Mulcom: a multiple comparison statistical test for microarray data in Bioconductor
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Enzo Medico
PROVIDER: E-GEOD-26736 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Background Many microarray experiments search for genes with differential expression between a common “reference” group and multiple test groups, like in the case of time-course designs or of various treatments versus a control condition. In such cases, currently employed statistical approaches based on t-test or close derivatives have limited efficacy, mostly because estimation of noise is done on only two groups at time. Alternative approaches based on ANOVA correctly capture noise from all the groups, but then do not confront single test groups with the reference. We therefore conceived a statistical test for pairwise comparisons between the reference group and each test group that uses within-group variance calculated from all the groups. Results We implemented an R-Bioconductor package named Mulcom, with a statistical test derived from the Dunnett’s test, designed to compare multiple experimental groups against a common reference. In addition to the basic Dunnett’s t value, the package includes an optional minimal fold-change threshold, m. Thanks to automated, permutation-based estimation of False Discovery Rate (FDR), the package also permits fast optimization of the test, to obtain the maximum number of significant genes at a given FDR value. When applied on a time-course experiment profiled in parallel on two microarray platforms, and compared with currently used tests, Mulcom displayed higher concordance of significant genes in the two array platforms, and higher enrichment in functional annotation to categories related to the biology of the experiment. Conclusions The Mulcom package provides a fast and powerful tool for the identification of differentially expressed genes when several experimental conditions are compared with a common reference. We found that Mulcom leads to lists of differentially expressed genes that are particularly consistent across microarray platforms and enriched in significant classes of genes. In our opinion, the main reasons for these good performances are three: (i) within-group variability is estimated from all experimental groups even if only two of them are compared each time; (ii) the optional fold-change threshold m avoids false positives due to aberrantly low within-group variability; (iii) automated test optimization allows maximizing sensitivity without compromising specificity. Ten MDA-MB-435 samples, biological duplicates of each condition (untreated, integrin Beta4 treatment, hepatocyte growth factor treatment for 1 hr, 6 hrs, or 24 hrs).
2011-08-09 | E-GEOD-26735 | biostudies-arrayexpress
Project description:Background Many microarray experiments search for genes with differential expression between a common “reference” group and multiple test groups, like in the case of time-course designs or of various treatments versus a control condition. In such cases, currently employed statistical approaches based on t-test or close derivatives have limited efficacy, mostly because estimation of noise is done on only two groups at time. Alternative approaches based on ANOVA correctly capture noise from all the groups, but then do not confront single test groups with the reference. We therefore conceived a statistical test for pairwise comparisons between the reference group and each test group that uses within-group variance calculated from all the groups. Results We implemented an R-Bioconductor package named Mulcom, with a statistical test derived from the Dunnett’s test, designed to compare multiple experimental groups against a common reference. In addition to the basic Dunnett’s t value, the package includes an optional minimal fold-change threshold, m. Thanks to automated, permutation-based estimation of False Discovery Rate (FDR), the package also permits fast optimization of the test, to obtain the maximum number of significant genes at a given FDR value. When applied on a time-course experiment profiled in parallel on two microarray platforms, and compared with currently used tests, Mulcom displayed higher concordance of significant genes in the two array platforms, and higher enrichment in functional annotation to categories related to the biology of the experiment. Conclusions The Mulcom package provides a fast and powerful tool for the identification of differentially expressed genes when several experimental conditions are compared with a common reference. We found that Mulcom leads to lists of differentially expressed genes that are particularly consistent across microarray platforms and enriched in significant classes of genes. In our opinion, the main reasons for these good performances are three: (i) within-group variability is estimated from all experimental groups even if only two of them are compared each time; (ii) the optional fold-change threshold m avoids false positives due to aberrantly low within-group variability; (iii) automated test optimization allows maximizing sensitivity without compromising specificity. Ten MDA-MB-435 samples, biological duplicates of each condition (untreated, integrin Beta4 treatment, hepatocyte growth factor treatment for 1 hr, 6 hrs, or 24 hrs).
2011-08-09 | E-GEOD-26732 | biostudies-arrayexpress
Project description:"The analysis was designed to search for putative seed sequences for miR-126 in the 3M-bM-^@M-^YUTR of up-regulated genes. ECs were co-transfected with anti-miR-126 and a plasmid vector bearing the GFP-coding sequence and three complementary sites for miR-126 downstream. As a consequence of the presence of miR-126 binding sites the GFP expression was under the control of miR-126. Fluorescente cells were the cells transfected with anti-miR-126 oligo. MiR-126 target enrichment analysis was performed on the basis of the Miranda database, as provided by Diana miRGen. Significance was estimated on the basis of hyper geometric distribution p-values comparing the occurrence of miR targets in the signature with respect to the universe, defined as the genes expressed in HUVEC. Frequency of log 2 ratio between the subset of mir-126 targets and the genes which are not targets of miR-126 were plotted, highlighting enrichment of the target of miR in differentially regulated genes. " 3 x biological replicates of transiently trasfected cells with anti miR-126 oligo (LNA), and controls oligo. 2 biological replicates for GFP are included as technical controls.
2013-10-18 | E-GEOD-32089 | biostudies-arrayexpress
Project description:Germline cell-derived pluripotent stem cells (GPSCs) are similar to embryonic stem (ES) cells in that they can proliferate intensively and differentiate into a variety of cell types, including cardiomyocytes and neurons. In this report, mouse GPSCs were induced to differentiate into hepatocytes with very high efficiency, and demonstrated, for the first time, to be functional. These hepatocytes were characterised at cellular and molecular levels. The GPSC-derived hepatocytes not only expressed hepatic markers, but were also metabolically active as shown by albumin and haptoglobin secretion, urea synthesis, glycogen storage and indocyanine green uptake. Previous studies have revealed some inherent differences in gene expression between undifferentiated mouse ES cells and GPSCs. We wanted to investigate whether this difference may impact on the hepatocyte differentiation capacity of the GPSCs. Large-scale gene expression profiling revealed a strong similarity between GPSC and ES cells at different stages of induced hepatic differentiation. Moreover, Pearson correlation analysis of the microarray datasets revealed that, at late hepatic differentiation stages, the in vitro-derived cells were closer to fetal mouse primary hepatocytes. Thus, adult GPSCs offer great potential for cell ment therapy for a wide variety of liver diseases. Mouse ES cells and GPSCs at various times of hepatocyte differentiation, compared to embryonal (E16) and postnatal (PN1) mouse primary hepatocytes. The supplementary file 'GSE19044_non-normalized.txt' contains non-normalized data for Samples GSM471318-GSM471359.
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Project description:Eighteen independent neurospheres derived from patients affected by primary glioblastoma were grouped into “classical”, “mesenchymal” or “proneural” subtypes according to analysis of genetic lesions and gene expression profiling. Here we show that expression of the MET oncogene, encoding the tyrosine kinase receptor for HGF, associates with mesenchymal and proneural neurospheres (Met-pos-NS). Met expression is almost absent from classical neurospheres (Met-neg-NS), and mutually exclusive with amplification and expression of the EGF receptor gene. Met-pos-NS and Met-neg-NS display distinct growth factor requirements, differentiate along divergent pathways, and generate tumors with distinctive histological features. Met-pos-NS contain a variable percentage of Met positive (Methigh) and Met negative (Metneg) cells. After purification, only Methigh cells display clonogenic ability in vitro, and regenerate neurospheres containing both Methigh and Metneg cells. After in vivo transplantation, Methigh cells display highly enriched tumorigenic potential as compared with Metneg cells. At functional level, in Methigh cells, HGF concomitantly sustains proliferation, clonogenicity, expression of self-renewal markers, migration and invasion. These data show that Met is a functional marker of glioblastoma stem cells, and a candidate target for molecular diagnosis and therapy of a glioblastoma subset. 37 samples (17 replicate samples and 1 triplicate sample)
2012-10-05 | E-GEOD-36426 | biostudies-arrayexpress
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2014-05-01 | E-GEOD-56699 | biostudies-arrayexpress
Project description:We noticed that a recently identified poor prognosis stem/serrated molecular subtype of colorectal cancer (CRC) is characterized by up-regulation of transcripts known to be also expressed by stromal cells. To better define the origin of such transcripts, we analyzed RNAseq and microarray datasets from CRC mouse xenografts, where human cancer cells are supported by murine stroma. The analysis revealed that mRNA levels of stem/serrated subtype genes are mostly due to stromal expression, even when the stromal fraction is below 5%. Indeed, a classifier based on genes exclusively expressed by cancer-associated fibroblasts was significantly associated, in multiple datasets, to poor prognosis of CRC and to radioresistance of rectal cancer. Tumor Matched with Corresponding PDX
2014-05-01 | E-GEOD-56695 | biostudies-arrayexpress
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