31 Expression clusters found (0.009 seconds)

WBPaper00041075:hcf-1(-)_skn-1(+)_downregulated

N.A. Reanalyzing previously published data.Authors compared the genes differentially expressed in hcf-1(pk924) mutant worms relative to N2 wild-type worms (referred to as hcf-1(-) profile) (Rizki et al., 2011) to those changed in wild-type worms treated with control RNAi relative to skn-1 RNAi (referred to as skn-1 (+) profile) (Oliveira et al., 2009). Genes that were down regulated in the absence of hcf-1 and the presence of skn-1.

WBPaper00041075:hcf-1(-)_skn-1(+)_upregulated

N.A. Reanalyzing previously published data.Authors compared the genes differentially expressed in hcf-1(pk924) mutant worms relative to N2 wild-type worms (referred to as hcf-1(-) profile) (Rizki et al., 2011) to those changed in wild-type worms treated with control RNAi relative to skn-1 RNAi (referred to as skn-1 (+) profile) (Oliveira et al., 2009). Genes that were up regulated in the absence of hcf-1 and the presence of skn-1.

WBPaper00044005:DAF-16_downregulated

Authors reanalyzed raw genome-wide expression data from five studies (McElwee et al., 2003; McElwee et al., 2004; Murphy et al., 2003; Shaw et al., 2007; Troemeletal.,2006) encompassing 75 genome-wide expression profiles, which authors used to construct 46 explicit contrasts between conditions with differing levels of DAF-16 activity. After complete reprocessing of the raw data (array-specific standardization, normalization, and remapping of probes), a log-fold-change and corresponding standard error were calculated for each transcript on each array (or array pair for single-channel technologies). Together, these were converted into a vote value between 1 (highly likely to be downregulated) and +1 (highly likely to be upregulated). The total voting score for each gene was computed as the sum of voting scores for individual experiments, which is robust in the sense that the influence of any individual experiment is limited to a single full vote. An empirical null distribution based on random permutation was created, and all genes were ranked from consistently upregulated (class I) to consistently downregulated (class II). The area under the null distribution (p value) for each gene that served as the basis for assigning genes to class I or class II at a 5% false discovery rate. Top 50 down regulated genes by DAF-16 based on reanalysis 75 previously published experiments.

WBPaper00044005:DAF-16_upregulated

Authors reanalyzed raw genome-wide expression data from five studies (McElwee et al., 2003; McElwee et al., 2004; Murphy et al., 2003; Shaw et al., 2007; Troemeletal.,2006) encompassing 75 genome-wide expression profiles, which authors used to construct 46 explicit contrasts between conditions with differing levels of DAF-16 activity. After complete reprocessing of the raw data (array-specific standardization, normalization, and remapping of probes), a log-fold-change and corresponding standard error were calculated for each transcript on each array (or array pair for single-channel technologies). Together, these were converted into a vote value between 1 (highly likely to be downregulated) and +1 (highly likely to be upregulated). The total voting score for each gene was computed as the sum of voting scores for individual experiments, which is robust in the sense that the influence of any individual experiment is limited to a single full vote. An empirical null distribution based on random permutation was created, and all genes were ranked from consistently upregulated (class I) to consistently downregulated (class II). The area under the null distribution (p value) for each gene that served as the basis for assigning genes to class I or class II at a 5% false discovery rate. Top 50 up regulated genes by DAF-16 based on reanalysis 75 previously published experiments.

WBPaper00031486:daf-16_downregulated

Raw microarray data (cel files) were normalized, fold-changes between genotypes were determined, and global statistical analysis was performed, using a slightly modified version of the recently described Goldenspike methodology implemented in R (version 2.0.1) (Choe et al. 2005). Briefly, this procedure performs eight different normalization routines, which are then used to produce an average fold-change difference and false-discovery rate (q-value) between different genotypes that takes into consideration the variance of probe set intensity across the different normalizations. The Goldenspike methodology has been shown to out-perform most commonly used normalization methods (Choe et al. 2005). The Goldenspike protocol was altered slightly to exclude absent probe sets (those probe sets called absent in all hybridizations by MAS5) prior to the final probe-set-level Loess normalization. This alteration was found to reduce the number of false positives associated with the absent probe sets (Schuster et al. 2007). Genes showing significantly decreased expression in daf-16(mgDf50);daf-2(e1370) comparing to daf-2(e1370), but not in daf-2(m577).

WBPaper00031486:daf-16_upregulated

WBPaper00036429:glp-1(oz112)_upregulated

The data were analyzed as described previously (Reinke et al., 2004), with a cut-off of > 1.8, p < 0.001 (Student's t test) used to define the 202 genes with enriched expression in the glp-1(oz112) mutant relative to controls. Genes with higher expression in glp-1(oz112) mutants than in wild type animals. (> 1.8-fold, p<0.01; based on four independently prepared sets of samples).

WBPaper00041002:HF_11d_2.0mM_Down

For selection of DEGs, an unpaired t -test was performed followed by a significance analysis of microarray (SAM) test including a calculation that estimates the false discovery rate (FDR). FDR, reducing on the one hand type I errors for null associations, was set to a non-stringent level of <12.5%, mainly to guard from an increase of type II error and also based on findings by Levine et al. (2011), which described 12.5% as most acceptable optimum level of FDR, representing the 90th percentile of the normal distribution curve. DEGs exceeding a fold change of 1.25 were further analyzed with respect to their functional clustering. This fold-cut-off was chosen to allow an interpretation that is biologically meaningful, akin to the notion that data of sound technical and experimental quality which returns strong, statistically significant, absolute signal intensities is sufficiently robust to justify a fold-cut-off of >1.2. This analysis was conducted using the functional annotation clustering tool of the Database for Annotation, Visualization, and Integrated Discovery (DAVID; Huang et al., 2007). Gene significantly down-regulated by treatment with 2.0mM of HuminFeed until older adult stage (11 days), with a minimum fold change in gene expression of 0.8.

WBPaper00041002:HQ_3d_0.2mM_Up

For selection of DEGs, an unpaired t -test was performed followed by a significance analysis of microarray (SAM) test including a calculation that estimates the false discovery rate (FDR). FDR, reducing on the one hand type I errors for null associations, was set to a non-stringent level of <12.5%, mainly to guard from an increase of type II error and also based on findings by Levine et al. (2011), which described 12.5% as most acceptable optimum level of FDR, representing the 90th percentile of the normal distribution curve. DEGs exceeding a fold change of 1.25 were further analyzed with respect to their functional clustering. This fold-cut-off was chosen to allow an interpretation that is biologically meaningful, akin to the notion that data of sound technical and experimental quality which returns strong, statistically significant, absolute signal intensities is sufficiently robust to justify a fold-cut-off of >1.2. This analysis was conducted using the functional annotation clustering tool of the Database for Annotation, Visualization, and Integrated Discovery (DAVID; Huang et al., 2007). Gene significantly up-regulated by treatment with 0.2mM of HuminFeed Hydroquinone until young adult stage (3 days), with a minimum fold change in gene expression of 1.25.

WBPaper00041002:HF_3d_2.0mM_Down

For selection of DEGs, an unpaired t -test was performed followed by a significance analysis of microarray (SAM) test including a calculation that estimates the false discovery rate (FDR). FDR, reducing on the one hand type I errors for null associations, was set to a non-stringent level of <12.5%, mainly to guard from an increase of type II error and also based on findings by Levine et al. (2011), which described 12.5% as most acceptable optimum level of FDR, representing the 90th percentile of the normal distribution curve. DEGs exceeding a fold change of 1.25 were further analyzed with respect to their functional clustering. This fold-cut-off was chosen to allow an interpretation that is biologically meaningful, akin to the notion that data of sound technical and experimental quality which returns strong, statistically significant, absolute signal intensities is sufficiently robust to justify a fold-cut-off of >1.2. This analysis was conducted using the functional annotation clustering tool of the Database for Annotation, Visualization, and Integrated Discovery (DAVID; Huang et al., 2007). Gene significantly down-regulated by treatment with 2.0mM of HuminFeed until young adult stage (3 days), with a minimum fold change in gene expression of 0.8.

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