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Characterization of technical variation in ChIP-on-chip studies

ABSTRACT: Analysis of technical variance of ChIP-on-Chip studies by characterization of Myc-binding in HL60 cells. Keywords: Chip-on-chip

ORGANISM(S): Homo sapiens

PROVIDER: GSE8448 | GEO | 2008/11/01

SECONDARY ACCESSION(S): PRJNA101523

REPOSITORIES: GEO

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Characterization of technical variation in ChIP-on-chip studies

Project description:Analysis of technical variance of ChIP-on-Chip studies by characterization of Myc-binding in HL60 cells. Keywords: Chip-on-chip Fully-blocked study of technical variance in Myc-binding in HL60 cells. Two different antibodies were used to generate 6 biologically independent replicates each. Each replicate was hybridized to arrays from two different batches in both dye-swap orientations, leading to 48 total arrays.

2008-10-31 | E-GEOD-8448 | biostudies-arrayexpress

Genome-Wide profile of c-Myc binding in HL60 cells

Project description:Analysis of technical variance of ChIP-on-Chip studies by characterization of Myc-binding in HL60 cells. Keywords: ChIP, c-Myc

2008-11-01 | GSE11245 | GEO

c-Myc binding in HL60 cells

Project description:Analysis of technical variance of ChIP-on-Chip studies by characterization of Myc-binding in HL60 cells. Keywords: Chip-on-chip

2008-11-01 | GSE8447 | GEO

c-Myc binding in HL60 cells

Project description:Analysis of technical variance of ChIP-on-Chip studies by characterization of Myc-binding in HL60 cells. Keywords: Chip-on-chip Characterization of c-Myc binding in HL60 cells. Thirteen biologically independent replicates of growing HL60 cells were subjected to ChIP with an N-terminal c-Myc antibody and hybridized to CpG island microarrays

2010-05-17 | E-GEOD-8447 | biostudies-arrayexpress

On the relevance of technical variation due to building pools in microarray experiments [Mouse]

Project description:The objective of the study was to assess the technical error due to blending of individual samples into pools in experimental data. The blending error variance component corresponds to random effects for inaccuracies, which were modeled on the logarithmic scale of normalized gene expression. It's estimation based on a linear mixed model, fitted for each transcript.

2015-09-10 | GSE72861 | GEO

BEARscc: robustness of single-cell clusters determined using simulated technical replicates

Project description:Technical variance is a major confounding factor in single-cell RNA sequencing, not least because measurements on the same cell are not replicable. We developed BEARscc, a tool that simulates experiment-specific technical replicates based on a probabilistic model of technical variance trained on RNA spike-in measurements. We demonstrate that the tool improves the unsupervised classification of cells and aids the interpretation of single-cell RNA-seq experiments.

2018-04-03 | GSE95155 | GEO

On the relevance of technical variation due to building pools in microarray experiments [Honey bee]

Project description:The objective of the study was to assess the technical error due to blending of individual samples into pools in different experimental data sets. The blending error variance component corresponds to random effects for inaccuracies, which were modeled on the logarithmic scale of normalized gene expression. It's estimation based on a linear mixed model, fitted for each transcript.

2015-09-11 | GSE72943 | GEO

Sox2 ChIP-chip on human neural stem cell line REN cells

Project description:Analysis of technical variance of ChIP-on-Chip studies by characterization of SOX2 in neural stem cell. Keywords: ChIP, SOX2

2012-08-30 | GSE34395 | GEO

Technical controls MiCA

Project description:Technical controls MiCA

| PRJEB30293 | ENA

RNA-seq: technical variability and sampling (male)

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

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