Unknown,Transcriptomics,Genomics,Proteomics

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Human monocyte derived macrophage microarray analysis

ABSTRACT: Microarray data was acquired from human primary monocyte-derived macrophages in order to test the validity of a graphical software package (z-score outlier detection (ZODET)) Complex human diseases can show significant heterogeneity between patients with the same phenotypic disorder. An outlier detection strategy was developed to identify variants at the level of gene transcription that are of potential biological and phenotypic importance. Here we describe a graphical software package (z-score outlier detection (ZODET)) that enables identification and visualisation of gross abnormalities in gene expression (outliers) in individuals, using whole genome microarray data. Mean and standard deviation of expression in a healthy control cohort is used to detect both over and under-expressed probes in individual test subjects. We compared the potential of ZODET to detect outlier genes in gene expression datasets with a previously described statistical method, gene tissue index (GTI), using a simulated expression dataset and a publicly available monocyte-derived macrophage microarray dataset. Taken together, these results support ZODET as a novel approach to identify outlier genes of potential pathogenic relevance in complex human diseases. The algorithm is implemented using R packages and java. 40 volunteer were bled and primary monocyte derived macrophages were cultured. These samples were then randomised into two equal sized groups (control - A1-20 and experimental - B1-20) and run on the ZODET software.

ORGANISM(S): Homo sapiens

SUBMITTER: Tony Segal

PROVIDER: E-GEOD-51256 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Background and aim: Analysis of data obtained from genome wide gene expression experiments is challenging, due to the huge amount of variables, management of the data and the need for multivariate analysis. We here present the R package: pcaGoPromoter that facilitates the interpretation of genome wide expression data to overcome these problems. In a first step principal component analysis is applied to overview any differences between the observations and possible groupings. The next step is interpretation of the principal components with respect to both biological function and involvement of predicted transcription factor binding sites. The robustness of the results is evaluated using cross validation. Illustrative plots of PCA score plots and Gene Ontology terms are available. To illustrate the functionality of the R package, we designed a serum stimulation experiment, where the main biological outcome is well documented. Results: Samples from the serum stimulation experiment were analyzed using the Affymetrix Human Genome U133 Plus 2.0 chip. The array data were analyzed by the tools of the pcaGoPromoter package, which resulted in a clear separation of the observations into the three experimental groups - controls, serum only and serum with inhibitor. The functional annotation of the axes in the PCA score plot showed the expected serum promoted biological processes such as cell cycle progression and the predicted involvement of the expected transcription factors including E2F. In addition unexpected results, e.g. the cholesterol synthesis in serum depleted cells and NF-?B activation in inhibitor treated cells were uncovered. Conclusion: The pcaGoPromoter R package provides a collection of tools for analyzing gene expression data. It works with any platform using gene symbols or Entrez Ids as probe identifiers. In addition support for several popular Affymetrix GeneChip platforms is provided. The tools give an overview of the data via principal component analysis, functional interpretation by Gene Ontology terms (biological processes), and indication of involvement of possible transcription factors. Thus, pcaGoPromoter structures the high-dimensional data of gene expression experiments and can be applied to generate hypotheses for further exploration. A serum starved human monocyte cell line (ATCC® Number: CRL-9853) was stimulated by serum in the presence or absence of the specific Erk-1/2 inhibitor U0126.

Project description:Although current mass spectrometry (MS)-based proteomics identifies and quantifies thousands of proteins and (modified) peptides, only a minority of them are subjected to in-depth downstream analysis. With the advent of automated processing workflows, biologically or clinically important results within a study are rarely validated by visualization of the underlying raw information. Although several tools for this are in principle available, they are often not integrated into the overall analysis nor readily extendable with new approaches. To remedy this, we developed AlphaViz, an open-source Python package to superimpose output from common analysis workflows on the raw data for quick and easy visualization and validation of protein and peptide identifications. AlphaViz takes advantage of recent breakthroughs in the deep learning-assisted prediction of experimental peptide properties to allow manual assessment of the expected and measured peptide result deviation. We focused on the visualization of the 4-dimensional data cuboid provided by Bruker TimsTOF instruments, where the ion mobility dimension, besides intensity and retention time, can be predicted and used for verification. We illustrate how AlphaViz can quickly validate or invalidate peptide identifications regardless of the score given to them by automated workflows. Furthermore, we provide a â€˜predict modeâ€™ that can locate peptides present in the raw data but not reported by the search engine. This is illustrated with dilution series and the recovery of missing values from experimental replicates. Applied to phosphoproteomics of the EGF-signaling pathway, we show how key signaling nodes can be validated to enhance confidence for downstream interpretation or follow-up experiments. AlphaViz follows accepted standards for open-source software development, including extensive documentation, testing and continuous integration. It features an easy-to-install graphical user interface for end-users and a modular Python package for bioinformaticians. We hope that AlphaViz can help to make validation of critical proteomics results a standard feature in MS-based proteomics.

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Human monocyte derived macrophage microarray analysis

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