Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Effects of sample size on differential gene expression, rank order and prediction accuracy of a gene signature

ABSTRACT: Gene expression profiles were generated from muscle biopsies from 134 individuals, and differences in expression based on sex were explored. Top differentially expressed gene lists are often inconsistent between studies and it has been suggested that small sample sizes contribute to lack of reproducibility and poor prediction accuracy in discriminative models. We considered sex differences (69M-bM-^YM-^B, 65M-bM-^YM-^@) in 134 human skeletal muscle biopsies using DNA microarray. The full dataset and subsamples (n= 10 (5M-bM-^YM-^B, 5M-bM-^YM-^@) to n=120 (60M-bM-^YM-^B, 60M-bM-^YM-^@)) thereof were used to assess the effect of sample size on the differential expression of single genes, gene rank order and prediction accuracy. Using our full dataset (n=134), we identified 717 differentially expressed transcripts (p-value < 0.0001; false discovery rate < 0.006) and we were able to predict sex with 92% accuracy, both within our dataset and on external datasets. Both p-values and rank order of top differentially expressed genes became more variable using smaller subsamples. For example, at n=10 (5M-bM-^YM-^B, 5M-bM-^YM-^@), no gene was considered differentially expressed at p<0.0001 and prediction accuracy was ~50% (no better than chance). We found that sample size clearly affects microarray analysis results; small sample sizes result in unstable gene lists and poor prediction accuracy. We anticipate this will apply to other phenotypes, in addition to sex. RNA was isolated from 134 muscle samples. Gene expression is compared between males and females.

ORGANISM(S): Homo sapiens

SUBMITTER: Kathryn Graham

PROVIDER: E-GEOD-41726 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Effects of sample size on differential gene expression, rank order and prediction accuracy of a gene signature.

Stretch Cynthia C Khan Sheehan S Asgarian Nasimeh N Eisner Roman R Vaisipour Saman S Damaraju Sambasivarao S Graham Kathryn K Bathe Oliver F OF Steed Helen H Greiner Russell R Baracos Vickie E VE

PloS one 20130603 6

Top differentially expressed gene lists are often inconsistent between studies and it has been suggested that small sample sizes contribute to lack of reproducibility and poor prediction accuracy in discriminative models. We considered sex differences (69♂, 65 ♀) in 134 human skeletal muscle biopsies using DNA microarray. The full dataset and subsamples (n = 10 (5 ♂, 5 ♀) to n = 120 (60 ♂, 60 ♀)) thereof were used to assess the effect of sample size on the differential expression of single genes ...[more]

PMID: 23755224

Similar Datasets

Project description:The yeast Dekkera bruxellensis is as ethanol tolerant as Saccharomyces cerevisiae and may be found in bottled wine. It causes the spoilage of wine, beer, cider and soft drinks. In wines, the metabolic products responsible for spoilage by Dekkera bruxellensis are mainly volatile phenols. These chemical compounds are responsible for the taints described as M-bM-^@M-^XM-bM-^@M-^XmedicinalM-bM-^@M-^YM-bM-^@M-^Y in white wines (due to vinyl phenols) and as M-bM-^@M-^XM-bM-^@M-^XleatherM-bM-^@M-^YM-bM-^@M-^Y, M-bM-^@M-^XM-bM-^@M-^Xhorse sweatM-bM-^@M-^YM-bM-^@M-^Y and M-bM-^@M-^XM-bM-^@M-^XstableM-bM-^@M-^YM-bM-^@M-^Y in red wines (due to ethyl phenols mainly 4-ethylphenol). Apart from the negative aroma nuances imparted by these yeasts, positive aromas such as M-bM-^@M-^XsmokyM-bM-^@M-^Y, M-bM-^@M-^XspicyM-bM-^@M-^Y and M-bM-^@M-^XtoffeeM-bM-^@M-^Y are also cited. Our goal was to identify the impact that the wine spoilage yeast Dekkera bruxellensis has on fermenting S. cerevisiae cells, especially on its gene expression level. To this end we co-inoculated both yeast species at the start of fermentation in a synthetic wine must, using S. cerevisiae-only fermentations without Dekkera bruxellensis as a control. All fermentations were employed in special membrane reactors (50 KDa pore size cut-off) physically separating Dekkera bruxellensis from wine yeast S. cerevisiae. Biomass separation with this membrane was done to abolish the possibility of hybridizing also D. bruxellensis probes on Agilent V2 (8x15K format) G4813 DNA microarrays designed just for S. cerevisiae ORF targets. The 50 KDa pore membrane separating both yeasts allowed the exchange of ethanol, metabolites and sugars during the fermentation. Fermentations were carried out in synthetic wine must in duplicate for both the control S. cerevisiae (strain Lalvin EC1118) and mixed fermentation. Sampling of yeast S. cerevisiae for RNA extractions were performed at 22 h of fermentation, during the exponential growth phase of S. cerevisiae, at 92 h and 144 h of fermentation, during its early and late stationary growth phase and at 187 h of fermentation, during its phase of growth decline.

Project description:Background: Bacillus methanolicus MGA3 is a thermophilic, facultative ribulose monophosphate (RuMP) cycle methylotroph. Methylotrophy in this organism depends on the endogenous plasmid pBM19 although all ribulose monophosphate cycle enzymes are also encoded on the chromosome. Together with its ability to produce high yields of amino acids, the relevance of this microorganism as a promising and valuable candidate for future biotechnological applications is evident. The B.M-BM- methanolicus MGA3 genome consists of a 3,337,035 nucleotides (nt) circular chromosome, the 19,174M-BM- nt plasmid pBM19 and the 68,999M-BM- nt plasmid pBM69. 3,218 protein-coding regions were annotated on the chromosome, 22 on pBM19 and 82 on pBM69. In the present study, the RNA-seq approach was used to comprehensively investigate the transcriptome of B.M-BM- methanolicus MGA3 in order to improve the genome annotation, identify novel transcripts, analyze conserved sequence motifs involved in gene expression and reveal operon structures. For this aim, two different cDNA library preparation methods were applied: one which allows characterization of the whole transcriptome and another which includes enrichment of primary transcript 5M-bM-^@M-^YM-bM-^@M-^Qends. Results: Analysis of the primary transcriptome data enabled the detection of 2,167 putative transcription start sites (TSSs) which were categorized into 1,642 TSSs located in the upstream region (5M-bM-^@M-^Y UTR) of known protein-coding genes and 525 TSSs of novel antisense, intragenic, or intergenic transcripts. The first step of the analysis was the correction of 14 wrongly annotated translation start sites (TLSs) on the basis of the primary transcriptome data. Further investigation of the identified 5M-bM-^@M-^Y UTRs resulted in the detailed characterization of their length distribution and the detection of 75 hitherto unknown cis regulatory RNA elements. In addition to this, the exact TSSs positions were utilized to define conserved sequence motifs for translation start sites, ribosome binding sites and promoters in B. methanolicus MGA3. Based on the whole transcriptome data set, novel transcripts, operon structures and mRNA abundances were determined. The analysis of the operon structures revealed that almost half of the genes are transcribed monocistronically (940), whereas 1,164 genes are organized in 381 operons. Several of the genes related to methylotrophy had highly abundant transcripts. Conclusion: The extensive insights into the transcriptional landscape of B. methanolicus MGA3, gained in this study, represent a valuable foundation for further comparative quantitative transcriptome analyses and possibly also for the development of molecular biology tools which at present are very limited for this organism. Pooled samples from 16 different cultivation conditions are analysed in whole transcriptome and 5'end enriched transcriptome protocols.

Dataset Information

Effects of sample size on differential gene expression, rank order and prediction accuracy of a gene signature

Publications

Effects of sample size on differential gene expression, rank order and prediction accuracy of a gene signature.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure