Non-Biological Variability Study
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus Homo sapiens
PROVIDER: GSE26008 | GEO | 2010/12/11
SECONDARY ACCESSION(S): PRJNA135583
REPOSITORIES: GEO
Similar Datasets
Project description:Assessment of sources of non-biological (technical) variability in the processing of RNA samples and gene chips during microarray analysis. The purpose of this experiment was to measure technical variability in microarray data introduced by various steps in preparation of the RNA, amplification/labeling of the RNA and hybridization of the sample to the arrays on separate days. The experimental design utilized a single tissue source - homogenized brain tissue pooled from 3 eight week old male C57/Bl mice. Variables examined include: I) Technician extracting RNA (technician #1 prepared Pools 1 and 2; technician #2 prepared pools 3 and 4). II) RNA extraction (Pools 1-4 represent four independent RNA extractions from 3 homogenized pooled whole mouse brains from 8 week old male C57/B6 mice) III) Independent RNA labeling/amplification reactions - three performed from each RNA pool labeled A, B and C) and IV) Day of hybridization to GLYCOv1 arrays (labeled samples were hybridized to arrays over 3 days - 11/14/02, 11/15/02 and 11/18/02). 32 samples made it through to completion generating a high quality .CEL file for analysis. Four samples failed at various steps in the preparation/hybridiztion. All samples were male 8 week old C57/Bl mouse brain total RNA.
2010-12-11 | E-GEOD-26008 | biostudies-arrayexpress
Project description:Survey of gene expression in 9 types of C57/Bl mouse wild type tissue. RNA was extracted from homogenized whole organs/tissues and include brain, spleen, liver, thymus, kidney, lung, testes, bone marrow, and lymph node. 8 week old male littermate C57/Bl mice
2011-02-17 | E-GEOD-27346 | biostudies-arrayexpress
Project description:RNA-seq: technical variability and sampling
| PRJNA234515 | ENA
Project description:DNA methylation microarrays have become a widely used tool for investigating epigenetic modifications in various aspects of biomedical research. However, technical variability in methylation data poses challenges for downstream applications such as predictive modeling of health and disease. In this study, we measure the impact of common sources of technical variability in Illumina DNA methylation microarray data, with a specific focus on positional biases inherent within the microarray technology. By utilizing a dataset comprised of multiple, highly similar technical replicates, we identified a chamber number bias, with different chambers of the microarray exhibiting systematic differences in fluorescence intensities and their derived methylation beta values, which are only partially corrected for by existing preprocessing methods, and demonstrate that this positional bias can lead to false positive results during differential methylation testing. Additionally, our investigation identified outliers in low-level fluorescence data which might play a role in contributing to predictive error in computational models of health-relevant traits such as age.
2023-12-20 | GSE250556 | GEO
Project description:We extracted RNA from C57BL/6 mouse neutrophils to measure inter-individual variability in gene expression. 5 of the 10 blood samples were analyzed separately, the other 5 were pooled then split into 5 technical replicates to measure technical variability. We could then measure inter-individual variability in gene expression and compare it to the amplitude of miRNA-guided repression by miR-223.
2016-09-21 | GSE59549 | GEO
Project description:In this experiment, there are replicate measurements of gene expression in a group of similarly treated animals measured in such a way as to provide data for comparison of technical variation with biological variability. The data are twelve microarray measurements on each of six rats that were subject to the same control-group treatment. The twelve measurements encompass triplicate measures of RNA from the liver, from the kidney, and from two mixtures of both RNAs.
2009-09-22 | E-TABM-555 | biostudies-arrayexpress
Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
2014-01-15 | GSE54068 | GEO
Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
2014-01-15 | GSE54067 | GEO
Project description:In this experiment, there are replicate measurements of gene expression in a group of similarly treated animals measured in such a way as to provide data for comparison of technical variation with biological variability. The data are twelve microarray measurements on each of six rats that were subject to the same control-group treatment. The twelve measurements encompass triplicate measures of RNA from the liver, from the kidney, and from two mixtures of both RNAs.
2008-08-12 | E-TABM-536 | biostudies-arrayexpress
Project description:In this experiment, there are replicate measurements of gene expression in a group of similarly treated animals measured in such a way as to provide data for comparison of technical variation with biological variability. The data are twelve microarray measurements on each of six rats that were subject to the same control-group treatment. The twelve measurements encompass triplicate measures of RNA from the liver, from the kidney, and from two mixtures of both RNAs. IMPORTANT: 6C-1 and 1-6B1 are outlier because of poor cRNA yield. 4A-1 (low cRNA yield too) and 4D-1 seems to have been switched during processing. 2D1 and 5A3 were also mistake (samples picked from the wrong well in the original 96 plates) as they seems to cluster with B samples (75% liver-25% Kidney).
2008-09-18 | E-TABM-554 | biostudies-arrayexpress