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16 results for “Correlation Clustering”
An efficient not-only-linear correlation coefficient based on clustering: Supplementary Files
<p>Supplementary Files for the manuscript "An efficient not-only-linear correlation coefficient based on machine learning" available at https://doi.org/10.1101/2022.06.15.496326</p> <ul> <li>Supplementary File 1: All pairwise gene correlations using Pearson, Spearman and CCC among the top 5,000 genes in GTEx’s whole blood with the largest variance. Columns indicates whether the gene pair was categorized in the top or bottom 30% of each coefficient, the correlation value, and the significance of the association. Significance is only present for the top 10 gene pairs of each intersection in the “Disagreements” group (Figure 3a, right) where CCC disagrees with Pearson, Spearman or both.</li> <li>Supplementary File 2: Percentiles of the coefficient values for the top 5,000 genes in GTEx’s whole blood.</li> <li>Supplementary File 3: Pearson, Spearman and CCC correlations values and their significance for two gene pairs (<em>UTY</em> - <em>KDM6A</em> and <em>DDX3Y</em> - <em>KDM6A</em>) across all tissues in GTEx.</li> </ul>
Two-time correlation function based on speckle patterns from x-ray photon correlation spectroscopy associated with "Intermittent cluster dynamics and temporal fractional diffusion in a bulk metallic glass" (scientific article published in Nature Communications, 2024)
<p>This dataset consists of contrast data, i.e., the two-time correlation function, based on speckle patterns measured at the at the 8ID-E beamline of the Advanced Photon Source at Argonne National Laboratory.</p> <p>Experimental details are stated in the paper specified under "related work" and in the accompanying supplementary information.</p> <p>You are welcome to use this dataset in compliance with the CC BY 4.0 licence assigned to this dataset.</p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data consists of 32 text files in total, which correspond to the main and lower panel Figure 2 of the main publication. </p> <p>30 of these text files are contrast data, which are named "contrast_DT250s_nn.text" wiith "nn" as the identifier of consecutive data sets going from 1 to 30. Each data set consists of p rows and q columns, DT250s denotes the time resolution of data points, which is 250 s along both row and column values.</p> <p>The data set called "Time_Contrast_1to30s.txt" states the start time in seconds of the first data point of each of the thirty contrast data set.</p> <p>The data set called "ScatteredIntensity.txt" states the scattered intensity at full time resolution, i.e. 2.5 s.</p> <p>The files are plain text files with the data points separated by "space" along rows and "new line" along columns.</p>
Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants
<p>original daily data for 'Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants'</p>
Space of Optimal Solutions of the Correlation Clustering Problem for Complete Signed Graphs
<p><strong>Description. </strong>This is the data used in the experiments of the following paper:</p> <ul> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs,” <em>Journal of Complex Networks </em>8(6):cnaa025, 2020. DOI: <a href="http://doi.org/10.1093/comnet/cnaa025">10.1093/comnet/cnaa025</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-02994011">hal-02994011</a>⟩</li> </ul> <p>This dataset contains:</p> <ul> <li>Plot files used in the article;</li> <li>Input signed networks;</li> <li>All optimal solutions (i.e. optimal solution space) of the corresponding networks;</li> <li>Evaluation files.</li> </ul> <p><strong>Source code. </strong>The code source is accessible on GitHub: <a href="https://github.com/CompNet/Sosocc">https://github.com/CompNet/Sosocc</a></p> <p><strong>Citation. </strong>If you use the data or source code, please cite the above article.</p> <p><br><code>@Article{Arinik2020,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs},</code><br><code> journal = {Journal of Complex Networks},</code><br><code> year = {2020},</code><br><code> volume = {8},</code><br><code> number = {6},</code><br><code> pages = {cnaa025},</code><br><code> doi = {10.1093/comnet/cnaa025},</code><br><code>}</code><br><br></p> <p>--------------------------------------------</p> <p><strong>Details.</strong></p> <p><br><strong># PLOT FILES</strong><br>* `<em>Figure1.zip</em>`: Figures showing that there might be many distinct optimal solutions of a small-sized network.<br>* `<em>Figure2.zip</em>`: Figures showing that distinct optimal solutions of a given network might be partition-wise very similar or different.<br>* `<em>Figure4: All Results.zip</em>`: Figure 4 in the article contains only a few plots regarding the results for space considerations. This zip file contains all plots, and it is organized by the values of `<em>l<sub>0</sub></em>`. In each `<em>l<sub>0</sub></em>` folder, the results are shown in three different perspectives:<br>--- Detected Imbalance Percentage vs Graph Order (i.e. number of vertices)<br>--- Prop mispl vs Graph order<br>--- Graph order vs Prop mispl<br>* `<em>workflow.pdf</em>`: The workflow of the methodology used in the article.<br>* `<em>Syrian network With All Solutions.pdf</em>`: Syrian network (on top) with core part information through node colors, and its optimal solutions in which node colors represent partition information (on bottom).<br> </p> <p><strong>#NETWORKS</strong><br>All networks are in `<em>Input Signed Networks.tar.gz</em>`.<br>Networks are generated through a simple random model (available in <em>https://github.com/CompNet/SignedBenchmark</em>) designed to produce complete (or uncomplete) unweighted networks with built-in modular structure.<br>There are 3 parameters used for the generation:</p> <ol> <li>number of nodes (`<em>n</em>`)</li> <li>initial number of modules (`<em>l<sub>0</sub></em>`)</li> <li>proportion of misplaced links, i.e. proportion of frustrated links, (`<em>q<sub>m</sub></em>`)</li> </ol> <p>Inside `<em>Input Signed Networks.tar.gz</em>`:<br>NETWORKS<br>|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000<br>....|__propMispl=PROP_MISPL<br>........|__propNeg=PROP_NEG<br>............|__network=NETWORK_NO<br><br>- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000<br>The number of nodes, the initial number of modules and the network density are given. The network density is always 1, since we treat only complete signed networks.<br>- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL<br>Proportion of misplaced links is given.<br>- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG<br>Proportion of negative links (`<em>q<sub>n</sub></em>`) is specified. `<em>q<sub>n</sub></em>` changes depending on `<em>n</em>` and `<em>l<sub>0</sub></em>`. Since only complete signed networks are studied, this parameter is automatically computed from the other input parameters.<br>- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO<br>Network numbers are shown.<br>In the end, thre are three file formats describing the same network content: GraphML (.graphml), Pajek NET (.net) or .G format.<br><br><strong># PARTITIONS</strong><br>All partition results are in `<em>Partition Results.tar.gz</em>`. Note that all optimal partitions of a signed network are obtained through an exact partitioning method. The code source is accessible here: <em>https://github.com/arinik9/ExCC</em><br>Inside `<em>Partition Results.tar.gz</em>`:<br><br>PARTITIONS<br>|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000<br>....|__propMispl=PROP_MISPL<br>........|__propNeg=PROP_NEG<br>............|__network=NETWORK_NO<br>................|__"<em>ExCC-all</em>"<br>....................|__"<em>signed-unweighted</em>"<br><br>- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000<br>- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL<br>- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG<br>- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO<br>- The fifth hierarchy => the folders are named as follows: "<em>ExCC-all</em>"<br>The name of the partitioning method are shown. Since an exact partitioning method is used to obtain all distinct optimal solutions, it is named as "<em>ExCC-all</em>".<br>- The sixth hierarchy => the folders are named as follows: "<em>signed-unweighted</em>"<br>The type of signed networks are shown: signed and unweighted</p> <p>In the end, the partition results are located, and the file names are named as follows: <em>membership.txt</em>. Note that the first partition result number starts from zero.</p> <p> </p> <p><strong># EVALUATIONS</strong><br>Evaluation results related to our plots are in `<em>Evaluation Results.tar.gz</em>. Note that the hierarchy of this folder is the same as that of 'Partitions'. Inside `<em>Evaluation</em><em> Results.tar.gz</em>`:</p> <p>- `Best-k-for-kmedoids.csv`: It contains three columns. 1) the number of solution classes via kmedoids, 2) the best Silhouette score, 3) the best clustering in terms of Silhouette score, which represents solution classes.</p> <p>- `class-core-part-size-tresh=1.00.csv`. It indicates the proportion of core part size for each solution class.</p> <p>- `exec-time.csv`: It indicates the execution time in seconds.</p> <p>- `imbalance.csv`: It contains the information of imbalance as 1) count and 2) percentage</p> <p>- `nb-solution.csv`: It indicates the total number of solutions<br>--------------------------------------------</p> <p>Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</p>
Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants
<p>Data for "Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants"</p>
Efficient Enumeration of the Optimal Solutions to the Correlation Clustering problem
<div><strong>Description. </strong>This is the data used in the experiments presented in the following paper:<br> <div> <ul> <li> <div> <div>N. Arınık, R. Figueiredo, and V. Labatut, “Efficient Enumeration of the Optimal Solutions to the Correlation Clustering problem,” <em>Journal of Global Optimization</em>, vol. 86, pp. 355–391, 2023. DOI: <a href="http://doi.org/10.1007/s10898-023-01270-3">10.1007/s10898-023-01270-3</a> ⟨<a href="https://hal.science/hal-03935831">hal-03935831</a>⟩</div> </div> </li> </ul> <p><strong>Source code. </strong>The related source code is available on GitHub: </p> <ul> <li><a href="https://figshare.com/articles/dataset/Efficient_Enumeration_of_Correlation_Clustering_Optimal_Solution_Space/%3Ci%3Ehttps://github.com/%3C/i%3E%3Ci%3E%3Ci%3ECompNet%3C/i%3E/Sosocc%3C/i%3E">https://github.com/CompNet/Sosocc</a></li> <li><a href="https://figshare.com/articles/dataset/Efficient_Enumeration_of_Correlation_Clustering_Optimal_Solution_Space/%3Ci%3Ehttps://github.com/CompNet/EnumCC%3C/i%3E">https://github.com/CompNet/EnumCC</a></li> </ul> <div><strong>Citation. </strong>If you use these data, please cite the above reference:</div> <div><br><code>@Article{Arinik2021,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Efficient Enumeration of the Optimal Solutions to the Correlation Clustering problem},</code><br><code> journal = {Journal of Global Optimization},</code><br><code> year = {2023},</code><br><code> volume = {86},</code><br><code> pages = {355-391},</code><br><code> doi = {10.1007/s10898-023-01270-3},</code><br><code>}</code></div> <div> </div> <div><strong>Funding. </strong>This research benefited from the support of Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</div> <div> </div> <div><strong>Further details. </strong>We describe below the structure of the zip file `article_materials.zip`:</div> <div> <ul> <li>Experiments for Dataset 1 <ul> <li>delay_exec_time: all the results and plots regarding the difference of execution times between EnumCC(3) and OneTreeCC() (i.e., EnumCC(3) minus OneTreeCC()), represented on the log-scaled y-axis of the plots. When such difference takes a negative value, this means our proposed method EnumCC(3) runs faster than OneTreeCC().</li> <li>EnumCC_nb-jumps: all the results regarding the number of jumps related to EnumCC(3), i.e. n<sub><em>jump</em></sub>(EnumCC(3))</li> <li>exec_time: all the results regarding the execution times of EnumCC(3) and OneTreeCC().</li> <li>nb-sols: all the results regarding the number of optimals solutions based on EnumCC(3). Note that we show the results of OneTreeCC() only for those with n=50, since both methods run out of the time limit of 12h for several networks with n=50.</li> </ul> </li> <ul> <li>networks: We generate these complete and incomplete networks through our random signed network generator, which is publicly available online. For complete unweighted signed networks, this model relies on only three parameters: n (number of vertices), l<sub>0</sub> (initial number of modules) and <em>q<sub>m</sub></em> (proportion of misplaced edges, i.e. edges meant to be frustrated by construction). Moreover, we make the assumption that the proportion of misplaced edges is the same inside and between the modules. When it comes to incomplete unweighted signed networks, we introduce two more parameters, which are the density d of the graph and the proportion q neg of the negative edges. The last parameter q<sub>neg</sub> allows to control the ratio of positive to negative edges. For complete unweighted signed networks with d = 1, we generate 20 replications for parameter values l<sub>0</sub> = 3, n <em>∈</em> {32, 36, 40, 45, 50} and <em>q<sub>m</sub></em> <em>∈</em> {0.1, 0.2, 0.3, 0.4, 0.5, 0.6}. In these networks, the value of <em>q<sub>neg</sub> </em>with the considered parameters is approximately equal to 0.7. For incomplete unweighted signed networks with d <em>∈</em> {0.25, 0.50}, we generate 20 replications for parameter values l<sub>0</sub> = 3, n <em>∈</em> {32, 36, 40}, q<sub>m</sub> <em>∈</em> {0.1, 0.2, 0.3, 0.4, 0.5, 0.6} and <em>q<sub>neg</sub> </em><em>∈</em> {0.3, 0.5, 0.7}. In total, we produce 600 and 1,080 instances for complete and incomplete networks, respectively, which makes a total of 1,680 instances.</li> <li>partitions: folder containing the partitioning results of two methods: EnumCC(3) vs. OneTreeCC(). Note that the results of OneTreeCC() are not shown for space considerations, except for those with n=50.</li> <li>Results</li> </ul> <li>Experiments for Dataset 2</li> <ul> <li>benchmark netwoks: We generate these complete and incomplete networks through our random signed network generator, which is publicly available online. The optimal solution for a generated network is known by construction. For a given n, d and l<sub>0</sub>, we first create a perfectly structurally balanced (i.e., internally positive and externally negative) signed network with a built-in module structure. The underlying module structure constitutes the optimal partition. Then, in order to take into account different positive to negative ratio values for internal and external edges we generate several signed networks by perturbing the initial signed network without affecting its underlying optimal partition, thanks to its definition of stability range. We generate signed networks with parameter values n <em>∈</em> {30, 40, 50, 60, 70, 90}, d <em>∈</em> {0.25, 1.00} and l<sub>0</sub> <em>∈</em> {2, 4, 6}. In total, we produce 214 and 184 instances for complete and incomplete networks, respectively, which makes a total of 398 instances.</li> <li>benchmark partitions: folder containing the partitioning results of two methods: CoNS(<em>r<sub>max</sub></em>) with vs. without MVMO pruning, where <em>r<sub>max</sub></em> ∈ {3,4}.</li> <li>results: two `csv` files containing benchmark results between CoNS(<em>r<sub>max</sub></em>) with vs. without MVMO pruning, where r<sub>max</sub> ∈ {3,4}.</li> </ul> </ul> </div> </div> </div>
Data for: Evolution of static and dynamical density correlations of one-dimensional soft-core bosons from the Tonks-Girardeau limit to a clustering fluid.
<p>Data and scripts to reproduce the figures of the paper "Evolution of static and dynamical density correlations of one-dimensional soft-core bosons from the Tonks-Girardeau limit to a clustering fluid."</p>
Theoretical analysis of correlations between two quantum fields exciting a three-level system using the cluster-expansion approach
<p>Dataset of the publication "Theoretical analysis of correlations between two quantum fields exciting a three-level system using the cluster-expansion approach" H. Rose, O. V. Tikhonova, T. Meier, and P. R. Sharapova, Proc. SPIE 11999, Ultrafast Phenomena and Nanophotonics XXVI, 1199905 (2022). ( https://doi.org/10.1117/12.2608528 ). The zip file includes the data on which the plots shown in figures 1, 2, and 3 are based.</p>
SPRS Total Correlation clustering
<p>Total Correlation calculated using the Total Correlation Explanation algorithm<a href="https://www.zotero.org/google-docs/?FBcmlE"> </a>(CorEx), which is an optimization technique that identifies latent variables to explain the dependence in the data.</p>
Clustering of the structures by using “snakes-&-dragons” approach, or correlation matrix as a signal
Open the record for dataset details and reuse information.
Correlated DNA methylation clusters in whole blood
GEO Series GSE54882. Homo sapiens. 305 samples. Type: Methylation profiling by genome tiling array.
Geometries for "Reduced Scaling Correlated Natural Transition Orbitals for Multilevel Coupled Cluster Calculations"
<p>Formaldehyde, formaldehyde-in-water, and azobenzene dye geometries used in "Reduced Scaling Correlated Natural Transition Orbitals for Multilevel Coupled Cluster Calculations" in xyz-format.</p>
Study of seroPREvalence Vis-à-vis SARS-CoV2 and Correlation With Clinical Forms of COVID-19 in Patients Followed in Pneumology in the Cluster Area of the Grand-Est Region (Strasbourg University Hospit
ClinicalTrials.gov study NCT04528901. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Expression Data from the Riesling grape must fermentation by industrial M2 and M2 nsf1∆ S. cerevisiae strains for functional characterization of the NSF1 (YPL230W) gene via Correlation Clustering-ba
GEO Series GSE34117. Saccharomyces cerevisiae; Schizosaccharomyces pombe. 14 samples. Type: Expression profiling by array.
Gene expression clusters change and molecular correlation via STING pathway between orbital MALT lymphoma and orbital IgG4-related eye diseases
GEO Series GSE276421. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Dataset related to article "Independent expression of circulating and tissue levels of PD-L1: correlation of clusters with tumor metabolism and outcome in patients with non-small cell lung cancer."
<p>PURPOSE:</p> <p>To evaluate the clinical-pathological and prognostic significance of the circulating PD-L1 level in patients with surgically treated NSCLC, by combining data for PD-L1 expression with other immune-related markers and tumor metabolism.</p> <p>METHODS:</p> <p>Overall, 40 patients with resected NSCLC (stage Ia-IIIa) who had preoperative blood storage and underwent staging PET/CT were enrolled for the study. In all cases, we determined plasma levels of PD-L1 (pg/ml), immune-reactive areas (IRA %) covered by CD3, CD68, CD20, CD8, PD-1, and PD-L1 in the tumor specimen, and metabolic parameters on PET, i.e., SUV<sub>max</sub>, SUV<sub>peak</sub>, metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Variables were statistically analyzed to establish their association with disease-free survival (DFS).</p> <p>RESULTS:</p> <p>The circulating levels of PD-L1 in the bloodstream could be determined in 38/40 (95%) samples. The mean and median expression levels were 34.86 pg/ml and 24.83 pg/ml, respectively. We did not find any statistically significant correlation between circulating PD-L1 and tissue expression of PD-L1/PD-1. Some mild degree of positive correlation was determined between tissue PD-L1 and SUV<sub>max</sub> (ρ =&thinsp;0.390; p =&thinsp;0.0148). Hierarchical clustering combining circulating, tissue, and metabolic parameters identified clusters with high metabolic tumor burden or high expression of plasma PD-L1 levels (Z score ≥ 2) as having a poor DFS (p =&thinsp;0.033). The multivariate analysis detected stage and metabolism (i.e., SUV<sub>max</sub> and SUV<sub>peak</sub>) as independent prognostic factors for DFS.</p> <p>CONCLUSION:</p> <p>Plasma levels of PD-L1 are independent of the expression of PD-1/PD-L1 in NSCLC tumor tissue and, when combined with other clinical-pathological parameters, allow for the identification of clusters with different outcomes.</p>
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