33 research outputs found
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