Microarray analysis techniques: Difference between revisions

Revision as of 17:59, 11 June 2019

Revision as of 14:32, 11 July 2019

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*[[Permutations]] are calculated based on the number of samples

*Block Permutations

**Blocks are batches of microarrays; for example for eight samples split into two groups (control and affected) there are 4!=24 permutations for each block and the total number of permutations is (24)(24)= 576. A minimum of 1000 permutations are recommended;<ref name="R1"/><ref name="R2">{{cite journal | last1 = Dinu | first1 = I. P. | last2 = JD | first2 = | last3 = Mueller | first3 = T | last4 = Liu | first4 = Q | last5 = Adewale | first5 = AJ | last6 = Jhangri | first6 = GS | last7 = Einecke | first7 = G | last8 = Famulski | first8 = KS | last9 = Halloran | first9 = P | last10 = Yasui | first10 = Y. | year = 2007 | title = Improving gene set analysis of microarray data by SAM-GS. | url = | journal = BMC Bioinformatics | volume = 8 | issue = | page = 242 | doi=10.1186/1471-2105-8-242| pmid = 17612399 | pmc = 1931607 }}</ref><ref name="R3">{{cite journal | last1 = Jeffery | first1 = I. H. | last2 = DG | first2 = | last3 = Culhane | first3 = AC. | year = 2006 | title = Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data | url = | journal = BMC Bioinformatics | volume = 7 | issue = | page = 359 | doi=10.1186/1471-2105-7-359| pmid = 16872483 | pmc = 1544358 }}</ref>

the number of permutations is set by the user when imputing correct values for the data set to run SAM

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*Correlates expression data to clinical parameters<ref name="R6"/>

*Correlates expression data with time<ref name="R1"/>

*Uses data permutation to estimates False Discovery Rate for multiple testing<ref name="R7"/><ref name="R8"/><ref name="R6"/><ref name="R5">{{cite journal | last1 = Larsson | first1 = O. W. C | last2 = Timmons | first2 = JA. | year = 2005 | title = Considerations when using the significance analysis of microarrays (SAM) algorithm | url = | journal = BMC Bioinformatics | volume = 6 | issue = | page = 129 | doi = 10.1186/1471-2105-6-129 | pmid = 15921534 | pmc = 1173086 }}</ref>

*Reports local false discovery rate (the FDR for genes having a similar d_i as that gene)<ref name="R1"/> and miss rates <ref name="R1"/><ref name="R7"/>

*Can work with blocked design for when treatments are applied within different batches of arrays<ref name="R1"/>

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==References==

{{reflist|refs=

<ref name="R6">{{cite journal | last1 = Tusher | first1 = V. G. | last2 = Tibshirani | first2 = R. | display-authors =et al | year = 2001 | title = Significance analysis of microarrays applied to the ionizing radiation response | url = http://www-stat.stanford.edu/~tibs/SAM/pnassam.pdf | format = PDF | journal = Proceedings of the National Academy of Sciences | volume = 98 | issue = 9| pages = 5116–5121 | doi=10.1073/pnas.091062498| pmid = 11309499 | pmc = 33173 }}</ref>

<ref name="R7">{{cite journal | last1 = Zang | first1 = S. | last2 = Guo | first2 = R. | display-authors = et al | year = 2007 | title = Integration of statistical inference methods and a novel control measure to improve sensitivity and specificity of data analysis in expression profiling studies | url = | journal = Journal of Biomedical Informatics | volume = 40 | issue = 5| pages = 552–560 | doi=10.1016/j.jbi.2007.01.002| pmid = 17317331 }}</ref>

}}