The prior probability can derive the variety in the interpretation of p-value
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE26498 | GEO | 2011/01/12
SECONDARY ACCESSION(S): PRJNA136723
REPOSITORIES: GEO
Similar Datasets
Project description:Neurog3 and NeuroD1 share the ability to induce endocrine differentiation when expressed ectopically in vivo (Apelqvist et al., 1999; Schwitzgebel et al., 2000; Grapin-Botton et al., 2001) and in vitro (Heremans et al., 2002; Gasa et al., 2004), suggesting that they target a common set of genes. To test this hypothesis, we directly compared in the same cells the effects of Neurog3 and NeuroD1 on global gene expression patterns. Normalized M and A values [limmaGUI output] for the neurogenin3 (Ngn3) and neuroD1 (Nd1) experiments (individual or combined) have been linked below as Supplementary files. Note that M=log2(test/ref), eg, log2(Nd1/Bgal); A=[log2(test*ref)]/2, eg, [log2(Nd1*Bgal)]/2. Additional measures of reliability/probability are also included within the supplementary files. Keywords: comparative genomic hybridization, neurogenin3, neuroD, ductal cells, endocrine, differentiation
2007-08-29 | GSE7884 | GEO
Project description:Neurog3 and NeuroD1 share the ability to induce endocrine differentiation when expressed ectopically in vivo (Apelqvist et al., 1999; Schwitzgebel et al., 2000; Grapin-Botton et al., 2001) and in vitro (Heremans et al., 2002; Gasa et al., 2004), suggesting that they target a common set of genes. To test this hypothesis, we directly compared in the same cells the effects of Neurog3 and NeuroD1 on global gene expression patterns. Normalized M and A values [limmaGUI output] for the neurogenin3 (Ngn3) and neuroD1 (Nd1) experiments (individual or combined) have been linked below as Supplementary files. Note that M=log2(test/ref), eg, log2(Nd1/Bgal); A=[log2(test*ref)]/2, eg, [log2(Nd1*Bgal)]/2. Additional measures of reliability/probability are also included within the supplementary files. Mouse pancreatic ductal cells (mPAC cells) were infected with equal titers of recombinant adenoviruses encoding human Neurogenin3 (AdCMV-NEUROG3) or mouse NeuroD1 (AdCMV-NeuroD1), and 48 hrs later their transcriptomes were compared with that of control cells infected with an adenovirus encoding betagalactosidase (beta-gal; AdCMV-Bgal) using cDNA microarrays. Differences in the activation patterns of the two transcription factors were then determined by comparing the fold-change (FC) values of the pair-wise hybridizations Neurog3/Bgal and NeuroD1/beta-gal.
2007-08-29 | E-GEOD-7884 | biostudies-arrayexpress
Project description:Coronary artery disease (CAD) remains a leading cause of death worldwide. Acute coronary syndromes (ACS) are the spectrum of diseases arising from coronary atherosclerotic plaque rupture, ranging from unstable angina (UA; clinical symptoms of cardiac ischemia without myocardial necrosis) to myocardial infarction (MI; clinical symptoms of cardiac ischemia with myocardial necrosis). We use microrray to identify changes in pathways following MI.This study examines mRNA expression levels in human whole blood at 7 and 30 days post ACS. Patients with MI are compared to those with UA (not healthy controls), thus focusing on differences in mRNA expression due to the acute clinical events rather than underlying atherosclerosis and its treatment. We recruited 26 patients presenting with acute coronary syndromes (ACS); 8 with unstable angina (UA) and 18 with MI. Supplementary files: The files contain the combined values (for each group) of the single patients' expression levels, the fold changes and the significance levels associated. Gene expression levels were estimated using probabilistic models implemented in puma (Propagating Uncertainty in Microarray Analysis, bioconductor.org), which provide estimates for the variance and credibility interval for probe level errors of each transcript. FCs were calculated after combining gene expression values within groups using Bayesian hierarchical model, incorporating probe level errors into the variance estimate. Significance levels for differentially expressed genes were detected by calculating the probability of positive log ratio (PPLR). The higher is the probability the more confident is the estimate of that positive FC, conversely the lower is the probability the more confident is the estimate of that FC to be negative. This model was implemented in the pumaComb and pumaDE modules within puma. file1 = mRNA_MI_combday30_exprs file2 = mRNA_MI_combDay7_exprs
2011-05-06 | E-GEOD-29111 | biostudies-arrayexpress
Project description:This investigation provides a robust multi-dimensional compendium of gene expression data relevant to mouse facial development. It profiles the transcriptome ofectoderm and mesenchyme from the three facial prominences in a time series encompassing their growth and fusion. Analysis of the dataset identified more than 8000 differentially expressed genes comprising dramatically different ectoderm and mesenchyme programs. The mesenchyme programs included many genes identified in earlier analyses as well hundreds of genes not previously implicated in craniofacial development. The ectoderm programs included over a thousand genes that highlight epithelial structure, cell-cell interactions and signaling. The dataset includes 45 .cel files, DABG probability and RMA log2 expression values for each probeset, and statistics for 9457 probesets representing 8575 genes.
2014-10-10 | GSE62214 | GEO
Project description:These tracks display a synthesis of evidence from different assays as part of the four Open Chromatin track sets. This track displays open chromatin regions and/or transcription factor binding sites identified in multiple cell types by one or more complementary methodologies, DNaseI hypersensitivity (HS) (Duke DNaseI HS), Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE) (UNC FAIRE), and chromatin immunoprecipitation (ChIP) for select regulatory factors (UTA TFBS). Each methodology was performed on the same cell type using identical growth conditions. (Note: Data for some or all ChIP experiments may not be available for all cell types). Regions that overlap between methodologies identify regulatory elements that are cross-validated indicating high confidence regions. In addition, multiple lines of evidence suggest that regions detected by a single assay (e.g., DNase-only or FAIRE-only) are also biologically relevant (Song et al., submitted). DNaseI HS data: DNaseI is an enzyme that has long been used to map general chromatin accessibility, and DNaseI "hypersensitivity" is a feature of active cis-regulatory sequences.The use of this method has led to the discovery of functional regulatory elements that include promoters, enhancers, silencers, insulators, locus control regions, and novel elements. DNaseI hypersensitivity signifies chromatin accessibility following binding of trans-acting factors in place of a canonical nucleosome. FAIRE data: FAIRE (Formaldehyde Assisted Isolation of Regulatory Elements) is a method to isolate and identify nucleosome-depleted regions of the genome. FAIRE was initially discovered in yeast and subsequently shown to identify active regulatory elements in human cells (Giresi et al., 2007). Similar to DNaseI HS, FAIRE appears to identify functional regulatory elements that include promoters, enhancers, silencers, insulators, locus control regions and novel elements. ChIP data: ChIP (Chromatin Immunoprecipitation) is a method to identify the specific location of proteins that are directly or indirectly bound to genomic DNA. By identifying the binding location of sequence-specific transcription factors, general transcription machinery components, and chromatin factors, ChIP can help in the functional annotation of the open chromatin regions identified by DNaseI HS mapping and FAIRE. Input data: As a background control experiment, we sequenced the input genomic DNA sample that was used for ChIP. Crosslinked chromatin is sheared and the crosslinks are reversed without carrying out the immunoprecipitation step. This sample is otherwise processed in a manner identical to the ChIP sample as described below. The input track is useful in revealing potential artifacts arising from the sequence alignment process such as copy number differences between the reference genome and the sequenced samples, as well as regions of poor sequence alignability. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf For each site, the maximum F-Seq Density Signal value has been calculated for each assay that was performed in that cell type. F-Seq employs Parzen kernel density estimation to create base pair scores (Boyle et al., 2008b). Significant regions, or peaks, were determined by fitting the data to a gamma distribution to calculate p-values. Contiguous regions where p-values were below a 0.05 (DNaseI HS, ChIP) or 0.1 (FAIRE) threshold were considered significant. See assay specific description pages ( Duke DNaseI HS, UNC FAIRE and UTA TFBS) for more details. A Fisher's Combined P-value for DNaseI HS and FAIRE was calculated using Fisher's combined probability test. First, a test statistic is calculated using the formula x^2 = -2*sum(ln(pi)) where pi are the p-values calculated for DNaseI HS and FAIRE. X2 follows a chi-squared distribution, thus a combined p-value can be assigned to this test statistic.
2012-09-12 | E-GEOD-40833 | biostudies-arrayexpress
Project description:A test study to validate submission ready files of Bakta
| PRJEB44484 | ENA
Project description:Comparison of WT, xrn1 delta and upf1 delta strains were used in a tiling array to yield genomic regions regulated by these proteins The supplementary CHP files record either the signal in log2 space or the p-values in linear space, per TAS output. The CHP files are further divided between UPF1 delta vs. WT and XRN1 delta vs. WT.
2008-08-21 | GSE11621 | GEO
Project description:The majority of peptides presented by MHC class I results from proteasomal protein turnover. The specialized immunoproteasome, which is induced during inflammation, plays a major role in antigenic peptide generation. However, other cellular proteases can, either alone or together with the proteasome, contribute peptides for MHC class I loading non-canonically. We used an im-munopeptidomics workflow combined with a prediction software for proteasomal cleavage probabilities to analyze how inflammatory conditions affect proteasomal processing of immune epitopes presented by A549 cells. Treatment of A549 cells with IFNγ enhanced proteasomal cleavage probability of MHC class I ligands for both, the constitutive proteasome and the im-munoproteasome. Furthermore, IFNγ alters the contribution of the different HLA allotypes to the immunopeptidome. When we calculated HLA allotype-specific proteasomal cleavage probabili-ties for MHC class I ligands, peptides presented by HLA-A*30:01 showed characteristics hinting at a reduced C-terminal proteasomal cleavage probability independently of the type of proteasome. This was confirmed by HLA-A*30:01 ligands from the immune epitope database, which also showed this effect. Furthermore, two additional HLA allotypes, namely HLA-A*03:01 and HLA-A*11:01, presented peptides with a markedly reduced C-terminal proteasomal cleavage probability. Peptides eluted from all three HLA allotypes shared a peptide binding motif with a C-terminal lysine residue suggesting that this lysine residue impairs proteasome-dependent HLA ligand production and might, in turn, favor peptide generation by other cellular proteases. The mass spectrometry raw files used in this study were originally produced for a previous publication, where they were utilized to address the influence of cigarette smoke on the antiviral T-cell immune response. Here, we have reanalyzed the data to investigate processing of MHC class I ligands by c20S or i20S, respectively. Original publication: Chen, J.; Wang, X.; Schmalen, A.; Haines, S.; Wolff, M.; Ma, H.; Zhang, H.; Stoleriu, M.G.; Nowak, J.; Nakayama, M.; et al. Antiviral CD8(+) T cell immune responses are impaired by cigarette smoke and in COPD. Eur Respir J 2023, doi:10.1183/13993003.01374-2022.
2023-10-24 | PXD044235 | Pride
Project description:Definition of genome wide binding profile of Olig2, Ascl1, Tcf3, Max, NFI, Sox2, Sox9 and Sox21 in mouse neural stem cells. Three sets of processed data files, together with a README file (README_E-MTAB-2228.docx) describing the source and content of each file, are provided as additional files to this ArrayExpress submission and can be found in https://www.ebi.ac.uk/arrayexpress/files/E-MTAB-2228 .
2014-09-19 | E-MTAB-2228 | biostudies-arrayexpress
Project description:DNA methylation analysis was perfomed using Infinium 450K Methylation BeadChip platform on 140 PFA ependymoma patient samples. Resulting .idat files were then uploaded to the molecularneuropathology.org classifier to obtain molecular subgroup and copy number variance. Idat files were batch normalized and background corrected using default settings of the R package ChAMP to obtain GpG methylation beta values for analysis of differentially methylated GpG regions.
2023-06-01 | GSE226960 | GEO