Preferred analysis methods for Affymetrix GeneChips. II. An expanded, balanced, wholly-defined spike-in dataset
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ABSTRACT:
ORGANISM(S): Drosophila melanogaster
PROVIDER: GSE21344 | GEO | 2010/04/15
SECONDARY ACCESSION(S): PRJNA126141
REPOSITORIES: GEO
Similar Datasets
Project description:We present a new wholly defined Affymetrix spike-in dataset consisting of 18 microarrays. Over 5700 RNAs are spiked in at relative concentrations ranging from 1- to 4-fold, and the arrays from each condition are balanced with respect to both total RNA amount and degree of positive- versus negative-fold change. We use this new â??Platinum Spikeâ?? dataset to evaluate microarray analysis routes and contrast the results to those achieved using our earlier Golden Spike dataset. PCR products from 5725 Drosophila Gene Collection release 1.0 (DGCr1) cDNA clones were collected into 28 distinct pools, and three independent in vitro transcription and labeling reactions were performed for each pool. Labeled cRNAs from each individual pool were then added at specified amounts to samples A and B to achieve the desired fold change differences between samples. 24 cRNAs generated from DGCr2 cDNA clones were added to each sample in known concentrations before hybridization.
2010-04-28 | E-GEOD-21344 | biostudies-arrayexpress
Project description:We have generated a wholly defined spike-in dataset for Agilent microarrays consisting of 12 arrays with more than 2000 differentially expressed, and approximately 3600 background, cRNAs. The composition of this “Ag Spike”dataset is identical to that of our previous Platinum Spike dataset (GSE21344) and therefore allows direct cross-platform comparison. Comparison between the Ag Spike and Platinum Spike studies shows high agreement between results obtained using the Affymetrix and Agilent platforms.
2010-10-23 | GSE24866 | GEO
Project description:We have generated a wholly defined spike-in dataset for Agilent microarrays consisting of 12 arrays with more than 2000 differentially expressed, and approximately 3600 background, cRNAs. The composition of this âAg Spikeâdataset is identical to that of our previous Platinum Spike dataset (GSE21344) and therefore allows direct cross-platform comparison. Comparison between the Ag Spike and Platinum Spike studies shows high agreement between results obtained using the Affymetrix and Agilent platforms. We used the same 28 PCR pools that had been used before to generate the Platinum-Spike dataset (GSE21344). These pools contain PCR products from 5725 Drosophila Gene Collection release 1.0 (DGCr1) cDNA clones. We mixed PCR products from all pools together with specified relative abundance to generate two samples representing the A and B conditions. Both samples were in vitro transcribed and then labeled with Cy3 and Cy5 fluorescent dye. Labeled A samples were hybridized with B samples labeled with reverse dye to three Agilent Drosophila Gene Expression microarrays. The process from in vitro transcription to hybridization was repeated twice. At the end we generated 12 arrays that were randomly placed in three independent slides to remove potential systematic error due to array position.
2010-10-23 | E-GEOD-24866 | biostudies-arrayexpress
Project description:Due to the lack of controlled microarray experimental data, evaluation of gene identification methods has been performed using simulated data. As the ability of these approaches to mimic actual experimental data is questionable, conclusions drawn can be misleading. We applied spike-in approaches to develop experimental benchmark data and used it to evaluate various gene identification methods. mRNA that correspond to specific spots on a microarray were obtained by in-vitro transcription of selected clones. They were then spiked in at 11 varying concentrations to a common pool of mRNA, creating a set of known differentially expressed genes. mRNA was obtained from mouse ATCC CRL1606 hybridoma cells. From self hybridization results 200 genes that consistently exhibited a log ratio value close to 0 were randomly selected across a range of intensity. In-vitro transcripts of these 200 genes were obtained from their respective clones. From RNA gel electrophoresis and test array results, 169 of the 200 transcripts were deemed successful. Experiments were then conducted by spiking these 169 transcripts to the common pool of mRNA extracted from the hybridoma cells. With exception of the spiked in genes, the experimental set-up is identical to a self hybridization. This experiment is thus different from other microarray experiments as the set of differentially expressed genes is known as they have been artificially created. Spiked in genes were introduced at 11 concentrations: 0pmol (self hybridization), 0.025pmol, 0.05pmol, 0.10pmol, 0.15pmol, 0.20pmol, 0.25pmol, 0.5pmol, 0.75pmol 1pmol and 2pmol. For concentrations 0pmol, 0.05pmol, 0.10pmol, 0.25pmol, 0.50pmol and 0.75pmol, 6 replicates were obtained. For concentrations 0.025pmol, 0.15pmol, 0.20pmol, 1pmol and 2pmol, 3 replicates were obtained. Dye swaps were not performed for these experiments. Keywords: other
2005-05-03 | GSE2548 | GEO
Project description:A wholly-defined Agilent microarray spike-in dataset
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Project description:Due to the lack of controlled microarray experimental data, evaluation of gene identification methods has been performed using simulated data. As the ability of these approaches to mimic actual experimental data is questionable, conclusions drawn can be misleading. We applied spike-in approaches to develop experimental benchmark data and used it to evaluate various gene identification methods. mRNA that correspond to specific spots on a microarray were obtained by in-vitro transcription of selected clones. They were then spiked in at 11 varying concentrations to a common pool of mRNA, creating a set of known differentially expressed genes. mRNA was obtained from mouse ATCC CRL1606 hybridoma cells. From self hybridization results 200 genes that consistently exhibited a log ratio value close to 0 were randomly selected across a range of intensity. In-vitro transcripts of these 200 genes were obtained from their respective clones. From RNA gel electrophoresis and test array results, 169 of the 200 transcripts were deemed successful. Experiments were then conducted by spiking these 169 transcripts to the common pool of mRNA extracted from the hybridoma cells. With exception of the spiked in genes, the experimental set-up is identical to a self hybridization. This experiment is thus different from other microarray experiments as the set of differentially expressed genes is known as they have been artificially created. Spiked in genes were introduced at 11 concentrations: 0pmol (self hybridization), 0.025pmol, 0.05pmol, 0.10pmol, 0.15pmol, 0.20pmol, 0.25pmol, 0.5pmol, 0.75pmol 1pmol and 2pmol. For concentrations 0pmol, 0.05pmol, 0.10pmol, 0.25pmol, 0.50pmol and 0.75pmol, 6 replicates were obtained. For concentrations 0.025pmol, 0.15pmol, 0.20pmol, 1pmol and 2pmol, 3 replicates were obtained. Dye swaps were not performed for these experiments.
2010-06-10 | E-GEOD-2548 | biostudies-arrayexpress
Project description:Concomitant with the rise in the popularity of DNA microarrays has been a surge of proposed methods for the analysis of microarray data. Fully controlled "spike-in" datasets are an invaluable but rare tool for assessing the performance of various methods.We generated a new wholly defined Affymetrix spike-in dataset consisting of 18 microarrays. Over 5700 RNAs are spiked in at relative concentrations ranging from 1- to 4-fold, and the arrays from each condition are balanced with respect to both total RNA amount and degree of positive versus negative fold change. We use this new "Platinum Spike" dataset to evaluate microarray analysis routes and contrast the results to those achieved using our earlier Golden Spike dataset.We present updated best-route methods for Affymetrix GeneChip analysis and demonstrate that the degree of "imbalance" in gene expression has a significant effect on the performance of these methods.
| S-EPMC2897828 | biostudies-literature
Project description:We have conducted a study to determine which DNA microarray analysis method for identification of differential expression works best with a varying number of replicates, in particular, when the number of replicates is small. Using spiked-in mRNA with two-channel microarrays, as well as simulated data generated /in silico/, we measured the efficacy of five gene identification methods, namely /t/-test, Significance Analysis of Microarrays (SAM), median log ratio, median fold change and Wilcoxon ranksum test. By comparing the receiver operating characteristic (ROC) curves as well as the ranking accuracy versus sample size plots, our results show that the simple median methods (median log ratio and median fold change) are more accurate and robust than the other statistical methods in identifying differential gene expression, especially when the number of sample replicates is small. Keywords: Spike-in
2007-08-03 | GSE8655 | GEO
Project description:Due to the lack of controlled microarray experimental data, evaluation of gene identification methods has been performed using simulated/manipulated data. As the ability of these approaches to mimic actual experimental data is questionable, conclusions drawn can be misleading. We applied spike-in approaches to develop experimental benchmark data and used it to evaluate various gene identification methods. <br> <br> mRNA was obtained from mouse ATCC CRL1606 hybridoma cells. From self hybridization results 200 genes that consistently exhibited a log ratio value close to 0 were randomly selected across a range of intensity. In-vitro transcripts of these 200 genes were obtained from their respective clones. From RNA gel electrophoresis and test array results, 169 of the 200 transcripts were deemed successful. Experiments were then conducted by spiking these 169 transcripts to the common pool of mRNA extracted from the hybridoma cells. With exception of the spiked in genes, the experimental set-up is identical to a self hybridization. This experiment is thus different from other microarray experiments as the set of differentially expressed genes is known as they have been artificially created. <br> <br> <br>
2005-03-17 | E-MEXP-280 | biostudies-arrayexpress
Project description:To unbiasedly evaluate the quantitative performance of different quantitative methods, and compare different popular proteomics data processing workflows, we prepared a benchmark dataset where the various levels of spikeed-in E. Coli proteome that true fold change (i.e. 1 fold, 1.5 fold, 2 fold, 2.5 fold and 3 fold) and true identities of positives/negatives (i.e. E.Coli proteins are true positives while Human proteins are true negatives) are known. To best mimic the proteomics application in comparison of multiple replicates, each fold change group contains 4 replicates, so there are 20 LC-MS/MS analysis in this benchmark dataset. To our knowledge, this spike-in benchmark dataset is largest-scale ever that encompasses 5 different spike level, >500 true positive proteins, and >3000 true negative proteins (2peptide criteria, 1% protein FDR), with a wide concentration dynamic range. The dataset is ideal to test quantitative accuracy, precision, false-positive biomarker discovery and missing data level.
2018-05-16 | PXD003881 | Pride