Statistical analysis of differential gene expression relative to a fold change threshold on NanoString data of mouse odorant receptor genes
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE53876 | GEO | 2014/01/08
SECONDARY ACCESSION(S): PRJNA233618
REPOSITORIES: GEO
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Project description:We developed a systematic statistical method, tTREAT, to identify differentially expressed genes with respect to a predefined FC threshold. The tTREAT approach aims to reduce false discoveries. We applied statistical tests relative to a Fold Change threshold to a dataset about mouse odorant receptor gene expression generated by the NanoString technology. We used mouse strains M-bM-^HM-^FP and M-bM-^HM-^FH that lack the P element and the H element respectively. We also used a mouse strain generated by chromosome engineering (M-bM-^HM-^FOlfr7M-bM-^HM-^F) whereby a 2.4 Mb region is deleted, thus lacking 99 odorant receptor genes. We sought to understand the regulation of the expression of odorant receptor genes by comparing mutant mice to wildtype mice.
2014-01-08 | E-GEOD-53876 | biostudies-arrayexpress
Project description:BackgroundA challenge in gene expression studies is the reliable identification of differentially expressed genes. In many high-throughput studies, genes are accepted as differentially expressed only if they satisfy simultaneously a p value criterion and a fold change criterion. A statistical method, TREAT, has been developed for microarray data to assess formally if fold changes are significantly higher than a predefined threshold. We have recently applied the NanoString digital platform to study expression of mouse odorant receptor genes, which form with 1,200 members the largest gene family in the mouse genome. Our objectives are, on these data, to decrease false discoveries when formally assessing the genes relative to a fold change threshold, and to provide a guided selection in the choice of this threshold.ResultsStatistical tests have been developed for microarray data to identify genes that are differentially expressed relative to a fold change threshold. Here we report that another approach, which we refer to as tTREAT, is more appropriate for our NanoString data, where false discoveries lead to costly and time-consuming follow-up experiments. Methods that we refer to as tTREAT2 and the running fold change model improve the performance of the statistical tests by protecting or selecting the fold change threshold more objectively. We show the benefits on simulated and real data.ConclusionsGene-wise statistical analyses of gene expression data, for which the significance relative to a fold change threshold is important, give reproducible and reliable results on NanoString data of mouse odorant receptor genes. Because it can be difficult to set in advance a fold change threshold that is meaningful for the available data, we developed methods that enable a better choice (thus reducing false discoveries and/or missed genes) or avoid this choice altogether. This set of tools may be useful for the analysis of other types of gene expression data.
| S-EPMC4016238 | biostudies-literature
Project description:UnlabelledBackgroundBecause of the large volume of data and the intrinsic variation of data intensity observed in microarray experiments, different statistical methods have been used to systematically extract biological information and to quantify the associated uncertainty. The simplest method to identify differentially expressed genes is to evaluate the ratio of average intensities in two different conditions and consider all genes that differ by more than an arbitrary cut-off value to be differentially expressed. This filtering approach is not a statistical test and there is no associated value that can indicate the level of confidence in the designation of genes as differentially expressed or not differentially expressed. At the same time the fold change by itself provide valuable information and it is important to find unambiguous ways of using this information in expression data treatment.ResultsA new method of finding differentially expressed genes, called distributional fold change (DFC) test is introduced. The method is based on an analysis of the intensity distribution of all microarray probe sets mapped to a three dimensional feature space composed of average expression level, average difference of gene expression and total variance. The proposed method allows one to rank each feature based on the signal-to-noise ratio and to ascertain for each feature the confidence level and power for being differentially expressed. The performance of the new method was evaluated using the total and partial area under receiver operating curves and tested on 11 data sets from Gene Omnibus Database with independently verified differentially expressed genes and compared with the t-test and shrinkage t-test. Overall the DFC test performed the best - on average it had higher sensitivity and partial AUC and its elevation was most prominent in the low range of differentially expressed features, typical for formalin-fixed paraffin-embedded sample sets.ConclusionsThe distributional fold change test is an effective method for finding and ranking differentially expressed probesets on microarrays. The application of this test is advantageous to data sets using formalin-fixed paraffin-embedded samples or other systems where degradation effects diminish the applicability of correlation adjusted methods to the whole feature set.
| S-EPMC3526407 | biostudies-literature
Project description:The problem of identifying differential activity such as in gene expression is a major defeat in biostatistics and bioinformatics. Equally important, however much less frequently studied, is the question of similar activity from one biological condition to another. The fold-change, or ratio, is usually considered a relevant criterion for stating difference and similarity between measurements. Importantly, no statistical method for concomitant evaluation of similarity and distinctness currently exists for biological applications. Modern microarray, digital PCR (dPCR), and Next-Generation Sequencing (NGS) technologies frequently provide a means of coefficient of variation estimation for individual measurements. Using fold-change, and by making the assumption that measurements are normally distributed with known variances, we designed a novel statistical test that allows us to detect concomitantly, thus using the same formalism, differentially and similarly expressed genes (http://cds.ihes.fr). Given two sets of gene measurements in different biological conditions, the probabilities of making type I and type II errors in stating that a gene is differentially or similarly expressed from one condition to the other can be calculated. Furthermore, a confidence interval for the fold-change can be delineated. Finally, we demonstrate that the assumption of normality can be relaxed to consider arbitrary distributions numerically. The Concomitant evaluation of Distinctness and Similarity (CDS) statistical test correctly estimates similarities and differences between measurements of gene expression. The implementation, being time and memory efficient, allows the use of the CDS test in high-throughput data analysis such as microarray, dPCR, and NGS experiments. Importantly, the CDS test can be applied to the comparison of single measurements (N=1) provided the variance (or coefficient of variation) of the signals is known, making CDS a valuable tool also in biomedical analysis where typically a single measurement per subject is available.
| S-EPMC5054499 | biostudies-literature
Project description:The advanced medium-throughput NanoString nCounter technology has been increasingly used for mRNA or miRNA differential expression (DE) studies due to its advantages including direct measurement of molecule expression levels without amplification, digital readout and superior applicability to formalin fixed paraffin embedded samples. However, the analysis of nCounter data is hampered because most methods developed are based on t-tests, which do not fit the count data generated by the NanoString nCounter system. Furthermore, data normalization procedures of current methods are either not suitable for counts or not specific for NanoString nCounter data. We develop a novel DE detection method based on NanoString nCounter data. The method, named NanoStringDiff, considers a generalized linear model of the negative binomial family to characterize count data and allows for multifactor design. Data normalization is incorporated in the model framework through data normalization parameters, which are estimated from positive controls, negative controls and housekeeping genes embedded in the nCounter system. We propose an empirical Bayes shrinkage approach to estimate the dispersion parameter in the model and a likelihood ratio test to identify differentially expressed genes. Simulations and real data analysis demonstrate that the proposed method performs better than existing methods.
| S-EPMC5175344 | biostudies-literature
Project description:Although compatibility is highly advocated in traditional Chinese medicine (TCM), inappropriate combination of some herbs may reduce the therapeutic action and even produce toxic effects. Kansui and licorice, one of TCM "Eighteen Incompatible Medicaments", are the most representative cases of improper herbal combination, which may still be applied simultaneously under given conditions. However, the potential mechanism of their compatibility and incompatibility is unclear. In the present study, two different ratios of kansui and licorice, representing their compatibility and incompatibility respectively, were designed to elucidate their interaction by comparative plasma/tissue metabolomics and a heatmap with relative fold change. As a result, glycocholic acid, prostaglandin F2a, dihydroceramide and sphinganine were screened out as the principal alternative biomarkers of compatibility group; sphinganine, dihydroceramide, arachidonic acid, leukotriene B4, acetoacetic acid and linoleic acid were those of incompatibility group. Based on the values of biomarkers in each tissue, the liver was identified as the compatible target organ, while the heart, liver, and kidney were the incompatible target organs. Furthermore, important pathways for compatibility and incompatibility were also constructed. These results help us to better understand and utilize the two herbs, and the study was the first to reveal some innate characters of herbs related to TCM "Eighteen Incompatible Medicaments".
| S-EPMC6951493 | biostudies-literature
Project description:Visual processing transforms the complexities of the visual world into useful information. Ciona, an invertebrate chordate and close relative of the vertebrates, has one of the simplest nervous systems known, yet has a range of visuomotor behaviors. This simplicity has facilitated studies linking behavior and neural circuitry. Ciona larvae have two distinct visuomotor behaviors - a looming shadow response and negative phototaxis. These are mediated by separate neural circuits that initiate from different clusters of photoreceptors, with both projecting to a CNS structure called the posterior brain vesicle (pBV). We report here that inputs from both circuits are processed to generate fold change detection (FCD) outputs. In FCD, the behavioral response scales with the relative fold change in input, but is invariant to the overall magnitude of the stimulus. Moreover, the two visuomotor behaviors have fundamentally different stimulus/response relationships - indicative of differing circuit strategies, with the looming shadow response showing a power relationship to fold change, while the navigation behavior responds linearly. Pharmacological modulation of the FCD response points to the FCD circuits lying outside of the visual organ (the ocellus), with the pBV being the most likely location. Consistent with these observations, the connectivity and properties of pBV interneurons conform to known FCD circuit motifs, but with different circuit architectures for the two circuits. The negative phototaxis circuit forms a putative incoherent feedforward loop that involves interconnecting cholinergic and GABAergic interneurons. The looming shadow circuit uses the same cholinergic and GABAergic interneurons, but with different synaptic inputs to create a putative non-linear integral feedback loop. These differing circuit architectures are consistent with the behavioral outputs of the two circuits. Finally, while some reports have highlighted parallels between the pBV and the vertebrate midbrain, suggesting a common origin for the two, others reports have disputed this, suggesting that invertebrate chordates lack a midbrain homolog. The convergence of visual inputs at the pBV, and its putative role in visual processing reported here and in previous publications, lends further support to the proposed common origin of the pBV and the vertebrate midbrain.
| S-EPMC8419522 | biostudies-literature
Project description:We study immune responses induced by amyloid targeting antibodies and CAA-mediated microhemorrhages using histology and gene expression analyses in AD mouse models and primary culture settings. We cultured and plated primary bone marrow-derived macrophages in wells coated with high Fc receptor activating antibody IgG2a or isotype control antibody harboring the LALAPG mutation, ablating Fc-FcR mediated effector functions
2023-09-14 | GSE222466 | GEO
Project description:BackgroundTransmembrane proteins (TMPs) are the key components of signal transduction, cell-cell adhesion and energy and material transport into and out from the cells. For the deep understanding of these processes, structure determination of transmembrane proteins is indispensable. However, due to technical difficulties, only a few transmembrane protein structures have been determined experimentally. Large-scale genomic sequencing provides increasing amounts of sequence information on the proteins and whole proteomes of living organisms resulting in the challenge of bioinformatics; how the structural information should be gained from a sequence.ResultsHere, we present a novel method, TMFoldRec, for fold prediction of membrane segments in transmembrane proteins. TMFoldRec based on statistical potentials was tested on a benchmark set containing 124 TMP chains from the PDBTM database. Using a 10-fold jackknife method, the native folds were correctly identified in 77% of the cases. This accuracy overcomes the state-of-the-art methods. In addition, a key feature of TMFoldRec algorithm is the ability to estimate the reliability of the prediction and to decide with an accuracy of 70%, whether the obtained, lowest energy structure is the native one.ConclusionThese results imply that the membrane embedded parts of TMPs dictate the TM structures rather than the soluble parts. Moreover, predictions with reliability scores make in this way our algorithm applicable for proteome-wide analyses.AvailabilityThe program is available upon request for academic use.
| S-EPMC4486421 | biostudies-literature
Project description:Neuropeptides are essential cell-to-cell signaling molecules that influence diverse regulatory and behavioral functions within biological systems. Differing in their amino acid sequences and post-translational modifications, hundreds of neuropeptides are produced via a series of enzymatic processing steps, and their levels vary with location, time, and physiological condition. Due to their wide range of endogenous concentrations and inherent chemical complexity, using mass spectrometry (MS) to accurately quantify changes in peptide levels can be challenging. Here we evaluate three different MS systems for their ability to accurately measure neuropeptide levels: capillary liquid chromatography-electrospray ionization-ion trap (CapLC-ESI-IT) MS, ultraperformance liquid chromatography-electrospray ionization-quadrupole-time-of-flight (UPLC-LC-ESI-Q-TOF) MS, and matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS. Specifically, eight sample mixtures composed of five neuropeptide standards, with four technical replicates of each, were labeled with H(4)/D(4)-succinic anhydride, followed by relative peptide quantitation using the three MS platforms. For these samples, the CapLC-ESI-IT MS platform offered the most robust ability to accurately quantify peptides over a concentration range of 1200-fold, although it required larger sample sizes than the other two platforms. Both the UPLC-ESI-Q-TOF MS and the MALDI-TOF MS systems had lower limits of quantification, with the MALDI-TOF having the lowest. By implementing several data acquisition schemes and optimizing the data analysis approaches, we were able to accurately quantify peptides over a three orders of magnitude concentration range using either the UPLC or MALDI-TOF platforms. Overall these results increase our understanding of both the capabilities and limits of using MS-based approaches to measure peptides.
| S-EPMC3515743 | biostudies-literature