul> </li> <li>Additional_Raw_Data.xlsx <ul> <li>raw data of Figure 3A: (geometric mean expression levels, co-expression estimates) for 4999 gene pairs and 11 methods</li> <li>raw data of Figure 3C: running times for 11 methods</li> <li>raw data of Supplementary Figure 3: (geometric mean expression levels, co-expression estimates) for 4999 gene pairs and 11 methods under two settings of sequencing detph variations</li> </ul> </li> </ul>
S1314, Co-expression Extrapolation (COXEN) Program to Predict Chemotherapy Response in Patients With Bladder Cancer
ClinicalTrials.gov study NCT02177695. IPD Sharing: YES. Countries: 1. Publications: 3.
LC-MS/MS data for WT human PANX1 with or without co-expressing Src-Y529F mutant
Open the record for dataset details and reuse information.
Data from: Dissecting nutrient-related co-expression networks in phosphate starved poplars
Phosphorus (P) is an essential plant nutrient, but its availability is often limited in soil. Here, we studied changes in the transcriptome and in nutrient element concentrations in leaves and roots of poplars (Populus × canescens) in response to P deficiency. P starvation resulted in decreased concentrations of S and major cations (K, Mg, Ca), in increased concentrations of N, Zn and Al, while C, Fe and Mn were only little affected. In roots and leaves >4,000 and >9,000 genes were differently expressed upon P starvation. These genes clustered in eleven co-expression modules of which seven were correlated with distinct elements in the plant tissues. One module (4.7% of all differentially expressed genes) was strongly correlated with changes in the P concentration in the plant. In this module the GO term "response to P starvation" was enriched with phosphoenolpyruvate carboxylase kinases, phosphatases and pyrophosphatases as well as regulatory domains such as SPX, but no phosphate transporters. The P-related module was also enriched in genes of the functional category "galactolipid synthesis". Galactolipids substitute phospholipids in membranes under P limitation. Two modules, one correlated with C and N and the other with biomass, S and Mg, were connected with the P-related module by co-expression. In these modules GO terms indicating "DNA modification" and "cell division" as well as "defense" and "RNA modification" and "signaling" were enriched; they contained phosphate transporters. Bark storage proteins were among the most strongly upregulated genes in the growth-related module suggesting that N, which could not be used for growth, accumulated in typical storage compounds. In conclusion, weighted gene coexpression network analysis revealed a hierarchical structure of gene clusters, which separated phosphate starvation responses correlated with P tissue concentrations from other gene modules, which most likely represented transcriptional adjustments related to down-stream nutritional changes and stress.
Data and code for Fitzgerald et al: MDD seeded co-expression networks
<p>Below is a decription of the data and code supplied within this repository related to Fitzgerald et al "Astrocyte fatty acid metabolism as a driver of risk for major depressive disorder"</p> <table> <tbody> <tr> <td>Generated data </td> </tr> <tr> <td>Data</td> <td>About</td> <td> </td> </tr> <tr> <td>All_GTEx_DLPFC_networks.RData</td> <td>Non-thresholded coexpression summary statistics for MDD risk genes in GTEx frontal cortex</td> <td> </td> </tr> <tr> <td>my_big_negative_GTEx_DLPFC_list.RData</td> <td>"All_GTEx_DLPFC_networks.Rdata" data filtered to those genes with R < -0.5 and FDR < 0.05</td> <td> </td> </tr> <tr> <td>my_big_positive_GTEx_DLPFC_list.RData</td> <td>"All_GTEx_DLPFC_networks.Rdata" data filtered to those genes with R > 0.5 and FDR < 0.05</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Generated code</td> </tr> <tr> <td>File</td> <td>About</td> <td>Related figure</td> </tr> <tr> <td>Chang_bootstrap.R</td> <td>Bootstrapping of coexpression networks in the Chang et all data for comparing FADS1 coexpressed genes across disease states</td> <td>Fig 4G</td> </tr> <tr> <td>CMC_QC.R</td> <td>Quality control for the common mind consortium data for validation of coexpression networks</td> <td>Supp</td> </tr> <tr> <td>Cont_vs_MDD_modscores.R</td> <td>Generating module scores in snRNA-seq data</td> <td>Fig 4E</td> </tr> <tr> <td>FADS1_clustering.R</td> <td>Clustering of snRNA-seq data using genes coexpressed with FADS1</td> <td>Fig 5</td> </tr> <tr> <td>gene_analysis.sh</td> <td>Annotation of GWAS summary statistics using Hi-C data</td> <td>Fig 2A</td> </tr> <tr> <td>gene_set_analysis.sh</td> <td>GWAS enrichment analysis using MAGMA</td> <td>Fig 6C</td> </tr> <tr> <td>GTEx_coexp_networks.R</td> <td>Generating seeded coexpression networks for MDD risk genes in the GTEx dataset</td> <td>Fig 2B</td> </tr> <tr> <td>GTEx_QC_1.R</td> <td>Filtering of the GTEx dataset</td> <td>NA</td> </tr> <tr> <td>GTEx_QC_2.R</td> <td>Normalisation and regression of technical covariates from the GTEx data</td> <td>NA</td> </tr> <tr> <td>Labonte_et_al_QC.R</td> <td>Quality control, filtering and regression of technical covariates from the Labonte et al dataset</td> <td>Fig 4F</td> </tr> <tr> <td>Milo_analysis.R</td> <td>Neighbourhood based analysis for differentially abundant nuclei between control and MDD nuclei</td> <td>Fig 5H</td> </tr> <tr> <td>Nagy_et_al_astro_subsetting.R</td> <td>Subsetting astrocytes from the full Nagy et al snRNA-seq dataset </td> <td>NA</td> </tr> <tr> <td>Network_analysis.R</td> <td>To generate and analyse a graph of coexpression networks</td> <td>Fig 3E</td> </tr> <tr> <td>NicheNet.R</td> <td>For a NicheNet analysis to infer patterns of cell-cell communication</td> <td>Fig 6F</td> </tr> <tr> <td>Vizium_analysis.R</td> <td>Processing spatial RNA-seq data and generating cell scores for spatial inference of identified cell states</td> <td>Fig 5F</td> </tr> </tbody> </table>
Transcription Factor Co-Expression Mediates Lineage Priming for Embryonic and Extra-Embryonic Differentiation
<p>In early mammalian development, cleavage stage blastomeres and inner cell mass (ICM) cells co-express embryonic and extra-embryonic transcriptional determinants. Using a double protein-based reporter we identify an embryonic stem cell (ESC)population that co-expresses the extra-embryonic factor GATA6 alongside the embryonic factor SOX2. Based on single cell transcriptomics, we find this population resembles the unsegregated ICM, exhibiting enhanced differentiation potential for endoderm while maintaining epiblast competence. To relate transcription factor binding in these to future fate, we describe a complete enhancer set in both ESCs and naïve extra-embryonic endoderm stem cells and assess SOX2 and GATA6 binding at these elements in the ICM-like ESC sub-population. Both factors support cooperative recognition in these lineages, with GATA6 bound alongside SOX2 on a fraction of pluripotency enhancers and SOX2 alongside GATA6 more extensively on endoderm enhancers, suggesting that cooperative binding between these antagonistic factors both supports self-renewal and prepares progenitor cells for later differentiation.</p>
Meta-analysis of scRNA-seq Co-expression in Human Neural Organoids Reveals High Variability in Recapitulating Primary Tissue
<p>Contains all code and data for Werner and Gillis, Meta-analysis of scRNA-seq Co-expression in Human Neural Organoids Reveals High Variability in Recapitulating Primary Tissue, 2024. </p> <p>Additionally, the code and data for this paper can be found at https://github.com/JonathanMWerner/meta_organoid_analysis with an easy to view github markdown file containing all the code used to generate all figure panel plots at https://github.com/JonathanMWerner/meta_organoid_analysis/blob/main/figure_plots_with_data_code.md.</p> <p>Due to file size limits on github, there are several data files not available on github, but are available here on zenodo in the data_for_plots.zip file, see below:</p> <pre>umap_embeddings_Fig2A.Rdata<br>cross_dataset_aggregated_exp_metaMarker_all_fetal_SuppFig1B_Fig2E.Rdata<br>organoid_egad_results_ranked_6_26_24_Fig3D.Rdata<br>fetal_egad_results_ranked_6_26_24_Fig3D.Rdata<br>org_eigenvec_matrices_SuppFig3CD.Rdata</pre> <p><br>The R package developed for this paper is available at https://github.com/JonathanMWerner/preservedCoexp</p>
Table S6.44,270 pairs of co-expression relationships Between 612 lncRNAs and 2,742 mRNAs
<p>The file includes information about 44270 lncRNA-mRNA pairs related to lncRNA.ensembl,lncRNA.symbol,gene.ensembl,gene.symbol,r,FDR, as well as information about 612 lncRNAs and 2742 mRNAs associated with 44274 co-expressed gene pairs. Where, r denotes differential gene significance coefficient; p-value was corrected by Benjamini-Hochberg algorithm to obtain FDR value.</p>
Supplementary information files: Gene co-expression network and differential expression analyses of subcutaneous white adipose tissue reveal novel insights into the pathological mechanisms underlying ketosis in dairy cows
<p>Supplementary information files: Gene co-expression network and differential expression analyses of subcutaneous white adipose tissue reveal novel insights into the pathological mechanisms underlying ketosis in dairy cows</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.