Whole Blood Transcriptional Modules generated on Illumina Hu-6 V2 Beadchips
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE29536 | GEO | 2014/03/11
SECONDARY ACCESSION(S): PRJNA141393
REPOSITORIES: GEO
Similar Datasets
Project description:This dataset was used to establish whole blood transcriptional modules (n=260) that represent groups of coordinately expressed transcripts that exhibit altered abundance within individual datasets or across multiple datasets. This modular framework was generated to reduce the dimensionality of whole blood microarray data processed on the Illumina Beadchip platform yielding data-driven transcriptional modules with biologic meaning. This series combines nine independent datasets representing a spectrum of human pathologies expected to result in changes in gene abundance related to changes in expression or cellular composition of whole blood. These nine datasets are composed of 410 individual whole blood profiles generated from patients with HIV, tuberculosis, sepsis, systemic lupus erythematosus, systemic arthritis, B-cell deficiency and liver transplant. For each dataset healthy controls are also included. Each dataset’s expression data was preprocessed independently. First, probes were discarded if they were not present in at least ten percent of the dataset’s samples. Then, the sample data for each dataset was normalized using the BeadStudio average normalization algorithm. Once normalized, the signal was scaled such that all signals less than ten were set to ten. The signal median of all of the dataset’s samples was calculated for each probe. Probes were discarded if no sample had a difference in signal from the median that was greater than or equal to thirty, or if no sample had a fold change relative to the median that was either greater than or equal to 1.5, or less than or equal to 0.67. Finally, data was transformed to the log2 of the signal divided by the mean. Each of the preprocessed datasets was clustered in parallel using Euclidean distance and the Hartigan’s K-Means clustering algorithm, a hybrid of hierarchical and K-Means clustering algorithms. The number of clusters (k) was set to thirty, chosen to provide significant power during later module extraction steps. A higher value could have been chosen for k, but was not in order to minimize possibly arbitrary cluster splitting. Taking the nine sets of thirty clusters as input, we constructed a weighted co-cluster graph, a probe by probe matrix where the value of each cell (the weight) is set to the number of times probe_i and probe_j are found in the same cluster. In this instance, the values range from zero to nine, inclusive. At this point, the goal is to extract sets of probes that are most frequently clustered together, proceeding from the most stringent requirements to the least. To accomplish this, we employ the iterative algorithm. To begin, the maximum clique threshold is initialized to the number of input cluster sets, the paraclique threshold is calculated, and a minimum seed size is chosen (we used ten). The outer loop begins by creating an unweighted graph through application of the maximum clique threshold to the weighted co-cluster graph such that a probe pair, or edge, is represented in the unweighted graph if and only if the corresponding weight in the co-cluster graph equals or exceeds this threshold. We then begin the inner loop. The first step is to isolate the largest set of probes such that all pairs of probes in the set are completely connected in the unweighted graph - that is, there is no pair of probes in the set where the weight from the initial graph is smaller than the maximum clique threshold. In graph theoretic terms, the probes form a maximum clique. If the size of the probe set is smaller than the minimum seed size, we escape from the inner loop, reduce the threshold by one, and return to the beginning of the outer loop. Otherwise, the probe set is at least as large as the minimum seed size and it becomes the seed for a module. To allow for the inevitable clustering inaccuracies, we then employ the paraclique algorithm revisiting the co-cluster graph and adding to the seed any probe that is found to cluster with at least eighty-five percent of the seed’s members a number of times equal or exceeding the paraclique threshold. This final probe set is a module. It is removed from both graphs and named in accordance with the iterations in which it was found (i.e. a module extracted in the first iteration of the outer loop and the second iteration of the inner loop is designated M1.2). The inner loop then begins again with the reduced graphs. Those modules with conserved expression across diseases (formed by transcripts that cluster together for all nine datasets) were selected in early rounds whereas modules with greater disease specificity (formed by transcripts that cluster together only in a subset of the nine datasets) were selected in later rounds.
2014-03-11 | E-GEOD-29536 | biostudies-arrayexpress
Project description:Microglia are multifunctional cells that are important for brain development and homeostasis. Despite the growing evidence of their complexity, the field lacks a unified system for categorizing the many microglial states. In an effort to develop a comprehensive classification system, we profiled transcriptomes of mouse microglia in 96 different stimulus conditions. We generated fine-resolution gene modules that are robustly preserved across datasets by employing a two-step clustering approach. These modules served as the basis for a combinatorial code that we then used to characterize microglial activation under inflammatory stimulus conditions in vitro. Many of the modules were preserved in vivo, and were differentially regulated during acute inflammation and in aging. In summary, the microglial gene modules presented here are a novel resource for classifying and characterizing microglial states in health and disease.
2018-12-15 | GSE109329 | GEO
Project description:Abnormally accumulated tau protein aggregates are one of the hallmarks of dementia, including Alzheimer’s disease (AD), and are believed to play a critical role in neurodegeneration. In order to investigate proteomic alteration driven by tau aggregates, we implemented quantitative proteomics to analyze disease model mice expressing human MAPTP301S transgene (hTau-Tg) and quantified more than 9,000 proteins in total. We applied the weighted gene co-expression analysis (WGCNA) algorithm to the datasets and explored protein co-expression modules that were associated with the accumulation of tau aggregates and were preserved in proteomes of AD brains. This led us to identify four modules with functions related to neuroinflammatory responses, mitochondrial energy production processes (including the tricarboxylic acid cycle and oxidative phosphorylation), cholesterol biosynthesis, and postsynaptic density. Furthermore, a phosphoproteomics study uncovered phosphorylation sites that were highly correlated with these modules. Our datasets represent resources for understanding the molecular basis of tau-induced neurodegeneration, including AD.
2022-07-05 | PXD031129 | JPOST Repository
Project description:We performed a systematic computational network-based analysis of large-scale F. verticillioides RNA-seq datasets to identify gene subnetwork modules associated with virulence and fumonisin regulation.
2020-03-03 | GSE146208 | GEO
Project description:Using RNA-Seq datasets, we identified subnetwork modules (group of genes) and key functional (hub) genes associated with the fungal pathogenicity by developing a computational pipeline with network-based comparative analusis approach
2017-12-21 | GSE108365 | GEO
Project description:In this study we investigated the developmental dynamics of genes targeted in vivo by the transcription factor RAMOSA1, a key regulator of determinacy, and revealed potential mechanisms for repressing branches in distinct stem cell populations in developing maize inflorescences. To identify targets of RA1 and to distinguish direct vs. indirect interactions, we performed Chromatin Immunoprecipitation (ChIP)-seq and compared the results to gene expression data (RNA-seq datasets for Eveland et al., 2013, submitted). We mapped genome-wide occupancy of RA1 and showed that it differently regulates modules of target genes based on spatiotemporal context.
2013-11-27 | GSE51048 | GEO
Project description:In this study we investigated the developmental dynamics of genes targeted in vivo by the transcription factor RAMOSA1, a key regulator of determinacy, and revealed potential mechanisms for repressing branches in distinct stem cell populations in developing maize inflorescences. To identify targets of RA1 and to distinguish direct vs. indirect interactions, we performed Chromatin Immunoprecipitation (ChIP)-seq and compared the results to gene expression data (RNA-seq datasets for Eveland et al., 2013, submitted). We mapped genome-wide occupancy of RA1 and showed that it differently regulates modules of target genes based on spatiotemporal context. Plants expressing complementing RA1 transgenes tagged with HA or YFP were used in parallel experiments. Ear and tassel primordia were collected and tag-specific antibodies were used to pull down RA1 bound to its target loci. Genome-wide analysis of RA1 occupancy revealed thousands of putative binding sites (i.e. peaks significantly enriched (p < 1e-05) compared to input DNA).
2013-11-27 | E-GEOD-51048 | biostudies-arrayexpress
Project description:Epigenetic modules governing skin epidermal homeostasis
| PRJNA808412 | ENA
Project description:Transcriptome sequencing elucidates gene modules in microglia
| PRJNA430446 | ENA
Project description:Epigenetic modules governing skin epidermal homeostasis [III]
| PRJNA851380 | ENA