A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments-0

<p><b>Copyright information:</b></p><p>Taken from "A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments"</p><p>http://www.biomedcentral.com/1471-2105/8/364</p><p>BMC Bioinformatics 2007;8():364-364.</p><p>Published online 27 Sep 2007</p><p>PMCID:PMC2246152.</p><p></p>wer) scales. This gene is uniformly underexpressed in metastatic samples. Open circles indicate primary tumor samples, and stars indicate metastatic samples

A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments-6

<p><b>Copyright information:</b></p><p>Taken from "A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments"</p><p>http://www.biomedcentral.com/1471-2105/8/364</p><p>BMC Bioinformatics 2007;8():364-364.</p><p>Published online 27 Sep 2007</p><p>PMCID:PMC2246152.</p><p></p>ale. The color strip in blue and yellow below the heatmap indicates primary and metastatic tumors, respectivel

A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments-8

<p><b>Copyright information:</b></p><p>Taken from "A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments"</p><p>http://www.biomedcentral.com/1471-2105/8/364</p><p>BMC Bioinformatics 2007;8():364-364.</p><p>Published online 27 Sep 2007</p><p>PMCID:PMC2246152.</p><p></p>e POE scale

A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments-1

<p><b>Copyright information:</b></p><p>Taken from "A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments"</p><p>http://www.biomedcentral.com/1471-2105/8/364</p><p>BMC Bioinformatics 2007;8():364-364.</p><p>Published online 27 Sep 2007</p><p>PMCID:PMC2246152.</p><p></p> This gene is underexpressed primarily in metastatic samples of the Chen liver study. Open circles indicate primary tumor samples, and stars indicate metastatic samples

A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments-7

<p><b>Copyright information:</b></p><p>Taken from "A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments"</p><p>http://www.biomedcentral.com/1471-2105/8/364</p><p>BMC Bioinformatics 2007;8():364-364.</p><p>Published online 27 Sep 2007</p><p>PMCID:PMC2246152.</p><p></p> scale. The color strip in blue and yellow below the heatmap indicate primary and metastatic tumors, respectively

SAINT-MS1: Protein–Protein Interaction Scoring Using Label-free Intensity Data in Affinity Purification-Mass Spectrometry Experiments

We present a statistical method SAINT-MS1 for scoring protein–protein interactions based on the label-free MS1 intensity data from affinity purification-mass spectrometry (AP-MS) experiments. The method is an extension of Significance Analysis of INTeractome (SAINT), a model-based method previously developed for spectral count data. We reformulated the statistical model for log-transformed intensity data, including adequate treatment of missing observations, that is, interactions identified in some but not all replicate purifications. We demonstrate the performance of SAINT-MS1 using two recently published data sets: a small LTQ-Orbitrap data set with three replicate purifications of single human bait protein and control purifications and a larger drosophila data set targeting insulin receptor/target of rapamycin signaling pathway generated using an LTQ-FT instrument. Using the drosophila data set, we also compare and discuss the performance of SAINT analysis based on spectral count and MS1 intensity data in terms of the recovery of orthologous and literature-curated interactions. Given rapid advances in high mass accuracy instrumentation and intensity-based label-free quantification software, we expect that SAINT-MS1 will become a useful tool allowing improved detection of protein interactions in label-free AP-MS data, especially in the low abundance range

A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments-9

<p><b>Copyright information:</b></p><p>Taken from "A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments"</p><p>http://www.biomedcentral.com/1471-2105/8/364</p><p>BMC Bioinformatics 2007;8():364-364.</p><p>Published online 27 Sep 2007</p><p>PMCID:PMC2246152.</p><p></p>the POE scale

PECA: A Novel Statistical Tool for Deconvoluting Time-Dependent Gene Expression Regulation

Protein expression varies as a result of intricate regulation of synthesis and degradation of messenger RNAs (mRNA) and proteins. Studies of dynamic regulation typically rely on time-course data sets of mRNA and protein expression, yet there are no statistical methods that integrate these multiomics data and deconvolute individual regulatory processes of gene expression control underlying the observed concentration changes. To address this challenge, we developed Protein Expression Control Analysis (PECA), a method to quantitatively dissect protein expression variation into the contributions of mRNA synthesis/degradation and protein synthesis/degradation, termed RNA-level and protein-level regulation respectively. PECA computes the rate ratios of synthesis versus degradation as the statistical summary of expression control during a given time interval at each molecular level and computes the probability that the rate ratio changed between adjacent time intervals, indicating regulation change at the time point. Along with the associated false-discovery rates, PECA gives the complete description of dynamic expression control, that is, which proteins were up- or down-regulated at each molecular level and each time point. Using PECA, we analyzed two yeast data sets monitoring the cellular response to hyperosmotic and oxidative stress. The rate ratio profiles reported by PECA highlighted a large magnitude of RNA-level up-regulation of stress response genes in the early response and concordant protein-level regulation with time delay. However, the contributions of RNA- and protein-level regulation and their temporal patterns were different between the two data sets. We also observed several cases where protein-level regulation counterbalanced transcriptomic changes in the early stress response to maintain the stability of protein concentrations, suggesting that proteostasis is a proteome-wide phenomenon mediated by post-transcriptional regulation

PECA: A Novel Statistical Tool for Deconvoluting Time-Dependent Gene Expression Regulation

Protein expression varies as a result of intricate regulation of synthesis and degradation of messenger RNAs (mRNA) and proteins. Studies of dynamic regulation typically rely on time-course data sets of mRNA and protein expression, yet there are no statistical methods that integrate these multiomics data and deconvolute individual regulatory processes of gene expression control underlying the observed concentration changes. To address this challenge, we developed Protein Expression Control Analysis (PECA), a method to quantitatively dissect protein expression variation into the contributions of mRNA synthesis/degradation and protein synthesis/degradation, termed RNA-level and protein-level regulation respectively. PECA computes the rate ratios of synthesis versus degradation as the statistical summary of expression control during a given time interval at each molecular level and computes the probability that the rate ratio changed between adjacent time intervals, indicating regulation change at the time point. Along with the associated false-discovery rates, PECA gives the complete description of dynamic expression control, that is, which proteins were up- or down-regulated at each molecular level and each time point. Using PECA, we analyzed two yeast data sets monitoring the cellular response to hyperosmotic and oxidative stress. The rate ratio profiles reported by PECA highlighted a large magnitude of RNA-level up-regulation of stress response genes in the early response and concordant protein-level regulation with time delay. However, the contributions of RNA- and protein-level regulation and their temporal patterns were different between the two data sets. We also observed several cases where protein-level regulation counterbalanced transcriptomic changes in the early stress response to maintain the stability of protein concentrations, suggesting that proteostasis is a proteome-wide phenomenon mediated by post-transcriptional regulation

PECA: A Novel Statistical Tool for Deconvoluting Time-Dependent Gene Expression Regulation

Protein expression varies as a result of intricate regulation of synthesis and degradation of messenger RNAs (mRNA) and proteins. Studies of dynamic regulation typically rely on time-course data sets of mRNA and protein expression, yet there are no statistical methods that integrate these multiomics data and deconvolute individual regulatory processes of gene expression control underlying the observed concentration changes. To address this challenge, we developed Protein Expression Control Analysis (PECA), a method to quantitatively dissect protein expression variation into the contributions of mRNA synthesis/degradation and protein synthesis/degradation, termed RNA-level and protein-level regulation respectively. PECA computes the rate ratios of synthesis versus degradation as the statistical summary of expression control during a given time interval at each molecular level and computes the probability that the rate ratio changed between adjacent time intervals, indicating regulation change at the time point. Along with the associated false-discovery rates, PECA gives the complete description of dynamic expression control, that is, which proteins were up- or down-regulated at each molecular level and each time point. Using PECA, we analyzed two yeast data sets monitoring the cellular response to hyperosmotic and oxidative stress. The rate ratio profiles reported by PECA highlighted a large magnitude of RNA-level up-regulation of stress response genes in the early response and concordant protein-level regulation with time delay. However, the contributions of RNA- and protein-level regulation and their temporal patterns were different between the two data sets. We also observed several cases where protein-level regulation counterbalanced transcriptomic changes in the early stress response to maintain the stability of protein concentrations, suggesting that proteostasis is a proteome-wide phenomenon mediated by post-transcriptional regulation