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45 results for “matrix factorization”
Metabolic pathway prediction using non-negative matrix factorization with improved precision
<p>We include samples of various data types used in the work "Metabolic pathway prediction using non-negative matrix factorization with improved precision"</p> <p>More information about the software package and instructions are provided in <a href="https://github.com/hallamlab/triUMPF">hallamlab/triUMPF</a></p>
easyMF: A Web Platform for Matrix Factorization-based Biological Discovery from Large-scale Transcriptome Data
<p>With the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we present easyMF, a user-friendly web platform that aims to facilitate biological discovery from large-scale transcriptome data through matrix factorization (MF). The easyMF platform enables users with little bioinformatics experience to streamline transcriptome analysis from raw reads to gene expression and to decompose expression matrix from thousands of genes to a handful of metagenes. easyMF also offers a series of functional modules for metagene-based exploratory analysis with an emphasis on functional gene discovery. As a modular, containerized and open-source platform, easyMF can be customized to satisfy users’ specific demands and deployed as a web server for broad applications. easyMF is freely available at https://github.com/cma2015/easyMF. We demonstrated the application of easyMF with four case studies using 940 RNA sequencing datasets from maize (<em>Zea mays </em>L<em>.</em>).</p>
Species matrix of Indonesian litter and soil Collembola with environmental factors
<p>Collembola are among the most abundant and diverse microarthropods that affect litter decomposition, control microbial functioning and support invertebrate predators. Rainforest conversion and expansion of plantations in tropical regions are associated with changes in microclimate and biodiversity decline, but information on the impact of tropical land use on Collembola communities and their seasonal fluctuations is very limited. Here, we investigated seasonal fluctuations in density and community composition of Collembola in rainforest, and in rubber and oil palm plantations in Jambi province (Sumatra, Indonesia), a region with moderate seasonality (wet and dry seasons) that experienced one of the strongest deforestations globally during the last decades. Samples were taken at the end of the wet season (March), the beginning of the dry season (June), the end of the dry season (August) and the beginning of the wet season (November). Collembola density in the litter was at a maximum at the beginning of the wet season, whereas in soil it generally varied little. Densities of euedaphic (soil-adapted) and epedaphic species (upper litter-adapted) fluctuated less with season than hemiedaphic (lower litter-adapted) and atmobiotic species (aboveground-adapted). Collembola community composition changed with season in each of the land-use systems, with the differences being more pronounced in litter than in soil. Differences in community composition among all three land-use systems were most pronounced at the beginning of the dry season. Water content, pH, fungal and bacterial biomarkers, C/N ratio and root biomass were identified as factors related to seasonal variations in species composition of Collembola in different systems. Our results show that Collembola density and community composition often fluctuate in a similar way across land-use systems, indicating that they are driven by common environmental factors, with water content, pH and food availability being of prominent importance. Notably, differences in Collembola community composition between rainforest and plantation systems were most pronounced in the dry season (June, August), indicating that the conversion of rainforest into plantation systems aggravates detrimental effects of low moisture on soil animal communities. Fluctuations in Collembola communities were generally stronger in litter than in soil, reflecting that Collembola in litter are less buffered against climatic variations than those in soil. Overall, the results document the sensitivity of tropical soil animal communities to seasonal climatic variations, which <span>intensifies</span> the effects of the conversion of rainforest into plantation systems on soil biodiversity.</p>
Multiply improved positive matrix factorization for source apportionment of volatile organic compounds during the COVID-19 shutdown in Tianjin, China
<p>This dataset contains the input and output data of the multiple PMF for volatile organic compounds (VOCs) source apportionment in the suburbs of Tianjin, China, during the outbreak of COVID-19 period (November 2019 to March 2020).</p>
A temporary cholesterol-rich diet and bacterial extracellular matrix factors favor Salmonella spp. biofilm formation in the cecum
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Species matrix of Indonesian litter and soil Collembola with environmental factors
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Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
<p>This work presents an advanced source apportionment approach that estimates the uncertainties of the positive matrix factorization model. The selection of the environmentally meaningful solutions is challenging in such models and a systematic approach to identify and sort the factors is important. We applied this method to a novel data set covering one yearly cycle at three sites in Estonia. The main results showed that biomass burning dominates in winter, while biogenic sources prevail in summer.</p>
MOTL: enhancing multi-omics matrix factorization with transfer learning
<p>The <strong>Lrn_5000D_Fctrzn_100k_001TH.zip</strong> file contains the results of a MOFA factorization of the TGCA learning dataset, to be downloaded and used for transfer learning factorization of a target dataset with MOTL. The MOFA output is in the <strong>Model.hdf5</strong> file, and intercepts for the factorization are in the <strong>EstimatedIntercepts.rds</strong> file. The <strong>FctrMeta.json </strong>file contains metadata related to the MOFA factorization. The nohup.out file is the log of the factorization.</p> <p>The <strong>expdat_meta.rds</strong> file contains metadata from the preprocessing of the TCGA multi-omics learning dataset that was factorized. This should also to be downloaded as it is an input to MOTL</p>
Estimation of the road traffic sound levels based on Non-Negative Matrix Factorization dataset
<p>Sound database to compute the NonnegMatrixFact experience (download the .m files here: https://github.com/jean-remyGloaguen/article2017EstimationAmbiance) dedicated to the estimation of the traffic sound level on urban sound mixtures.</p> <p>This sound database includes two subfolders: <em>ambiance</em> and <em>dictionary</em></p> <p><em>- ambiance </em>folder includes 6 sub-corpus (<em>alert</em>, <em>animals, climate, human, mechanics</em> and <em>transportation</em>). In each sub-corpus, 50 sound mixtures are available and divided into two files: one dedicated to the traffic signal, the other to the interfering signal.</p> <p><em>- dictionary </em>folder includes the isolated sounds dedicated to the dictionary learning for NMF</p>
Recombinant Human Platelet-derived Growth Factor in Combination With Collagen Matrix
ClinicalTrials.gov study NCT04462237. IPD Sharing: NO. Countries: 1. Publications: 2.
Molecular subtyping of alzheimer’s disease with consensus non-negative matrix factorization
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Data from: Using matrix and tensor factorizations for the single-trial analysis of population spike trains
Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding.
Supplementary data to BANMF-S: a blockwise accelerated non-negative matrix factorization framework with structural network constraints for single cell RNA-seq data imputation
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Results of matrix factorization decompositions for a set of 14 colon cancer transcriptomic datasets using different methods
<p>These are the results of application of several matrix factorization methods (ICA, StabilizedICA, NMF, PCA) for a set of 14 independent colon cancer transcriptomics datasets. The analysis was done to compare the methods in terms of their reproducibility (ability to generalize). </p> <p>The results of this analysis was published in </p> <p><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6821374/pdf/btz225.pdf">Cantini L, Kairov U, de Reyniès A, Barillot E, Radvanyi F, Zinovyev A. Assessing reproducibility of matrix factorization methods in independent transcriptomes. Bioinformatics. 2019 Nov 1;35(21):4307-4313. doi: 10.1093/bioinformatics/btz225. </a></p> <p> </p>
Data from: Using matrix and tensor factorizations for the single-trial analysis of population spike trains
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Interferon Regulatory Factor 5 represses mitochondrial matrix protein GHITM to limit macrophage oxidative capacity in early response to obesity [bmdm]
GEO Series GSE208667. Mus musculus. 42 samples. Type: Expression profiling by high throughput sequencing.
Integrative analysis of single cell genomics data by coupled nonnegative matrix factorizations (RNA-Seq)
GEO Series GSE115968. Mus musculus. 464 samples. Type: Expression profiling by high throughput sequencing.
The bone transcription factor Osterix controls extracellular matrix and node of Ranvier related gene expression in oligodendrocytes [RNA-seq]
GEO Series GSE221192. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Macrophage-Mediated Bone Formation of Scaffolds Modified with MSC-derived Extracellular Matrix Depends on Migration Inhibitory Factor Signaling Pathway
GEO Series GSE174477. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Transforming Growth Factor-β (TGFβ) Prevents Hypertension-Induced Aortic Dissection in Adult Mice via Extracellular Matrix Production (Single cells KO vs. WT SMC)
GEO Series GSE194083. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
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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.