PADGE: analysis of heterogeneous patterns of differential gene expression

2007 ◽  
Vol 32 (1) ◽  
pp. 154-159 ◽  
Author(s):  
Li Li ◽  
Amitabha Chaudhuri ◽  
John Chant ◽  
Zhijun Tang

We have devised a novel analysis approach, percentile analysis for differential gene expression (PADGE), for identifying genes differentially expressed between two groups of heterogeneous samples. PADGE was designed to compare expression profiles of sample subgroups at a series of percentile cutoffs and to examine the trend of relative expression between sample groups as expression level increases. Simulation studies showed that PADGE has more statistical power than t-statistics, cancer outlier profile analysis (COPA) (Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Science 310: 644–648, 2005), and kurtosis (Teschendorff AE, Naderi A, Barbosa-Morais NL, Caldas C. Bioinformatics 22: 2269–2275, 2006). Application of PADGE to microarray data sets in tumor tissues demonstrated its utility in prioritizing cancer genes encoding potential therapeutic targets or diagnostic markers. A web application was developed for researchers to analyze a large gene expression data set from heterogeneous biological samples and identify differentially expressed genes between subsets of sample classes using PADGE and other available approaches. Availability: http://www.cgl.ucsf.edu/Research/genentech/padge/ .

2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.


Blood ◽  
2009 ◽  
Vol 114 (23) ◽  
pp. 4847-4858 ◽  
Author(s):  
Kunju Sridhar ◽  
Douglas T. Ross ◽  
Robert Tibshirani ◽  
Atul J. Butte ◽  
Peter L. Greenberg

AbstractMicroarray analysis with 40 000 cDNA gene chip arrays determined differential gene expression profiles (GEPs) in CD34+ marrow cells from myelodysplastic syndrome (MDS) patients compared with healthy persons. Using focused bioinformatics analyses, we found 1175 genes significantly differentially expressed by MDS versus normal, requiring a minimum of 39 genes to separately classify these patients. Major GEP differences were demonstrated between healthy and MDS patients and between several MDS subgroups: (1) those whose disease remained stable and those who subsequently transformed (tMDS) to acute myeloid leukemia; (2) between del(5q) and other MDS patients. A 6-gene “poor risk” signature was defined, which was associated with acute myeloid leukemia transformation and provided additive prognostic information for International Prognostic Scoring System Intermediate-1 patients. Overexpression of genes generating ribosomal proteins and for other signaling pathways was demonstrated in the tMDS patients. Comparison of del(5q) with the remaining MDS patients showed 1924 differentially expressed genes, with underexpression of 1014 genes, 11 of which were within the 5q31-32 commonly deleted region. These data demonstrated (1) GEPs distinguishing MDS patients from healthy and between those with differing clinical outcomes (tMDS vs those whose disease remained stable) and cytogenetics [eg, del(5q)]; and (2) molecular criteria refining prognostic categorization and associated biologic processes in MDS.


2012 ◽  
Vol 11 ◽  
pp. CIN.S9542 ◽  
Author(s):  
Niklaus Fankhauser ◽  
Igor Cima ◽  
Peter Wild ◽  
Wilhelm Krek

Mutations in cancer-causing genes induce changes in gene expression programs critical for malignant cell transformation. Publicly available gene expression profiles produced by modulating the expression of distinct cancer genes may therefore represent a rich resource for the identification of gene signatures common to seemingly unrelated cancer genes. We combined automatic retrieval with manual validation to obtain a data set of high-quality gene microarray profiles. This data set was used to create logical models of the signaling events underlying the observed expression changes produced by various cancer genes and allowed to uncover unknown and verifiable interactions. Data clustering revealed novel sets of gene expression profiles commonly regulated by distinct cancer genes. Our method allows retrieval of significant new information and testable hypotheses from a pool of deposited cancer gene expression experiments that are otherwise not apparent or appear insignificant from single measurements. The complete results are available through a web-application at http://biodata.ethz.ch/cgi-bin/geologic .


2020 ◽  
Vol 6 (4) ◽  
pp. 205521732097851
Author(s):  
IS Brorson ◽  
AM Eriksson ◽  
IS Leikfoss ◽  
V Vitelli ◽  
EG Celius ◽  
...  

Background Genetic and clinical observations have indicated T cells are involved in MS pathology. There is little insight in how T cells are involved and whether or not these can be used as markers for MS. Objectives Analysis of the gene expression profiles of circulating CD8+ T cells of MS patients compared to healthy controls. Methods RNA from purified CD8+ T cells was sequenced and analyzed for differential gene expression. Pathway analyses of genes at several p-value cutoffs were performed to identify putative pathways involved. Results We identified 36 genes with significant differential gene expression in MS patients. Four genes reached at least 2-fold differences in expression. The majority of differentially expressed genes was higher expressed in MS patients. Genes associated to MS in GWAS showed enrichment amongst the differentially expressed genes. We did not identify enrichment of specific pathways amongst the differentially expressed genes in MS patients. Conclusions CD8+ T cells of MS patients show differential gene expression, with predominantly higher activity of genes in MS patients. We do not identify specific biological pathways in our study. More detailed analysis of CD8+ T cells and subtypes of these may increase understanding of how T cells are involved in MS.


Zygote ◽  
2015 ◽  
Vol 24 (2) ◽  
pp. 266-276 ◽  
Author(s):  
Fazl A. Ashkar ◽  
Tamas Revay ◽  
NaYoung Rho ◽  
Pavneesh Madan ◽  
Isabelle Dufort ◽  
...  

SummaryThyroid hormones (THs) have been shown to improve in vitro embryo production in cattle by increasing blastocyst formation rate, and the average cell number of blastocysts and by significantly decreasing apoptosis rate. To better understand those genetic aspects that may underlie enhanced early embryo development in the presence of THs, we characterized the bovine embryonic transcriptome at the blastocyst stage, and examined differential gene expression profiles using a bovine-specific microarray. We found that 1212 genes were differentially expressed in TH-treated embryos when compared with non-treated controls (>1.5-fold at P < 0.05). In addition 23 and eight genes were expressed uniquely in control and treated embryos, respectively. The expression of genes specifically associated with metabolism, mitochondrial function, cell differentiation and development were elevated. However, TH-related genes, including those encoding TH receptors and deiodinases, were not differentially expressed in treated embryos. Furthermore, the over-expression of 52 X-chromosome linked genes in treated embryos suggested a delay or escape from X-inactivation. This study highlights the significant impact of THs on differential gene expression in the early embryo; the identification of TH-responsive genes provides an insight into those regulatory pathways activated during development.


2010 ◽  
Vol 22 (1) ◽  
pp. 297
Author(s):  
L. Jiang ◽  
S. L. Marjani ◽  
M. Bertolini ◽  
H. A. Lewin ◽  
G. B. Anderson ◽  
...  

During the past several decades, in vitro fertilization (IVF) has been increasingly used in animal production and human infertility treatment. In vitro production (IVP) has been shown to cause reduced developmental competence, aberrant gene expression, and developmental abnormalities. Our objective was to determine how in vitro procedures influence global gene expression during fetal development. To this end, we analyzed the gene expression profiles of liver and placentome tissue samples (n = 18) from IVP and in vivo-derived fetuses at Days 90 and 180 of gestation (n = 5 IVP and n = 4 in vivo-derived pregnancies for each day of gestation). Standard in vitro maturation and fertilization protocols were employed. Putative zygotes were co-cultured with bovine oviductal epithelial cells to the blastocyst stage. In vivo embryos were collected 7 days after AI by nonsurgical uterine flushing. Blastocyst-stage IVP and in vivo embryos were transferred to synchronized recipients and monitored until collection at Day 90 or 180. The pregnancy rate at Day 90 was 12% and 27% for IVP and in vivo pregnancies, respectively (Bertolini et al. 2004 Reproduction 128, 341-354). To conduct expression profiling, total RNA from each tissue sample and a standard reference was indirectly labeled with Cy3 and Cy5, respectively, and hybridized in duplicate to custom, bovine 13 K oligonucleotide microarrays. After Loess normalization, a two-way (origin and day) ANOVA model (GeneSpring 7.3.1) was used to identify differentially expressed genes in each tissue. The P-values were adjusted for multiple comparisons using a 5% false discovery rate (FDR). The expression of 11 candidate genes was confirmed independently by quantitative RT-PCR. Surprisingly, in both the liver and placentome tissues, no differential gene expression was detected between the IVP and in vivo fetuses at Day 90 and 180. This was observed even when the FDR was relaxed to 10% and 20%. A total of 879 genes (523 genes ≥ 1.5-fold) were differentially expressed during liver development from 90 to 180 days of gestation. Conversely, no differential gene expression was detected in the placentomes during this developmental period. Our findings show that during early and mid gestation, surviving IVP fetuses had normal patterns of gene expression. It is possible that embryos with less severe perturbations may survive with their gene expression normalized as development proceeds. Additionally, initial changes in gene expression caused by IVP may affect subsequent development, but do not necessarily persist throughout gestation. Present addresses: L. Jiang, Columbia University, New York, NY, USA; S. L. Marjani, Yale University, New Haven, CT, USA; M. Bertolini, University of Fortaleza, CE, Brazil. This work was supported by USDA grants to X.Y, H.A.L., and X.C T.


PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7821 ◽  
Author(s):  
Xiaoming Zhang ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
Zhengguo He ◽  
Cun Liu ◽  
...  

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.


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