Microarray Type 1 Error
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faq • rss Community Log In Sign Up Add New Post Question: Estimate The Type 2 Error In A Microarray Study 4 5.9 years ago by Julien Textoris • 420 Marseille, France Julien Textoris • 420 wrote: Hi all, to answer reviewing of a clinical how to calculate family wise error rate paper in which we analyzed the whole blood transcriptome in patients with pulmonary infection, and for
Multiple Testing Bonferroni
which the results are negative, the reviewer asked me to estimate the type II error, or power of the anaysis, which is a true question,
Experiment Wise Error Rate
givent the negative results. However, i don't really know how to compute/estimate this. The FDR and correction for multi-testing are an estimate of type I error, but for type II, i don't really know how to do this with multitesting
Multiple Testing Correction
(the analysis was a supervised analysis with SAM algorithm in two groups of patients (one microarray for each patient)). Hope someone could give me some clue ? Thanks in advance Julien microarray error transcriptome • 1.2k views ADD COMMENT • link • Not following Follow via messages Follow via email Do not follow written 5.9 years ago by Julien Textoris • 420 5 5.9 years ago by Daniel Swan ♦ 12k Earlham Institute, Norwich, UK Daniel Swan ♦ 12k wrote: There are how to calculate fdr in r packages to calculate power in R/BioConductor. sizepower springs to mind: "This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice." You should, of course, do this prior to your experiment to know how many samples to use for a desired power, rather than using it as a post-hoc assessment of the work you've done. ADD COMMENT • link modified 5.9 years ago by Michael Dondrup ♦ 39k • written 5.9 years ago by Daniel Swan ♦ 12k 2 +1 for doing power analysis BEFORE the study ... but I find that this is never the case. ADD REPLY • link written 5.9 years ago by Will ♦ 4.3k 1 The sizepower package looks more versatile than SPSA, as it works with many experiment designs whiel SPSA only works with two-sample comparisons. ADD REPLY • link written 5.9 years ago by Michael Dondrup ♦ 39k I think Michael fixed my link - thanks :) ADD REPLY • link written 5.9 years ago by Daniel Swan ♦ 12k Depends which community I'm working with, the medics are used to doing it for studies involving
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to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of https://en.wikipedia.org/wiki/DNA_microarray a genome. Each DNA spot contains picomoles (10−12 moles) of a specific DNA sequence, known as probes (or reporters or oligos). These can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA (also called anti-sense RNA) sample (called target) under high-stringency conditions. Probe-target error rate hybridization is usually detected and quantified by detection of fluorophore-, silver-, or chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target. The original nucleic acid arrays were macro arrays approximately 9 x 12 cm and the first computerized image based analysis was published in 1981[1] Contents 1 Principle 2 Uses how to calculate and types 2.1 Fabrication 2.2 Spotted vs. in situ synthesised arrays 2.3 Two-channel vs. one-channel detection 2.4 A typical protocol 3 Microarrays and bioinformatics 3.1 Experimental design 3.2 Standardization 3.3 Data analysis 3.4 Annotation 3.5 Data warehousing 4 Alternative technologies 5 Multi-stranded DNA Microarray 6 Glossary 7 See also 8 References 9 External links Principle[edit] Hybridization of the target to the probe. Main article: Nucleic acid hybridization For more details on this topic, see DNA microarray experiment. The core principle behind microarrays is hybridization between two DNA strands, the property of complementary nucleic acid sequences to specifically pair with each other by forming hydrogen bonds between complementary nucleotide base pairs. A high number of complementary base pairs in a nucleotide sequence means tighter non-covalent bonding between the two strands. After washing off non-specific bonding sequences, only strongly paired strands will remain hybridized. Fluorescently labeled target sequences that bind to a probe sequence generate a signal that depends on the hybridizati