scholarly journals A measure of agreement across numerous conditions: Reproducibility of co-expression networks across tissues

2017 ◽  
Author(s):  
Alejandro Cáceres ◽  
Juan R. González
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Expression Data ◽  
Full Agreement ◽  
Statistical Measures ◽  
Expression Studies ◽  
Genome Wide ◽  
Gene Expression Studies ◽  
Microarray Studies ◽  
Level Of Agreement

AbstractThere is great interest to study how co-expression gene networks change across tissues. However, the reproducibility assessment of these studies is challenged by a lack of fully confirmatory experiments from independent researchers. While an increment in the number of studies with expression data for several tissues is expected, statistical measures are still needed to assess the reproducibility between studies. We identified a gap in the statistical literature concerning the assessment of agreement between studies across numerous conditions. The gap precluded us to test, using standard statistics, the level of agreement between the GTEX (RNAseq) and BRAINEAC (microarray) studies to distinguish the structure of co-expression networks across four brain tissues. We propose a generalization of a classical measure of agreement, Cohen’s κ, derive its distributional characteristics and determine its reliability properties. In the gene expression studies, our generalization of κ showed full agreement for genome-wide networks in BRAINEAC benchmarked against GTEX, and highest agreement for brain specific pathways. Our highly interpretable measure can contribute to anticipated efforts on reproducibility research.

Gene Expression Studies May Identify Lower Risk Myelodysplastic Syndrome Patients Likely to Respond to Therapy with Ezatiostat Hydrochloride (TLK199)

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2779-2779
Author(s):  
Naomi Galili ◽  
Pablo Tamayo ◽  
Olga B Botvinnik ◽  
Jill P Mesirov ◽  
Jennifer Zikria ◽  
...  
Keyword(s):  
Gene Expression ◽  
Angiotensin Ii ◽  
Mononuclear Cells ◽  
Expression Data ◽  
Phase 2 ◽  
Glutathione S Transferase ◽  
Gene Set ◽  
Expression Studies ◽  
Phase 2 Study ◽  
Gene Expression Studies

Abstract Abstract 2779 Interpretation of gene expression studies in MDS have been especially challenging due to the heterogeneity of the cell lineages that comprise the malignant clone. In attempting to overcome these difficulties we have used a bedside-to-bench approach to define an expression signature that may identify patients likely to respond. Ezatiostat hydrochloride (TLK199) is an inhibitor of glutathione S-transferase, an enzyme that is over expressed in many cancers, and has been shown in vitro to stimulate growth and differentiation of hematopoietic progenitor cells and to induce apoptosis in leukemia cells. Based on multilineage responses in low-Int1 MDS patients in our phase 2 study of oral TLK199, a multi institutional phase 2 study was conducted in low-Int1 patients. Response was evaluated by International Working Group (IWG 2006) criteria. Pre-therapy bone marrow mononuclear cells of patients treated with TLK199 were analyzed for gene expression on the Illumina HT12v4 whole genome array with IRB approval. RNA isolated from the marrow mononuclear cells was available on 9 responders (R) and 21 non-responders (NR). Five R and 13 NR were randomly chosen to create a training set with the intent to later use the remaining samples for model testing. We identified the top 100 differentially expressed genes using a sensitive metric based on the normalized mutual information. We also performed single-sample Gene Set Enrichment Analysis to find the most salient differences in terms of pathways and biological processes between R/NR. Of special note are the 4 microRNA s differentially expressed between R/NR. Three miRNAs are under-expressed (miR-129, 802 and 548e) and one (miR-155) is over-expressed in R. Reduced expression of miR-129 has been reported in solid tumors when over-expressed has been shown to have anti-proliferative activity in cell lines. SOX4 is a target gene for miR129 and reduced expression of miR-129 results in concomitant up-regulation of SOX4 mRNA which can function as both an oncogene and a tumor suppressor gene depending on tumor lineage. Over-expression of SOX4 inhibited cytokine induced granulocyte maturation in the myeloid 32Dcl3 cell line suggesting a possible role in MDS. MiR-802 targets the receptor for angiotensin II and when expression is decreased there is increased angiotensin II activity. It has recently been shown that angiotensin is a pro-inflammatory mediator that participates in apoptosis, angiogenesis and promotes mitochondrial dysfunction, all characteristics of MDS. In addition, the transcription factor ZFHX3, a predicted target of miR-802, is a negative regulator of c-MYB which has been shown to be up-regulated in all subtypes of MDS. Similarly, c-MYB is a predicted target of miR-155, which is over-expressed in TLK199 responders. MiR-155 was shown to be over-expressed in marrow cells of a subset of human AML patients. Of particular note are the studies showing that sustained expression of miR-155 in mouse hematopoietic stem cells cause a myeloproliferative/myelodysplastic disorder. Subsequent pathway analysis of this expression data revealed that a JNK gene set as defined from the GEO dataset GDSS8081 was consistently under-expressed in responders and over-expressed in non-responders. TLK199 has been shown to induce JUN/JNK by binding to glutathione S-transferase, a key inhibitor of this pathway. The expression data confirms that patients whose pre-therapy marrow shows under-expression of the JNK gene set are precisely those who benefit from this drug therapy and those patients who already over-express these genes are unlikely to respond. This study highlights two important points: 1) Using a bedside-to-bench strategy yielded a signature that distinguished responders from non-responders 2) The signature identified genes and signaling pathways that shed light on both the biology of the disease and the mechanism of action of the drug. In conclusion, if these results are confirmed in the test set, we will use the signature in a future prospective study to preselect MDS patients for therapy with this promising drug. Disclosures: Brown: Telik, Inc.: Employment, Equity Ownership.

scholarly journals Gene Expression Studies to Identify Significant Genes in AR, MTOR, MAPK Pathways and their Overlapping Regulatory Role in Prostate Cancer

2019 ◽  
Vol 16 (3) ◽  
Author(s):  
Nimisha Asati ◽  
Abhinav Mishra ◽  
Ankita Shukla ◽  
Tiratha Raj Singh
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Data ◽  
Meta Analysis ◽  
Expression Patterns ◽  
Mitogen Activated Protein Kinase ◽  
Expression Data ◽  
Mapk Pathways ◽  
Expression Studies ◽  
Gene Expression Studies

AbstractGene expression studies revealed a large degree of variability in gene expression patterns particularly in tissues even in genetically identical individuals. It helps to reveal the components majorly fluctuating during the disease condition. With the advent of gene expression studies many microarray studies have been conducted in prostate cancer, but the results have varied across different studies. To better understand the genetic and biological regulatory mechanisms of prostate cancer, we conducted a meta-analysis of three major pathways i.e. androgen receptor (AR), mechanistic target of rapamycin (mTOR) and Mitogen-Activated Protein Kinase (MAPK) on prostate cancer. Meta-analysis has been performed for the gene expression data for the human species that are exposed to prostate cancer. Twelve datasets comprising AR, mTOR, and MAPK pathways were taken for analysis, out of which thirteen potential biomarkers were identified through meta-analysis. These findings were compiled based upon the quantitative data analysis by using different tools. Also, various interconnections were found amongst the pathways in study. Our study suggests that the microarray analysis of the gene expression data and their pathway level connections allows detection of the potential predictors that can prove to be putative therapeutic targets with biological and functional significance in progression of prostate cancer.

scholarly journals Pathway-Level Information ExtractoR (PLIER) for gene expression data

2017 ◽  
Author(s):  
Weiguang Mao ◽  
Elena Zaslavsky ◽  
Boris M. Hartmann ◽  
Stuart C. Sealfon ◽  
Maria Chikina
Keyword(s):  
Gene Expression ◽  
Global Gene Expression ◽  
Expression Data ◽  
Regulate Gene Expression ◽  
Expression Studies ◽  
Inference Algorithms ◽  
Biological Insight ◽  
Level Information ◽  
Gene Expression Studies ◽  
Cell Proportion

AbstractA major challenge in gene expression analysis is to accurately infer relevant biological insight, such as regulation of cell type proportion or pathways, from global gene expression studies. We present a general solution for this problem that outperforms available cell proportion inference algorithms, and is more widely useful to automatically identify specific pathways that regulate gene expression. Our method improves replicability and biological insight when applied to trans-eQTL identification.

scholarly journals variancePartition: Interpreting drivers of variation in complex gene expression studies

2016 ◽  
Author(s):  
Gabriel E Hoffman ◽  
Eric E Schadt
Keyword(s):  
Gene Expression ◽  
Multiple Sources ◽  
Disease Biology ◽  
Expression Studies ◽  
Genome Wide ◽  
A Genome ◽  
Gene Expression Studies

As genomics studies become more complex and consider multiple sources of biological and technical variation, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics. We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation with a genome-wide summary, and identify genes that deviate from the genome-wide trend. variancePartition enables rapid interpretation of complex gene expression studies and is applicable to many genomics assays.

scholarly journals Threshold-free high-power methods for the ontological analysis of genome-wide gene-expression studies

2007 ◽  
Vol 8 (5) ◽  
pp. R74 ◽  
Author(s):  
Björn Nilsson ◽  
Petra Håkansson ◽  
Mikael Johansson ◽  
Sven Nelander ◽  
Thoas Fioretos
Keyword(s):  
Gene Expression ◽  
High Power ◽  
Expression Studies ◽  
Genome Wide ◽  
Ontological Analysis ◽  
Gene Expression Studies ◽  
Genome Wide Gene Expression

Sample Selection for Microarray Gene Expression Studies

2005 ◽  
Vol 44 (03) ◽  
pp. 461-467 ◽  
Author(s):  
D. Repsilber ◽  
L. Fink ◽  
M. Jacobsen ◽  
O. Bläsing ◽  
A. Ziegler
Keyword(s):  
Gene Expression ◽  
Study Groups ◽  
Case Control Studies ◽  
Microarray Gene Expression ◽  
Microarray Experiments ◽  
Expression Studies ◽  
Microarray Gene ◽  
Gene Expression Studies ◽  
Microarray Studies ◽  
Study Base

Summary Objectives: The choice of biomedical samples for microarray gene expression studies is decisive for both validity and interpretability of results. We present a consistent, comprehensive framework to deal with the typical selection problems in microarray studies. Methods: Microarray studies are designed either as case-control studies or as comparisons of parallel groups from cohort studies, since high levels of random variation in the experimental approach thwart absolute measurements of gene expression levels. Validity and results of gene expression studies heavily rely on the appropriate choice of these study groups. Therefore, the so-called principles of comparability, which are well known from both clinical and epidemiological studies, need to be applied to microarray experiments. Results: The principles of comparability are the study-base principle, the principle of deconfounding and the principle of comparable accuracy in measurements. We explain each of these principles, show how they apply to microarray experiments, and illustrate them with examples. The examples are chosen as to represent typical stumbling blocks of microarray experimental design, and to exemplify the benefits of implementing the principles of comparability in the setting of micro-array experiments. Conclusions: Microarray studies are closely related to classical study designs and therefore have to obey the same principles of comparability as these. Their validity should not be compromised by selection, confounding or information bias. The so-called study-base principle, calling for comparability and thorough definition of the compared cell populations, is the key principle for the choice of biomedical samples and controls in microarray studies.

scholarly journals Tissue heterogeneity is prevalent in gene expression studies

2020 ◽  
Author(s):  
Gregor Sturm ◽  
Markus List ◽  
Jitao David Zhang
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Tissue Type ◽  
Control Methods ◽  
Expression Data ◽  
Batch Effects ◽  
Tissue Heterogeneity ◽  
Sample Handling ◽  
Expression Studies ◽  
Gene Expression Studies

Background: Lack of reproducibility in gene expression studies has recently attracted much attention in and beyond the biomedical research community. Previous efforts have identified many underlying factors, such as batch effects and incorrect sample annotations. Recently, tissue heterogeneity, a consequence of unintended profiling of cells of other origins than the tissue of interest, was proposed as a source of variance that exacerbates irreproducibility and is commonly ignored. Results: Here, we systematically analyzed 2,692 publicly available gene expression datasets including 78,332 samples for tissue heterogeneity. We found a prevalence of tissue heterogeneity in gene expression data that affects on average 5-15% of the samples, depending on the tissue type. We distinguish cases of severe heterogeneity, which may be caused by mistakes in annotation or sample handling, from cases of moderate heterogeneity, which are more likely caused by tissue infiltration or sample contamination. Conclusions: Tissue heterogeneity is a widespread issue in publicly available gene expression datasets and thus an important source of variance that should not be ignored. We advocate the application of quality control methods such as BioQC to detect tissue heterogeneity prior to mining or analysing gene expression data.

scholarly journals JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts

2017 ◽  
Author(s):  
Chris Chatzinakos ◽  
Donghyung Lee ◽  
Bradley T Webb ◽  
Vladimir I Vladimirov ◽  
Kenneth S Kendler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Supplementary Information ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Expression Studies ◽  
Genome Wide ◽  
Gene Level ◽  
Supplementary Material ◽  
Gene Expression Studies

AbstractMotivationTo increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on i) summary statistics from genome-wide association studies (GWAS) and ii) linkage disequilibrium (LD) patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals.ResultsWe propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses i) cis-eQTL SNPs from the latest expression studies and ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and ii), to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of [email protected] informationSupplementary material is available at Bioinformatics online.

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