Microarray analysis techniques - Revision history

GreenC bot: Rescued 1 archive link. Wayback Medic 2.5 per WP:URLREQ#fda.gov

2025-05-29T21:31:04Z

Rescued 1 archive link. Wayback Medic 2.5 per WP:URLREQ#fda.gov

← Previous revision		Revision as of 21:31, 29 May 2025
Line 3:		Line 3:

	==Introduction==		==Introduction==
	Microarray data analysis is the final step in reading and processing data produced by a microarray chip. Samples undergo various processes including purification and scanning using the microchip, which then produces a large amount of data that requires processing via computer software. It involves several distinct steps, as outlined in the image below. Changing any one of the steps will change the outcome of the analysis, so the MAQC Project<ref>{{cite web \| url = https://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ \| title = MicroArray Quality Control (MAQC) Project \| access-date = 2007-12-26 \| author = Dr. Leming Shi, National Center for Toxicological Research \| publisher = U.S. Food and Drug Administration }}</ref> was created to identify a set of standard strategies. Companies exist that use the MAQC protocols to perform a complete analysis.<ref>{{cite web \|url=http://www.genusbiosystems.com/services-data.shtml \|title=GenUs BioSystems - Services - Data Analysis \|access-date=2008-01-02 }}</ref>		Microarray data analysis is the final step in reading and processing data produced by a microarray chip. Samples undergo various processes including purification and scanning using the microchip, which then produces a large amount of data that requires processing via computer software. It involves several distinct steps, as outlined in the image below. Changing any one of the steps will change the outcome of the analysis, so the MAQC Project<ref>{{cite web \| url = https://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ \| archive-url = https://web.archive.org/web/20051208055601/http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ \| url-status = dead \| archive-date = December 8, 2005 \| title = MicroArray Quality Control (MAQC) Project \| access-date = 2007-12-26 \| author = Dr. Leming Shi, National Center for Toxicological Research \| publisher = U.S. Food and Drug Administration }}</ref> was created to identify a set of standard strategies. Companies exist that use the MAQC protocols to perform a complete analysis.<ref>{{cite web \|url=http://www.genusbiosystems.com/services-data.shtml \|title=GenUs BioSystems - Services - Data Analysis \|access-date=2008-01-02 }}</ref>

	[[File:Microarray exp horizontal.svg\|thumb\|800px\|none\|The steps required in a microarray experiment]]		[[File:Microarray exp horizontal.svg\|thumb\|800px\|none\|The steps required in a microarray experiment]]

81.191.81.174: /* Aggregation and normalization */

2024-06-07T08:05:35Z

Aggregation and normalization

← Previous revision		Revision as of 08:05, 7 June 2024
Line 14:		Line 14:
	Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web \|url=http://bioinf.wehi.edu.au/limma/ \|title=LIMMA Library: Linear Models for Microarray Data \|access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R and MATLAB.<ref>{{Cite journal \|last1=Gatto \|first1=Laurent \|last2=Breckels \|first2=Lisa M. \|last3=Naake \|first3=Thomas \|last4=Gibb \|first4=Sebastian \|date=2015 \|title=Visualization of proteomics data using R and Bioconductor \|journal=Proteomics \|language=en \|volume=15 \|issue=8 \|pages=1375–1389 \|doi=10.1002/pmic.201400392 \|issn=1615-9853 \|pmc=4510819 \|pmid=25690415}}</ref><ref>{{Cite web \|title=Create intensity versus ratio scatter plot of microarray data - MATLAB mairplot \|url=https://www.mathworks.com/help/bioinfo/ref/mairplot.html#responsive_offcanvas \|access-date=2023-11-24 \|website=MathWorks}}</ref>		Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web \|url=http://bioinf.wehi.edu.au/limma/ \|title=LIMMA Library: Linear Models for Microarray Data \|access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R and MATLAB.<ref>{{Cite journal \|last1=Gatto \|first1=Laurent \|last2=Breckels \|first2=Lisa M. \|last3=Naake \|first3=Thomas \|last4=Gibb \|first4=Sebastian \|date=2015 \|title=Visualization of proteomics data using R and Bioconductor \|journal=Proteomics \|language=en \|volume=15 \|issue=8 \|pages=1375–1389 \|doi=10.1002/pmic.201400392 \|issn=1615-9853 \|pmc=4510819 \|pmid=25690415}}</ref><ref>{{Cite web \|title=Create intensity versus ratio scatter plot of microarray data - MATLAB mairplot \|url=https://www.mathworks.com/help/bioinfo/ref/mairplot.html#responsive_offcanvas \|access-date=2023-11-24 \|website=MathWorks}}</ref>

	Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA)<ref>{{cite journal\|last1=Irizarry\|first1=RA\|author-link1=Rafael Irizarry (scientist) \|author2=Hobbs, B \|author3=Collin, F \|author4=Beazer-Barclay, YD \|author5=Antonellis, KJ \|author6=Scherf, U \|author7= Speed, TP \|title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.\|journal=Biostatistics\|volume=4\|issue=2\|pages=249–64\|year=2003\|pmid=12925520 \|doi=10.1093/biostatistics/4.2.249\|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal \|vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP \|title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias \|journal=Bioinformatics \|volume=19 \|issue=2 \|pages=185–93 \|year=2003 \|pmid=12538238 \|doi=10.1093/bioinformatics/19.2.185\|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal \|vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B \|title=Algorithm-driven Artifacts in median polish summarization of Microarray data \|journal=BMC Bioinformatics \|volume=11 \|pages=553 \|year=2010 \|pmid=21070630 \|doi=10.1186/1471-2105-11-553 \|pmc=2998528 \|doi-access=free }}</ref> Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.		Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA)<ref>{{cite journal\|last1=Irizarry\|first1=RA\|author-link1=Rafael Irizarry (scientist) \|author2=Hobbs, B \|author3=Collin, F \|author4=Beazer-Barclay, YD \|author5=Antonellis, KJ \|author6=Scherf, U \|author7= Speed, TP \|title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.\|journal=Biostatistics\|volume=4\|issue=2\|pages=249–64\|year=2003\|pmid=12925520 \|doi=10.1093/biostatistics/4.2.249\|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal \|vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP \|title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias \|journal=Bioinformatics \|volume=19 \|issue=2 \|pages=185–93 \|year=2003 \|pmid=12538238 \|doi=10.1093/bioinformatics/19.2.185\|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal \|vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B \|title=Algorithm-driven Artifacts in median polish summarization of Microarray data \|journal=BMC Bioinformatics \|volume=11 \|pages=553 \|year=2010 \|pmid=21070630 \|doi=10.1186/1471-2105-11-553 \|pmc=2998528 \|doi-access=free }}</ref> [[Quantile normalization]], also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.

	The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal \|vauthors=Lim WK, Wang K, Lefebvre C, Califano A \|title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks \|journal=Bioinformatics \|volume=23 \|issue=13 \|pages=i282–8 \|year=2007 \|pmid=17646307 \|doi=10.1093/bioinformatics/btm201\|doi-access=free }}</ref>		The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal \|vauthors=Lim WK, Wang K, Lefebvre C, Califano A \|title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks \|journal=Bioinformatics \|volume=23 \|issue=13 \|pages=i282–8 \|year=2007 \|pmid=17646307 \|doi=10.1093/bioinformatics/btm201\|doi-access=free }}</ref>

Citation bot: Alter: journal. Add: authors 1-1. Removed proxy/dead URL that duplicated identifier. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | Suggested by Headbomb | Linked from Wikipedia:WikiProject_Academic_Journals/Journals_cited_by_Wikipedia/Sandbox2 | #UCB_webform_linked 308/518

2023-12-05T23:09:43Z

Alter: journal. Add: authors 1-1. Removed proxy/dead URL that duplicated identifier. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | Suggested by Headbomb | Linked from Wikipedia:WikiProject_Academic_Journals/Journals_cited_by_Wikipedia/Sandbox2 | #UCB_webform_linked 308/518

← Previous revision		Revision as of 23:09, 5 December 2023
Line 12:		Line 12:

	===Aggregation and normalization===		===Aggregation and normalization===
	Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web \|url=http://bioinf.wehi.edu.au/limma/ \|title=LIMMA Library: Linear Models for Microarray Data \|access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R and MATLAB.<ref>{{Cite journal \|~~last~~=Gatto \|~~first~~=Laurent \|last2=Breckels \|first2=Lisa M. \|last3=Naake \|first3=Thomas \|last4=Gibb \|first4=Sebastian \|date=2015 \|title=Visualization of proteomics data using R and Bioconductor ~~\|url=https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.201400392~~ \|journal=~~PROTEOMICS~~ \|language=en \|volume=15 \|issue=8 \|pages=1375–1389 \|doi=10.1002/pmic.201400392 \|issn=1615-9853 \|pmc=4510819 \|pmid=25690415}}</ref><ref>{{Cite web \|title=Create intensity versus ratio scatter plot of microarray data - MATLAB mairplot \|url=https://www.mathworks.com/help/bioinfo/ref/mairplot.html#responsive_offcanvas \|access-date=2023-11-24 \|website=MathWorks}}</ref>		Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web \|url=http://bioinf.wehi.edu.au/limma/ \|title=LIMMA Library: Linear Models for Microarray Data \|access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R and MATLAB.<ref>{{Cite journal \|last1=Gatto \|first1=Laurent \|last2=Breckels \|first2=Lisa M. \|last3=Naake \|first3=Thomas \|last4=Gibb \|first4=Sebastian \|date=2015 \|title=Visualization of proteomics data using R and Bioconductor \|journal=Proteomics \|language=en \|volume=15 \|issue=8 \|pages=1375–1389 \|doi=10.1002/pmic.201400392 \|issn=1615-9853 \|pmc=4510819 \|pmid=25690415}}</ref><ref>{{Cite web \|title=Create intensity versus ratio scatter plot of microarray data - MATLAB mairplot \|url=https://www.mathworks.com/help/bioinfo/ref/mairplot.html#responsive_offcanvas \|access-date=2023-11-24 \|website=MathWorks}}</ref>

	Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA)<ref>{{cite journal\|last1=Irizarry\|first1=RA\|author-link1=Rafael Irizarry (scientist) \|author2=Hobbs, B \|author3=Collin, F \|author4=Beazer-Barclay, YD \|author5=Antonellis, KJ \|author6=Scherf, U \|author7= Speed, TP \|title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.\|journal=Biostatistics\|volume=4\|issue=2\|pages=249–64\|year=2003\|pmid=12925520 \|doi=10.1093/biostatistics/4.2.249\|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal \|vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP \|title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias \|journal=Bioinformatics \|volume=19 \|issue=2 \|pages=185–93 \|year=2003 \|pmid=12538238 \|doi=10.1093/bioinformatics/19.2.185\|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal \|vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B \|title=Algorithm-driven Artifacts in median polish summarization of Microarray data \|journal=BMC Bioinformatics \|volume=11 \|pages=553 \|year=2010 \|pmid=21070630 \|doi=10.1186/1471-2105-11-553 \|pmc=2998528 \|doi-access=free }}</ref> Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.		Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA)<ref>{{cite journal\|last1=Irizarry\|first1=RA\|author-link1=Rafael Irizarry (scientist) \|author2=Hobbs, B \|author3=Collin, F \|author4=Beazer-Barclay, YD \|author5=Antonellis, KJ \|author6=Scherf, U \|author7= Speed, TP \|title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.\|journal=Biostatistics\|volume=4\|issue=2\|pages=249–64\|year=2003\|pmid=12925520 \|doi=10.1093/biostatistics/4.2.249\|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal \|vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP \|title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias \|journal=Bioinformatics \|volume=19 \|issue=2 \|pages=185–93 \|year=2003 \|pmid=12538238 \|doi=10.1093/bioinformatics/19.2.185\|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal \|vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B \|title=Algorithm-driven Artifacts in median polish summarization of Microarray data \|journal=BMC Bioinformatics \|volume=11 \|pages=553 \|year=2010 \|pmid=21070630 \|doi=10.1186/1471-2105-11-553 \|pmc=2998528 \|doi-access=free }}</ref> Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.

CambrianCrab: ce and filled in missing refs

2023-11-24T19:06:54Z

ce and filled in missing refs

Show changes

81.191.81.174 at 19:49, 30 September 2023

2023-09-30T19:49:03Z

← Previous revision		Revision as of 19:49, 30 September 2023
Line 14:		Line 14:
	Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web \|url=http://bioinf.wehi.edu.au/limma/ \|title=LIMMA Library: Linear Models for Microarray Data \|access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R, MATLAB, and Excel.{{cn\|date=March 2023}}		Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web \|url=http://bioinf.wehi.edu.au/limma/ \|title=LIMMA Library: Linear Models for Microarray Data \|access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R, MATLAB, and Excel.{{cn\|date=March 2023}}

	Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA) <ref>{{cite journal\|~~last~~=Irizarry\|~~first~~=RA\|author2=Hobbs, B \|author3=Collin, F \|author4=Beazer-Barclay, YD \|author5=Antonellis, KJ \|author6=Scherf, U \|author7= Speed, TP \|title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.\|journal=Biostatistics\|volume=4\|issue=2\|pages=249–64\|year=2003\|pmid=12925520 \|doi=10.1093/biostatistics/4.2.249\|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots, but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal \|vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP \|title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias \|journal=Bioinformatics \|volume=19 \|issue=2 \|pages=185–93 \|year=2003 \|pmid=12538238 \|doi=10.1093/bioinformatics/19.2.185\|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal \|vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B \|title=Algorithm-driven Artifacts in median polish summarization of Microarray data \|journal=BMC Bioinformatics \|volume=11 \|pages=553 \|year=2010 \|pmid=21070630 \|doi=10.1186/1471-2105-11-553 \|pmc=2998528 \|doi-access=free }}</ref> Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.		Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA) <ref>{{cite journal\|last1=Irizarry\|first1=RA\|author-link1=Rafael Irizarry (scientist) \|author2=Hobbs, B \|author3=Collin, F \|author4=Beazer-Barclay, YD \|author5=Antonellis, KJ \|author6=Scherf, U \|author7= Speed, TP \|title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.\|journal=Biostatistics\|volume=4\|issue=2\|pages=249–64\|year=2003\|pmid=12925520 \|doi=10.1093/biostatistics/4.2.249\|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots, but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal \|vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP \|title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias \|journal=Bioinformatics \|volume=19 \|issue=2 \|pages=185–93 \|year=2003 \|pmid=12538238 \|doi=10.1093/bioinformatics/19.2.185\|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal \|vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B \|title=Algorithm-driven Artifacts in median polish summarization of Microarray data \|journal=BMC Bioinformatics \|volume=11 \|pages=553 \|year=2010 \|pmid=21070630 \|doi=10.1186/1471-2105-11-553 \|pmc=2998528 \|doi-access=free }}</ref> Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.

	The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal \|vauthors=Lim WK, Wang K, Lefebvre C, Califano A \|title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks \|journal=Bioinformatics \|volume=23 \|issue=13 \|pages=i282–8 \|year=2007 \|pmid=17646307 \|doi=10.1093/bioinformatics/btm201\|doi-access=free }}</ref>		The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal \|vauthors=Lim WK, Wang K, Lefebvre C, Califano A \|title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks \|journal=Bioinformatics \|volume=23 \|issue=13 \|pages=i282–8 \|year=2007 \|pmid=17646307 \|doi=10.1093/bioinformatics/btm201\|doi-access=free }}</ref>

Citation bot: Add: doi-access. | Use this bot. Report bugs. | Suggested by Abductive | #UCB_webform 1534/3844

2023-09-02T16:02:41Z

Add: doi-access. | Use this bot. Report bugs. | Suggested by Abductive | #UCB_webform 1534/3844

← Previous revision		Revision as of 16:02, 2 September 2023
Line 14:		Line 14:
	Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web \|url=http://bioinf.wehi.edu.au/limma/ \|title=LIMMA Library: Linear Models for Microarray Data \|access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R, MATLAB, and Excel.{{cn\|date=March 2023}}		Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web \|url=http://bioinf.wehi.edu.au/limma/ \|title=LIMMA Library: Linear Models for Microarray Data \|access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R, MATLAB, and Excel.{{cn\|date=March 2023}}

	Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA) <ref>{{cite journal\|last=Irizarry\|first=RA\|author2=Hobbs, B \|author3=Collin, F \|author4=Beazer-Barclay, YD \|author5=Antonellis, KJ \|author6=Scherf, U \|author7= Speed, TP \|title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.\|journal=Biostatistics\|volume=4\|issue=2\|pages=249–64\|year=2003\|pmid=12925520 \|doi=10.1093/biostatistics/4.2.249\|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots, but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal \|vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP \|title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias \|journal=Bioinformatics \|volume=19 \|issue=2 \|pages=185–93 \|year=2003 \|pmid=12538238 \|doi=10.1093/bioinformatics/19.2.185\|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal \|vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B \|title=Algorithm-driven Artifacts in median polish summarization of Microarray data \|journal=BMC Bioinformatics \|volume=11 \|pages=553 \|year=2010 \|pmid=21070630 \|doi=10.1186/1471-2105-11-553 \|pmc=2998528}}</ref> Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.		Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA) <ref>{{cite journal\|last=Irizarry\|first=RA\|author2=Hobbs, B \|author3=Collin, F \|author4=Beazer-Barclay, YD \|author5=Antonellis, KJ \|author6=Scherf, U \|author7= Speed, TP \|title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.\|journal=Biostatistics\|volume=4\|issue=2\|pages=249–64\|year=2003\|pmid=12925520 \|doi=10.1093/biostatistics/4.2.249\|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots, but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal \|vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP \|title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias \|journal=Bioinformatics \|volume=19 \|issue=2 \|pages=185–93 \|year=2003 \|pmid=12538238 \|doi=10.1093/bioinformatics/19.2.185\|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal \|vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B \|title=Algorithm-driven Artifacts in median polish summarization of Microarray data \|journal=BMC Bioinformatics \|volume=11 \|pages=553 \|year=2010 \|pmid=21070630 \|doi=10.1186/1471-2105-11-553 \|pmc=2998528 \|doi-access=free }}</ref> Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.

	The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal \|vauthors=Lim WK, Wang K, Lefebvre C, Califano A \|title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks \|journal=Bioinformatics \|volume=23 \|issue=13 \|pages=i282–8 \|year=2007 \|pmid=17646307 \|doi=10.1093/bioinformatics/btm201\|doi-access=free }}</ref>		The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal \|vauthors=Lim WK, Wang K, Lefebvre C, Califano A \|title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks \|journal=Bioinformatics \|volume=23 \|issue=13 \|pages=i282–8 \|year=2007 \|pmid=17646307 \|doi=10.1093/bioinformatics/btm201\|doi-access=free }}</ref>
Line 29:		Line 29:
	==== Hierarchical clustering ====		==== Hierarchical clustering ====
	{{main\|Hierarchical clustering}}		{{main\|Hierarchical clustering}}
	Hierarchical clustering is a statistical method for finding relatively [[Homogeneity and heterogeneity#Homogeneity\|homogeneous]] clusters. Hierarchical clustering consists of two separate phases. Initially, a [[distance matrix]] containing all the pairwise distances between the genes is calculated. [[Pearson product-moment correlation coefficient\|Pearson's correlation]] and [[Spearman's rank correlation coefficient\|Spearman's correlation]] are often used as dissimilarity estimates, but other methods, like [[Taxicab geometry\|Manhattan distance]] or [[Euclidean distance]], can also be applied. Given the number of distance measures available and their influence in the clustering algorithm results, several studies have compared and evaluated different distance measures for the clustering of microarray data, considering their intrinsic properties and robustness to noise.<ref name=Gentleman>{{cite book\|last1=Gentleman\|first1=Robert\|title=Bioinformatics and computational biology solutions using R and Bioconductor\|date=2005\|publisher=Springer Science+Business Media\|location=New York\|isbn=978-0-387-29362-2\|display-authors=etal}}</ref><ref name=Jaskowiak2013>{{cite journal\|last1=Jaskowiak\|first1=Pablo A.\|last2=Campello\|first2=Ricardo J.G.B.\|last3=Costa\|first3=Ivan G.\|title=Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis\|journal=IEEE/ACM Transactions on Computational Biology and Bioinformatics\|volume=10\|issue=4\|pages=845–857\|doi=10.1109/TCBB.2013.9\|pmid=24334380\|year=2013\|s2cid=760277}}</ref><ref name=Jaskowiak2014>{{cite journal\|last1=Jaskowiak\|first1=Pablo A\|last2=Campello\|first2=Ricardo JGB\|last3=Costa\|first3=Ivan G\|title=On the selection of appropriate distances for gene expression data clustering\|journal=BMC Bioinformatics\|volume=15\|issue=Suppl 2\|pages=S2\|doi=10.1186/1471-2105-15-S2-S2\|pmid=24564555\|pmc=4072854\|year=2014}}</ref> After calculation of the initial distance matrix, the hierarchical clustering algorithm either (A) joins iteratively the two closest clusters starting from single data points (agglomerative, bottom-up approach, which is fairly more commonly used), or (B) partitions clusters iteratively starting from the complete set (divisive, top-down approach). After each step, a new distance matrix between the newly formed clusters and the other clusters is recalculated. Hierarchical cluster analysis methods include:		Hierarchical clustering is a statistical method for finding relatively [[Homogeneity and heterogeneity#Homogeneity\|homogeneous]] clusters. Hierarchical clustering consists of two separate phases. Initially, a [[distance matrix]] containing all the pairwise distances between the genes is calculated. [[Pearson product-moment correlation coefficient\|Pearson's correlation]] and [[Spearman's rank correlation coefficient\|Spearman's correlation]] are often used as dissimilarity estimates, but other methods, like [[Taxicab geometry\|Manhattan distance]] or [[Euclidean distance]], can also be applied. Given the number of distance measures available and their influence in the clustering algorithm results, several studies have compared and evaluated different distance measures for the clustering of microarray data, considering their intrinsic properties and robustness to noise.<ref name=Gentleman>{{cite book\|last1=Gentleman\|first1=Robert\|title=Bioinformatics and computational biology solutions using R and Bioconductor\|date=2005\|publisher=Springer Science+Business Media\|location=New York\|isbn=978-0-387-29362-2\|display-authors=etal}}</ref><ref name=Jaskowiak2013>{{cite journal\|last1=Jaskowiak\|first1=Pablo A.\|last2=Campello\|first2=Ricardo J.G.B.\|last3=Costa\|first3=Ivan G.\|title=Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis\|journal=IEEE/ACM Transactions on Computational Biology and Bioinformatics\|volume=10\|issue=4\|pages=845–857\|doi=10.1109/TCBB.2013.9\|pmid=24334380\|year=2013\|s2cid=760277}}</ref><ref name=Jaskowiak2014>{{cite journal\|last1=Jaskowiak\|first1=Pablo A\|last2=Campello\|first2=Ricardo JGB\|last3=Costa\|first3=Ivan G\|title=On the selection of appropriate distances for gene expression data clustering\|journal=BMC Bioinformatics\|volume=15\|issue=Suppl 2\|pages=S2\|doi=10.1186/1471-2105-15-S2-S2\|pmid=24564555\|pmc=4072854\|year=2014 \|doi-access=free }}</ref> After calculation of the initial distance matrix, the hierarchical clustering algorithm either (A) joins iteratively the two closest clusters starting from single data points (agglomerative, bottom-up approach, which is fairly more commonly used), or (B) partitions clusters iteratively starting from the complete set (divisive, top-down approach). After each step, a new distance matrix between the newly formed clusters and the other clusters is recalculated. Hierarchical cluster analysis methods include:
	* Single linkage (minimum method, nearest neighbor)		* Single linkage (minimum method, nearest neighbor)
	* Average linkage ([[UPGMA]]).		* Average linkage ([[UPGMA]]).
Line 38:		Line 38:
	==== K-means clustering ====		==== K-means clustering ====
	{{main\|k-means clustering}}		{{main\|k-means clustering}}
	K-means clustering is an algorithm for grouping genes or samples based on pattern into ''K'' groups. Grouping is done by minimizing the sum of the squares of distances between the data and the corresponding cluster [[centroid]]. Thus the purpose of K-means clustering is to classify data based on similar expression.<ref>{{cite web \|url=http://www.biostat.ucsf.edu/ \|title=Home \|website=biostat.ucsf.edu}}</ref> K-means clustering algorithm and some of its variants (including [[k-medoids]]) have been shown to produce good results for gene expression data (at least better than hierarchical clustering methods). Empirical comparisons of [[k-means]], [[k-medoids]], hierarchical methods and, different distance measures can be found in the literature.<ref name="Jaskowiak2014" /><ref name=Souto2011>{{cite journal\|last1=de Souto\|first1=Marcilio C. P.\|last2=Costa\|first2=Ivan G.\|last3=de Araujo\|first3=Daniel S. A.\|last4=Ludermir\|first4=Teresa B.\|last5=Schliep\|first5=Alexander\|title=Clustering cancer gene expression data: a comparative study\|journal=BMC Bioinformatics\|volume=9\|issue=1\|pages=497\|doi=10.1186/1471-2105-9-497\|pmid=19038021\|pmc=2632677\|year=2008}}</ref>		K-means clustering is an algorithm for grouping genes or samples based on pattern into ''K'' groups. Grouping is done by minimizing the sum of the squares of distances between the data and the corresponding cluster [[centroid]]. Thus the purpose of K-means clustering is to classify data based on similar expression.<ref>{{cite web \|url=http://www.biostat.ucsf.edu/ \|title=Home \|website=biostat.ucsf.edu}}</ref> K-means clustering algorithm and some of its variants (including [[k-medoids]]) have been shown to produce good results for gene expression data (at least better than hierarchical clustering methods). Empirical comparisons of [[k-means]], [[k-medoids]], hierarchical methods and, different distance measures can be found in the literature.<ref name="Jaskowiak2014" /><ref name=Souto2011>{{cite journal\|last1=de Souto\|first1=Marcilio C. P.\|last2=Costa\|first2=Ivan G.\|last3=de Araujo\|first3=Daniel S. A.\|last4=Ludermir\|first4=Teresa B.\|last5=Schliep\|first5=Alexander\|title=Clustering cancer gene expression data: a comparative study\|journal=BMC Bioinformatics\|volume=9\|issue=1\|pages=497\|doi=10.1186/1471-2105-9-497\|pmid=19038021\|pmc=2632677\|year=2008 \|doi-access=free }}</ref>

	===Pattern recognition===		===Pattern recognition===
Line 64:		Line 64:
	* [[Permutations]] are calculated based on the number of samples		* [[Permutations]] are calculated based on the number of samples
	* Block Permutations		* 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 \| 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. \| journal = BMC Bioinformatics \| volume = 8 \| 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 \| last3 = Culhane \| first3 = AC. \| year = 2006 \| title = Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data \| journal = BMC Bioinformatics \| volume = 7 \| page = 359 \| doi=10.1186/1471-2105-7-359\| pmid = 16872483 \| pmc = 1544358 }}</ref>		** 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 \| 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. \| journal = BMC Bioinformatics \| volume = 8 \| page = 242 \| doi=10.1186/1471-2105-8-242\| pmid = 17612399 \| pmc = 1931607 \| doi-access = free }}</ref><ref name="R3">{{cite journal \| last1 = Jeffery \| first1 = I. H. \| last2 = DG \| last3 = Culhane \| first3 = AC. \| year = 2006 \| title = Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data \| journal = BMC Bioinformatics \| volume = 7 \| page = 359 \| doi=10.1186/1471-2105-7-359\| pmid = 16872483 \| pmc = 1544358 \| doi-access = free }}</ref>
	the number of permutations is set by the user when imputing correct values for the data set to run SAM		the number of permutations is set by the user when imputing correct values for the data set to run SAM

Line 106:		Line 106:
	* Correlates expression data to clinical parameters<ref name="R6"/>		* Correlates expression data to clinical parameters<ref name="R6"/>
	* Correlates expression data with time<ref name="R1"/>		* 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 \| journal = BMC Bioinformatics \| volume = 6 \| page = 129 \| doi = 10.1186/1471-2105-6-129 \| pmid = 15921534 \| pmc = 1173086 }}</ref>		* 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 \| journal = BMC Bioinformatics \| volume = 6 \| page = 129 \| doi = 10.1186/1471-2105-6-129 \| pmid = 15921534 \| pmc = 1173086 \| doi-access = free }}</ref>
	* Reports local false discovery rate (the FDR for genes having a similar d<sub>i</sub> as that gene)<ref name="R1"/> and miss rates <ref name="R1"/><ref name="R7"/>		* Reports local false discovery rate (the FDR for genes having a similar d<sub>i</sub> 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"/>		* Can work with blocked design for when treatments are applied within different batches of arrays<ref name="R1"/>

DreamRimmer: Reverted edits by 217.149.179.66 (talk) (AV)

2023-05-25T16:21:29Z

Reverted edits by 217.149.179.66 (talk) (AV)

← Previous revision		Revision as of 16:21, 25 May 2023
Line 22:		Line 22:

	===Identification of significant differential expression===		===Identification of significant differential expression===
	Many strategies exist to identify array probes that show an unusual level of over-expression or under-expression. The simplest one is to call "significant" any probe that differs by an average of at least twofold between treatment groups. More sophisticated approaches are often related to [[t-test]]s or other mechanisms that take both effect size and variability into account. Curiously, the p-values associated with particular genes do not reproduce well between replicate experiments, and lists generated by straight fold change perform much better.<ref>{{cite journal \|vauthors=Shi L, Reid LH, Jones WD, etal \|title=The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements \|journal=Nat. Biotechnol. \|volume=24 \|issue=9 \|pages=1151–61 \|year=2006 \|pmid=16964229 \|doi=10.1038/nbt1239 \|pmc=3272078}}</ref><ref>{{cite journal \|vauthors=Guo L, Lobenhofer EK, Wang C, etal \|title=Rat toxicogenomic study reveals analytical consistency across microarray platforms \|journal=Nat. Biotechnol. \|volume=24 \|issue=9 \|pages=1162–9 \|year=2006 \|pmid=17061323 \|doi=10.1038/nbt1238\|s2cid=8192240 }}</ref> This represents an extremely important observation, since the point of performing experiments has to do with predicting general behavior. The MAQC group recommends using a fold change assessment plus a non-stringent p-value cutoff, further pointing out that changes in the background correction and scaling process have only a minimal impact on the rank order of fold change differences, but a substantial impact on p-values.{{cn\|date=March 2023}} ~~NOO~~		Many strategies exist to identify array probes that show an unusual level of over-expression or under-expression. The simplest one is to call "significant" any probe that differs by an average of at least twofold between treatment groups. More sophisticated approaches are often related to [[t-test]]s or other mechanisms that take both effect size and variability into account. Curiously, the p-values associated with particular genes do not reproduce well between replicate experiments, and lists generated by straight fold change perform much better.<ref>{{cite journal \|vauthors=Shi L, Reid LH, Jones WD, etal \|title=The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements \|journal=Nat. Biotechnol. \|volume=24 \|issue=9 \|pages=1151–61 \|year=2006 \|pmid=16964229 \|doi=10.1038/nbt1239 \|pmc=3272078}}</ref><ref>{{cite journal \|vauthors=Guo L, Lobenhofer EK, Wang C, etal \|title=Rat toxicogenomic study reveals analytical consistency across microarray platforms \|journal=Nat. Biotechnol. \|volume=24 \|issue=9 \|pages=1162–9 \|year=2006 \|pmid=17061323 \|doi=10.1038/nbt1238\|s2cid=8192240 }}</ref> This represents an extremely important observation, since the point of performing experiments has to do with predicting general behavior. The MAQC group recommends using a fold change assessment plus a non-stringent p-value cutoff, further pointing out that changes in the background correction and scaling process have only a minimal impact on the rank order of fold change differences, but a substantial impact on p-values.{{cn\|date=March 2023}}

	=== Clustering ===		=== Clustering ===

217.149.179.66: /* Identification of significant differential expression */

2023-05-25T16:21:13Z

Identification of significant differential expression

← Previous revision		Revision as of 16:21, 25 May 2023
Line 22:		Line 22:

	===Identification of significant differential expression===		===Identification of significant differential expression===
	Many strategies exist to identify array probes that show an unusual level of over-expression or under-expression. The simplest one is to call "significant" any probe that differs by an average of at least twofold between treatment groups. More sophisticated approaches are often related to [[t-test]]s or other mechanisms that take both effect size and variability into account. Curiously, the p-values associated with particular genes do not reproduce well between replicate experiments, and lists generated by straight fold change perform much better.<ref>{{cite journal \|vauthors=Shi L, Reid LH, Jones WD, etal \|title=The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements \|journal=Nat. Biotechnol. \|volume=24 \|issue=9 \|pages=1151–61 \|year=2006 \|pmid=16964229 \|doi=10.1038/nbt1239 \|pmc=3272078}}</ref><ref>{{cite journal \|vauthors=Guo L, Lobenhofer EK, Wang C, etal \|title=Rat toxicogenomic study reveals analytical consistency across microarray platforms \|journal=Nat. Biotechnol. \|volume=24 \|issue=9 \|pages=1162–9 \|year=2006 \|pmid=17061323 \|doi=10.1038/nbt1238\|s2cid=8192240 }}</ref> This represents an extremely important observation, since the point of performing experiments has to do with predicting general behavior. The MAQC group recommends using a fold change assessment plus a non-stringent p-value cutoff, further pointing out that changes in the background correction and scaling process have only a minimal impact on the rank order of fold change differences, but a substantial impact on p-values.{{cn\|date=March 2023}}		Many strategies exist to identify array probes that show an unusual level of over-expression or under-expression. The simplest one is to call "significant" any probe that differs by an average of at least twofold between treatment groups. More sophisticated approaches are often related to [[t-test]]s or other mechanisms that take both effect size and variability into account. Curiously, the p-values associated with particular genes do not reproduce well between replicate experiments, and lists generated by straight fold change perform much better.<ref>{{cite journal \|vauthors=Shi L, Reid LH, Jones WD, etal \|title=The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements \|journal=Nat. Biotechnol. \|volume=24 \|issue=9 \|pages=1151–61 \|year=2006 \|pmid=16964229 \|doi=10.1038/nbt1239 \|pmc=3272078}}</ref><ref>{{cite journal \|vauthors=Guo L, Lobenhofer EK, Wang C, etal \|title=Rat toxicogenomic study reveals analytical consistency across microarray platforms \|journal=Nat. Biotechnol. \|volume=24 \|issue=9 \|pages=1162–9 \|year=2006 \|pmid=17061323 \|doi=10.1038/nbt1238\|s2cid=8192240 }}</ref> This represents an extremely important observation, since the point of performing experiments has to do with predicting general behavior. The MAQC group recommends using a fold change assessment plus a non-stringent p-value cutoff, further pointing out that changes in the background correction and scaling process have only a minimal impact on the rank order of fold change differences, but a substantial impact on p-values.{{cn\|date=March 2023}} NOO

	=== Clustering ===		=== Clustering ===

Ingenuity: Reverted edits by 217.149.179.66 (talk) (AV)

2023-05-25T16:20:49Z

Reverted edits by 217.149.179.66 (talk) (AV)

← Previous revision		Revision as of 16:20, 25 May 2023
Line 5:		Line 5:
	Microarray data analysis is the final step in reading and processing data produced by a microarray chip. Samples undergo various processes including purification and scanning using the microchip, which then produces a large amount of data that requires processing via computer software. It involves several distinct steps, as outlined in the image below. Changing any one of the steps will change the outcome of the analysis, so the MAQC Project<ref>{{cite web \| url = https://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ \| title = MicroArray Quality Control (MAQC) Project \| access-date = 2007-12-26 \| author = Dr. Leming Shi, National Center for Toxicological Research \| publisher = U.S. Food and Drug Administration }}</ref> was created to identify a set of standard strategies. Companies exist that use the MAQC protocols to perform a complete analysis.<ref>{{cite web \|url=http://www.genusbiosystems.com/services-data.shtml \|title=GenUs BioSystems - Services - Data Analysis \|access-date=2008-01-02 }}</ref>		Microarray data analysis is the final step in reading and processing data produced by a microarray chip. Samples undergo various processes including purification and scanning using the microchip, which then produces a large amount of data that requires processing via computer software. It involves several distinct steps, as outlined in the image below. Changing any one of the steps will change the outcome of the analysis, so the MAQC Project<ref>{{cite web \| url = https://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ \| title = MicroArray Quality Control (MAQC) Project \| access-date = 2007-12-26 \| author = Dr. Leming Shi, National Center for Toxicological Research \| publisher = U.S. Food and Drug Administration }}</ref> was created to identify a set of standard strategies. Companies exist that use the MAQC protocols to perform a complete analysis.<ref>{{cite web \|url=http://www.genusbiosystems.com/services-data.shtml \|title=GenUs BioSystems - Services - Data Analysis \|access-date=2008-01-02 }}</ref>

	[[File:Microarray exp horizontal.svg\|thumb\|800px\|none\|The steps required in a microarray experiment]]~~я шучу (im joking)~~		[[File:Microarray exp horizontal.svg\|thumb\|800px\|none\|The steps required in a microarray experiment]]

	==Techniques==		==Techniques==

217.149.179.66: /* Introduction */

2023-05-25T16:20:36Z

Introduction

← Previous revision		Revision as of 16:20, 25 May 2023
Line 5:		Line 5:
	Microarray data analysis is the final step in reading and processing data produced by a microarray chip. Samples undergo various processes including purification and scanning using the microchip, which then produces a large amount of data that requires processing via computer software. It involves several distinct steps, as outlined in the image below. Changing any one of the steps will change the outcome of the analysis, so the MAQC Project<ref>{{cite web \| url = https://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ \| title = MicroArray Quality Control (MAQC) Project \| access-date = 2007-12-26 \| author = Dr. Leming Shi, National Center for Toxicological Research \| publisher = U.S. Food and Drug Administration }}</ref> was created to identify a set of standard strategies. Companies exist that use the MAQC protocols to perform a complete analysis.<ref>{{cite web \|url=http://www.genusbiosystems.com/services-data.shtml \|title=GenUs BioSystems - Services - Data Analysis \|access-date=2008-01-02 }}</ref>		Microarray data analysis is the final step in reading and processing data produced by a microarray chip. Samples undergo various processes including purification and scanning using the microchip, which then produces a large amount of data that requires processing via computer software. It involves several distinct steps, as outlined in the image below. Changing any one of the steps will change the outcome of the analysis, so the MAQC Project<ref>{{cite web \| url = https://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ \| title = MicroArray Quality Control (MAQC) Project \| access-date = 2007-12-26 \| author = Dr. Leming Shi, National Center for Toxicological Research \| publisher = U.S. Food and Drug Administration }}</ref> was created to identify a set of standard strategies. Companies exist that use the MAQC protocols to perform a complete analysis.<ref>{{cite web \|url=http://www.genusbiosystems.com/services-data.shtml \|title=GenUs BioSystems - Services - Data Analysis \|access-date=2008-01-02 }}</ref>

	[[File:Microarray exp horizontal.svg\|thumb\|800px\|none\|The steps required in a microarray experiment]]я шучу		[[File:Microarray exp horizontal.svg\|thumb\|800px\|none\|The steps required in a microarray experiment]]я шучу (im joking)

	==Techniques==		==Techniques==

← Previous revision		Revision as of 08:05, 7 June 2024
Line 14:		Line 14:
	Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web \|url=http://bioinf.wehi.edu.au/limma/ \|title=LIMMA Library: Linear Models for Microarray Data \|access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R and MATLAB.<ref>{{Cite journal \|last1=Gatto \|first1=Laurent \|last2=Breckels \|first2=Lisa M. \|last3=Naake \|first3=Thomas \|last4=Gibb \|first4=Sebastian \|date=2015 \|title=Visualization of proteomics data using R and Bioconductor \|journal=Proteomics \|language=en \|volume=15 \|issue=8 \|pages=1375–1389 \|doi=10.1002/pmic.201400392 \|issn=1615-9853 \|pmc=4510819 \|pmid=25690415}}</ref><ref>{{Cite web \|title=Create intensity versus ratio scatter plot of microarray data - MATLAB mairplot \|url=https://www.mathworks.com/help/bioinfo/ref/mairplot.html#responsive_offcanvas \|access-date=2023-11-24 \|website=MathWorks}}</ref>		Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web \|url=http://bioinf.wehi.edu.au/limma/ \|title=LIMMA Library: Linear Models for Microarray Data \|access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R and MATLAB.<ref>{{Cite journal \|last1=Gatto \|first1=Laurent \|last2=Breckels \|first2=Lisa M. \|last3=Naake \|first3=Thomas \|last4=Gibb \|first4=Sebastian \|date=2015 \|title=Visualization of proteomics data using R and Bioconductor \|journal=Proteomics \|language=en \|volume=15 \|issue=8 \|pages=1375–1389 \|doi=10.1002/pmic.201400392 \|issn=1615-9853 \|pmc=4510819 \|pmid=25690415}}</ref><ref>{{Cite web \|title=Create intensity versus ratio scatter plot of microarray data - MATLAB mairplot \|url=https://www.mathworks.com/help/bioinfo/ref/mairplot.html#responsive_offcanvas \|access-date=2023-11-24 \|website=MathWorks}}</ref>

	Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA)<ref>{{cite journal\|last1=Irizarry\|first1=RA\|author-link1=Rafael Irizarry (scientist) \|author2=Hobbs, B \|author3=Collin, F \|author4=Beazer-Barclay, YD \|author5=Antonellis, KJ \|author6=Scherf, U \|author7= Speed, TP \|title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.\|journal=Biostatistics\|volume=4\|issue=2\|pages=249–64\|year=2003\|pmid=12925520 \|doi=10.1093/biostatistics/4.2.249\|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal \|vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP \|title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias \|journal=Bioinformatics \|volume=19 \|issue=2 \|pages=185–93 \|year=2003 \|pmid=12538238 \|doi=10.1093/bioinformatics/19.2.185\|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal \|vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B \|title=Algorithm-driven Artifacts in median polish summarization of Microarray data \|journal=BMC Bioinformatics \|volume=11 \|pages=553 \|year=2010 \|pmid=21070630 \|doi=10.1186/1471-2105-11-553 \|pmc=2998528 \|doi-access=free }}</ref> Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.		Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA)<ref>{{cite journal\|last1=Irizarry\|first1=RA\|author-link1=Rafael Irizarry (scientist) \|author2=Hobbs, B \|author3=Collin, F \|author4=Beazer-Barclay, YD \|author5=Antonellis, KJ \|author6=Scherf, U \|author7= Speed, TP \|title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.\|journal=Biostatistics\|volume=4\|issue=2\|pages=249–64\|year=2003\|pmid=12925520 \|doi=10.1093/biostatistics/4.2.249\|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal \|vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP \|title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias \|journal=Bioinformatics \|volume=19 \|issue=2 \|pages=185–93 \|year=2003 \|pmid=12538238 \|doi=10.1093/bioinformatics/19.2.185\|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal \|vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B \|title=Algorithm-driven Artifacts in median polish summarization of Microarray data \|journal=BMC Bioinformatics \|volume=11 \|pages=553 \|year=2010 \|pmid=21070630 \|doi=10.1186/1471-2105-11-553 \|pmc=2998528 \|doi-access=free }}</ref> [[Quantile normalization]], also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.

	The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal \|vauthors=Lim WK, Wang K, Lefebvre C, Califano A \|title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks \|journal=Bioinformatics \|volume=23 \|issue=13 \|pages=i282–8 \|year=2007 \|pmid=17646307 \|doi=10.1093/bioinformatics/btm201\|doi-access=free }}</ref>		The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal \|vauthors=Lim WK, Wang K, Lefebvre C, Califano A \|title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks \|journal=Bioinformatics \|volume=23 \|issue=13 \|pages=i282–8 \|year=2007 \|pmid=17646307 \|doi=10.1093/bioinformatics/btm201\|doi-access=free }}</ref>

← Previous revision		Revision as of 19:49, 30 September 2023
Line 14:		Line 14:
	Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web \|url=http://bioinf.wehi.edu.au/limma/ \|title=LIMMA Library: Linear Models for Microarray Data \|access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R, MATLAB, and Excel.{{cn\|date=March 2023}}		Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web \|url=http://bioinf.wehi.edu.au/limma/ \|title=LIMMA Library: Linear Models for Microarray Data \|access-date=2008-01-01 }}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R, MATLAB, and Excel.{{cn\|date=March 2023}}

	Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA) <ref>{{cite journal\|~~last~~=Irizarry\|~~first~~=RA\|author2=Hobbs, B \|author3=Collin, F \|author4=Beazer-Barclay, YD \|author5=Antonellis, KJ \|author6=Scherf, U \|author7= Speed, TP \|title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.\|journal=Biostatistics\|volume=4\|issue=2\|pages=249–64\|year=2003\|pmid=12925520 \|doi=10.1093/biostatistics/4.2.249\|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots, but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal \|vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP \|title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias \|journal=Bioinformatics \|volume=19 \|issue=2 \|pages=185–93 \|year=2003 \|pmid=12538238 \|doi=10.1093/bioinformatics/19.2.185\|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal \|vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B \|title=Algorithm-driven Artifacts in median polish summarization of Microarray data \|journal=BMC Bioinformatics \|volume=11 \|pages=553 \|year=2010 \|pmid=21070630 \|doi=10.1186/1471-2105-11-553 \|pmc=2998528 \|doi-access=free }}</ref> Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.		Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA) <ref>{{cite journal\|last1=Irizarry\|first1=RA\|author-link1=Rafael Irizarry (scientist) \|author2=Hobbs, B \|author3=Collin, F \|author4=Beazer-Barclay, YD \|author5=Antonellis, KJ \|author6=Scherf, U \|author7= Speed, TP \|title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.\|journal=Biostatistics\|volume=4\|issue=2\|pages=249–64\|year=2003\|pmid=12925520 \|doi=10.1093/biostatistics/4.2.249\|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots, but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal \|vauthors=Bolstad BM, Irizarry RA, Astrand M, Speed TP \|title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias \|journal=Bioinformatics \|volume=19 \|issue=2 \|pages=185–93 \|year=2003 \|pmid=12538238 \|doi=10.1093/bioinformatics/19.2.185\|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal \|vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B \|title=Algorithm-driven Artifacts in median polish summarization of Microarray data \|journal=BMC Bioinformatics \|volume=11 \|pages=553 \|year=2010 \|pmid=21070630 \|doi=10.1186/1471-2105-11-553 \|pmc=2998528 \|doi-access=free }}</ref> Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.

	The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal \|vauthors=Lim WK, Wang K, Lefebvre C, Califano A \|title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks \|journal=Bioinformatics \|volume=23 \|issue=13 \|pages=i282–8 \|year=2007 \|pmid=17646307 \|doi=10.1093/bioinformatics/btm201\|doi-access=free }}</ref>		The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal \|vauthors=Lim WK, Wang K, Lefebvre C, Califano A \|title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks \|journal=Bioinformatics \|volume=23 \|issue=13 \|pages=i282–8 \|year=2007 \|pmid=17646307 \|doi=10.1093/bioinformatics/btm201\|doi-access=free }}</ref>