209:, 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. 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:
76:
63:
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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.
318:. Protein complex enrichment analysis tool (COMPLEAT) provides similar enrichment analysis at the level of protein complexes. The tool can identify the dynamic protein complex regulation under different condition or time points. Related system, PAINT and SCOPE performs a statistical analysis on gene promoter regions, identifying over and under representation of previously identified
28:
982:(t) are specified to guarantee genes called significant change at least a pre-specified amount. This means that the absolute value of the average expression levels of a gene under each of two conditions must be greater than the fold change (t) to be called positive and less than the inverse of the fold change (t) to be called negative.
290:
975:
50: – in a single experiment. Such experiments can generate very large amounts of data, allowing researchers to assess the overall state of a cell or organism. Data in such large quantities is difficult – if not impossible – to analyze without the help of computer programs.
340:
114:
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) is a normalization approach
322:
response elements. Another statistical analysis tool is Rank Sum
Statistics for Gene Set Collections (RssGsc), which uses rank sum probability distribution functions to find gene sets that explain experimental data. A further approach is contextual meta-analysis, i.e. finding out how a gene cluster
140:
for Robust
Microarray Summarization (FARMS) is a model-based technique for summarizing array data at perfect match probe level. It is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise.
58:
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
1097:
Visual identification of local artifacts, such as printing or washing defects, may likewise suggest the removal of individual spots. This can take a substantial amount of time depending on the quality of array manufacture. In addition, some procedures call for the elimination of all spots with an
130:
1080:
Depending on the type of array, signal related to nonspecific binding of the fluorophore can be subtracted to achieve better results. One approach involves subtracting the average signal intensity of the area between spots. A variety of tools for background correction and further analysis are
154:
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. This represents an extremely important
149:
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
59:
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 was created to identify a set of standard strategies. Companies exist that use the MAQC protocols to perform a complete analysis.
102:
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
297:
Specialized software tools for statistical analysis to determine the extent of over- or under-expression of a gene in a microarray experiment relative to a reference state have also been developed to aid in identifying genes or gene sets associated with particular
601:
475:
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
462:
SAM is run as an Excel Add-In, and the SAM Plot
Controller allows Customization of the False Discovery Rate and Delta, while the SAM Plot and SAM Output functionality generate a List of Significant Genes, Delta Table, and Assessment of Sample
369:, it is now possible to measure the expression of thousands of genes in a single hybridization experiment. The data generated is considerable, and a method for sorting out what is significant and what isn't is essential. SAM is distributed by
1071:
Entire arrays may have obvious flaws detectable by visual inspection, pairwise comparisons to arrays in the same experimental group, or by analysis of RNA degradation. Results may improve by removing these arrays from the analysis entirely.
310:-style statistic to identify groups of genes that are regulated together. This third-party statistics package offers the user information on the genes or gene sets of interest, including links to entries in databases such as NCBI's
556:
SAM calculates a test statistic for relative difference in gene expression based on permutation analysis of expression data and calculates a false discovery rate. The principal calculations of the program are illustrated below.
269:
Commercial systems for gene network analysis such as
Ingenuity and Pathway studio create visual representations of differentially expressed genes based on current scientific literature. Non-commercial tools such as FunRich,
441:
Adjust the Delta tuning parameter to get a significant # of genes along with an acceptable false discovery rate (FDR)) and Assess Sample Size by calculating the mean difference in expression in the SAM Plot
107:. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots. A common method for evaluating how well normalized an array is, is to plot an
227:
Different studies have already shown empirically that the Single linkage clustering algorithm produces poor results when employed to gene expression microarray data and thus should be avoided.
1716:
Jaskowiak, Pablo A.; Campello, Ricardo J.G.B.; Costa, Ivan G. (2013). "Proximity
Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis".
407:
of the data are used to determine if the expression of any gene is significant related to the response. The use of permutation-based analysis accounts for correlations in genes and avoids
94:, provide commercial data analysis software alongside their microarray products. There are also open source options that utilize a variety of methods for analyzing microarray data.
970:{\displaystyle \mathrm {False\ discovery\ rate\ (FDR)={\frac {Median\ (or\ 90^{th}\ percentile)\ of\ \#\ of\ falsely\ called\ genes}{Number\ of\ genes\ called\ significant}}} }
278:
also aid in organizing and visualizing gene network data procured from one or several microarray experiments. A wide variety of microarray analysis tools are available through
327:
is a public tool to perform contextual meta-analysis across contexts such as anatomical parts, stages of development, and response to diseases, chemicals, stresses, and
998:
Call each gene significant if the absolute value of the test statistic for that gene minus the mean test statistic for that gene is greater than a stated threshold
2188:"Integration of statistical inference methods and a novel control measure to improve sensitivity and specificity of data analysis in expression profiling studies"
194:
547:— no explicit response parameter is specified; the user specifies eigengene (principal component) of the expression data and treats it as a quantitative response
2243:<Zhang, S. (2007). "A comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performance." BMC Bioinformatics 8: 230.
249:. Thus the purpose of K-means clustering is to classify data based on similar expression. K-means clustering algorithm and some of its variants (including
126:
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.
79:
2554:
198:
286:. The frequently cited SAM module and other microarray tools are available through Stanford University. Another set is available from Harvard and MIT.
253:) have been shown to produce good results for gene expression data (at least better than hierarchical clustering methods). Empirical comparisons of
46:, which allow researchers to investigate the expression state of a large number of genes – in many cases, an organism's entire
1700:
1112:
2620:
1648:
Guo L, Lobenhofer EK, Wang C, et al. (2006). "Rat toxicogenomic study reveals analytical consistency across microarray platforms".
2157:
Chu, G., Narasimhan, B, Tibshirani, R, Tusher, V. "SAM "Significance
Analysis of Microarrays" Users Guide and technical document."
2651:
141:
According to the
Affycomp benchmark FARMS outperformed all other summarizations methods with respect to sensitivity and specificity.
1907:
1238:
2047:
562:
1601:"The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements"
567:
75:
123:, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.
515:— measurement units are different in the two groups; e.g. control and treatment groups with samples from different patients
245:
groups. Grouping is done by minimizing the sum of the squares of distances between the data and the corresponding cluster
186:
541:— each experimental units is measured at more than one time point; experimental units fall into a one or two class design
2320:
Dinu, I. P.; JD; Mueller, T; Liu, Q; Adewale, AJ; Jhangri, GS; Einecke, G; Famulski, KS; Halloran, P; Yasui, Y. (2007).
521:— same experimental units are measured in the two groups; e.g. samples before and after treatment from the same patients
1364:
395:, which measures the strength of the relationship between gene expression and a response variable. This analysis uses
2631:
1818:
de Souto, Marcilio C. P.; Costa, Ivan G.; de Araujo, Daniel S. A.; Ludermir, Teresa B.; Schliep, Alexander (2008).
1014:
Positive gene set — higher expression of most genes in the gene set correlates with higher values of the phenotype
529:— more than two groups with each containing different experimental units; generalization of two class unpaired type
1020:
Negative gene set — lower expression of most genes in the gene set correlates with higher values of the phenotype
403:. The response variable describes and groups the data based on experimental conditions. In this method, repeated
396:
275:
119:. The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.
1340:
2072:
374:
2373:"Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data"
1410:"A comparison of normalization methods for high density oligonucleotide array data based on variance and bias"
408:
2625:
2558:
180:
164:
1502:"Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks"
1147:"Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles"
1086:
411:
assumptions about the distribution of individual genes. This is an advantage over other techniques (e.g.,
283:
120:
115:
that does not take advantage of these mismatch spots but still must summarize the perfect matches through
2582:
482:
the number of permutations is set by the user when imputing correct values for the data set to run SAM
2646:
2615:
2605:
2274:
1107:
319:
31:
Example of an approximately 40,000 probe spotted oligo microarray with enlarged inset to show detail.
2051:
17:
2583:
ArrayExplorer - Compare microarray side by side to find the one that best suits your research needs
1197:
400:
381:
370:
303:
1369:"Exploration, normalization, and summaries of high density oligonucleotide array probe level data"
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236:
206:
168:
1082:
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Can work with blocked design for when treatments are applied within different batches of arrays
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354:
163:
Clustering is a data mining technique used to group genes having similar expression patterns.
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2199:
2019:
2011:
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202:
104:
62:
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1911:
1117:
432:
experiments — DNA microarray with oligo and cDNA primers, SNP arrays, protein arrays, etc.
362:
190:
137:
1242:
111:
of the data. MA plots can be produced using programs and languages such as R and MATLAB.
2278:
1367:; Hobbs, B; Collin, F; Beazer-Barclay, YD; Antonellis, KJ; Scherf, U; Speed, TP (2003).
261:, hierarchical methods and, different distance measures can be found in the literature.
2450:
2423:
2399:
2372:
2348:
2321:
2024:
2000:"Protein Complex-Based Analysis Framework for High-Throughput Data Sets. 6, rs5 (2013)"
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1600:
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1317:
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366:
324:
241:
K-means clustering is an algorithm for grouping genes or samples based on pattern into
1426:
1409:
1035:
Data from Oligo or cDNA arrays, SNP array, protein arrays, etc. can be utilized in SAM
293:
Example of FunRich tool output. Image shows the result of comparing 4 different genes.
2640:
2611:
2587:
2475:"Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis"
2297:
2259:
1218:
315:
116:
2491:
2474:
2424:"Considerations when using the significance analysis of microarrays (SAM) algorithm"
1559:
1542:
1518:
1501:
1385:
1368:
1677:
466:
404:
279:
1745:
2260:"Significance analysis of microarrays applied to the ionizing radiation response"
2158:
1341:"Create intensity versus ratio scatter plot of microarray data - MATLAB mairplot"
2514:
358:
189:
clusters. Hierarchical clustering consists of two separate phases. Initially, a
1958:
1782:
1766:"On the selection of appropriate distances for gene expression data clustering"
445:
List
Differentially Expressed Genes (Positively and Negatively Expressed Genes)
2204:
2187:
2015:
1451:"Algorithm-driven Artifacts in median polish summarization of Microarray data"
1122:
429:
416:
350:
87:
43:
1467:
1308:
380:
SAM identifies statistically significant genes by carrying out gene specific
2440:
2389:
2338:
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1163:
1044:
Uses data permutation to estimates False Discovery Rate for multiple testing
459:
for academic and non-academic users after completion of a registration step.
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Bioinformatics and computational biology solutions using R and Bioconductor
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1435:
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1326:
1300:
1182:
1001:
Estimate the false discovery rate based on expected versus observed values
1283:
Gatto, Laurent; Breckels, Lisa M.; Naake, Thomas; Gibb, Sebastian (2015).
456:
2588:
FARMS - Factor Analysis for Robust Microarray Summarization, an R package
246:
2600:
ArrayMining.net - web-application for online analysis of microarray data
1729:
1047:
Reports local false discovery rate (the FDR for genes having a similar d
27:
1583:"Affycomp III: A Benchmark for Affymetrix GeneChip Expression Measures"
311:
271:
254:
193:
containing all the pairwise distances between the genes is calculated.
185:
Hierarchical clustering is a statistical method for finding relatively
108:
91:
2117:
151:
47:
38:
are used in interpreting the data generated from experiments on DNA (
2076:
1661:
1616:
1999:
1263:
561:
289:
201:
are often used as dissimilarity estimates, but other methods, like
1057:
Can adjust threshold determining number of gene called significant
992:
For each permutation compute the ordered null (unaffected) scores
412:
338:
288:
217:
128:
74:
61:
2097:
1764:
Jaskowiak, Pablo A; Campello, Ricardo JGB; Costa, Ivan G (2014).
1718:
IEEE/ACM Transactions on Computational Biology and Bioinformatics
995:
Plot the ordered test statistic against the expected null scores
566:
1869:
579:
constant is chosen to minimize the coefficient of variation of
129:
535:— data of a time until an event (for example death or relapse)
339:
2599:
1998:
Vinayagam A, Hu Y, Kulkarni M, Roesel C, et al. (2013).
1820:"Clustering cancer gene expression data: a comparative study"
1582:
419:), which assume equal variance and/or independence of genes.
1887:
1543:"A new summarization method for affymetrix probe level data"
1196:
Dr. Leming Shi, National Center for Toxicological Research.
1978:
1285:"Visualization of proteomics data using R and Bioconductor"
2322:"Improving gene set analysis of microarray data by SAM-GS"
1932:
503:— tests whether the mean gene expression differs from zero
133:
Flowchart showing how the MAS5 algorithm by Agilent works.
2593:
2626:
GeneChip® Expression Analysis-Data Analysis Fundamentals
1145:
Subramanian A, Tamayo P, Mootha VK, et al. (2005).
435:
Input Expression Analysis in Microsoft Excel — see below
1098:
expression value below a certain intensity threshold.
1408:
Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003).
604:
145:
Identification of significant differential expression
223:
Complete linkage (maximum method, furthest neighbor)
2258:Tusher, V. G.; Tibshirani, R.; et al. (2001).
171:are widely used techniques in microarray analysis.
1264:"LIMMA Library: Linear Models for Microarray Data"
969:
365:are statistically significant. With the advent of
1038:Correlates expression data to clinical parameters
213:Single linkage (minimum method, nearest neighbor)
2594:StatsArray - Online Microarray Analysis Services
1759:
1757:
1755:
1449:Giorgi FM, Bolger AM, Lohse M, Usadel B (2010).
323:responds to a variety of experimental contexts.
2267:Proceedings of the National Academy of Sciences
1500:Lim WK, Wang K, Lefebvre C, Califano A (2007).
592:is equal to the expression levels (x) for gene
2606:FunRich - Perform gene set enrichment analysis
1599:Shi L, Reid LH, Jones WD, et al. (2006).
1541:Hochreiter S, Clevert DA, Obermayer K (2006).
1695:. New York: Springer Science+Business Media.
1219:"GenUs BioSystems - Services - Data Analysis"
989:Order test statistics according to magnitude
469:are calculated based on the number of samples
66:The steps required in a microarray experiment
8:
1813:
1811:
2118:"SAM: Significance Analysis of Microarrays"
1198:"MicroArray Quality Control (MAQC) Project"
314:and curated databases such as Biocarta and
353:, established in 2001 by Virginia Tusher,
347:Significance analysis of microarrays (SAM)
335:Significance analysis of microarrays (SAM)
80:National Center for Toxicological Research
2490:
2449:
2439:
2398:
2388:
2371:Jeffery, I. H.; DG; Culhane, AC. (2006).
2347:
2337:
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2286:
2203:
2023:
1933:"FunRich: Functional Enrichment Analysis"
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1835:
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1517:
1476:
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1425:
1384:
1316:
1172:
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727:
687:
605:
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455:SAM is available for download online at
302:. One such method of analysis, known as
26:
2515:"J. Craig Venter Institute -- Software"
2422:Larsson, O. W. C; Timmons, JA. (2005).
2186:Zang, S.; Guo, R.; et al. (2007).
1691:Gentleman, Robert; et al. (2005).
1134:
457:http://www-stat.stanford.edu/~tibs/SAM/
86:Most microarray manufacturers, such as
2632:Duke data_analysis_fundamentals_manual
1979:"BioCarta - Charting Pathways of Life"
2253:
2251:
2249:
2239:
2237:
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2137:
1594:
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361:, for determining whether changes in
7:
2612:Comparative Transcriptomics Analysis
2555:"Ocimum Biosolutions | Genowiz"
1140:
1138:
1113:Significance analysis of microarrays
1062:Error correction and quality control
1041:Correlates expression data with time
985:The SAM algorithm can be stated as:
438:Run SAM as a Microsoft Excel Add-Ins
18:Significance Analysis of Microarrays
1200:. U.S. Food and Drug Administration
1908:"Ariadne Genomics: Pathway Studio"
960:
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951:
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497:— real-valued (such as heart rate)
399:, since the data may not follow a
25:
2616:Reference Module in Life Sciences
2192:Journal of Biomedical Informatics
596:under y experimental conditions.
82:scientist reviews microarray data
1239:"Agilent | DNA Microarrays"
565:
560:
1427:10.1093/bioinformatics/19.2.185
2535:"Agilent | GeneSpring GX"
1081:available from TIGR, Agilent (
769:
711:
681:
669:
36:Microarray analysis techniques
1:
2492:10.1093/bioinformatics/bti605
2473:Wilson CL, Miller CJ (2005).
1560:10.1093/bioinformatics/btl033
1519:10.1093/bioinformatics/btm201
1386:10.1093/biostatistics/4.2.249
1051:as that gene) and miss rates
98:Aggregation and normalization
1151:Proc. Natl. Acad. Sci. U.S.A
509:— two sets of measurements
2668:
1783:10.1186/1471-2105-15-S2-S2
234:
178:
2652:Bioinformatics algorithms
2621:SAM download instructions
2205:10.1016/j.jbi.2007.01.002
2016:10.1126/scisignal.2003629
397:non-parametric statistics
384:and computes a statistic
1468:10.1186/1471-2105-11-553
306:Analysis (GSEA), uses a
2441:10.1186/1471-2105-6-129
2390:10.1186/1471-2105-7-359
2339:10.1186/1471-2105-8-242
1837:10.1186/1471-2105-9-497
1164:10.1073/pnas.0506580102
181:Hierarchical clustering
175:Hierarchical clustering
165:Hierarchical clustering
2288:10.1073/pnas.091062498
1301:10.1002/pmic.201400392
1011:Significant gene sets
971:
343:
294:
284:R programming language
199:Spearman's correlation
134:
121:Quantile normalization
83:
67:
32:
2122:tibshirani.su.domains
1076:Background correction
972:
351:statistical technique
342:
292:
195:Pearson's correlation
132:
78:
65:
30:
1245:on December 22, 2007
1108:Microarray databases
1087:Ocimum Bio Solutions
602:
320:transcription factor
42:), RNA, and protein
2279:2001PNAS...98.5116G
1888:"Ingenuity Systems"
1730:10.1109/TCBB.2013.9
472:Block Permutations
401:normal distribution
371:Stanford University
304:Gene Set Enrichment
265:Pattern recognition
2428:BMC Bioinformatics
2377:BMC Bioinformatics
2326:BMC Bioinformatics
1959:"Software - Broad"
1824:BMC Bioinformatics
1770:BMC Bioinformatics
1455:BMC Bioinformatics
967:
344:
308:Kolmogorov-Smirnov
295:
237:k-means clustering
231:K-means clustering
207:Euclidean distance
203:Manhattan distance
169:k-means clustering
135:
84:
68:
40:Gene chip analysis
33:
1702:978-0-387-29362-2
964:
929:
908:
890:
881:
843:
822:
798:
789:
783:
774:
738:
722:
710:
668:
653:
623:
545:Pattern discovery
355:Robert Tibshirani
216:Average linkage (
16:(Redirected from
2659:
2570:
2569:
2567:
2566:
2557:. Archived from
2551:
2545:
2544:
2542:
2541:
2531:
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2522:
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2511:
2505:
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2402:
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2368:
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2351:
2341:
2317:
2311:
2310:
2300:
2290:
2273:(9): 5116–5121.
2264:
2255:
2244:
2241:
2218:
2217:
2207:
2183:
2160:
2155:
2132:
2131:
2129:
2128:
2114:
2108:
2107:
2105:
2104:
2094:
2088:
2087:
2085:
2084:
2075:. Archived from
2069:
2063:
2062:
2060:
2059:
2050:. Archived from
2044:
2038:
2037:
2027:
1995:
1989:
1988:
1986:
1985:
1975:
1969:
1968:
1966:
1965:
1955:
1949:
1943:
1942:
1940:
1939:
1929:
1923:
1922:
1920:
1919:
1910:. Archived from
1904:
1898:
1897:
1895:
1894:
1884:
1878:
1877:
1874:biostat.ucsf.edu
1866:
1860:
1859:
1849:
1839:
1815:
1806:
1805:
1795:
1785:
1761:
1750:
1749:
1713:
1707:
1706:
1688:
1682:
1681:
1645:
1639:
1638:
1628:
1596:
1587:
1586:
1579:
1573:
1572:
1562:
1538:
1532:
1531:
1521:
1497:
1491:
1490:
1480:
1470:
1446:
1440:
1439:
1429:
1405:
1399:
1398:
1388:
1361:
1355:
1354:
1352:
1351:
1337:
1331:
1330:
1320:
1295:(8): 1375–1389.
1280:
1274:
1273:
1271:
1270:
1260:
1254:
1253:
1251:
1250:
1241:. Archived from
1235:
1229:
1228:
1226:
1225:
1215:
1209:
1208:
1206:
1205:
1193:
1187:
1186:
1176:
1166:
1157:(43): 15545–50.
1142:
976:
974:
973:
968:
966:
965:
963:
927:
906:
888:
879:
859:
841:
820:
796:
787:
781:
772:
736:
735:
734:
720:
708:
688:
666:
651:
621:
569:
564:
486:Response formats
105:local regression
21:
2667:
2666:
2662:
2661:
2660:
2658:
2657:
2656:
2637:
2636:
2628:(by Affymetrix)
2579:
2574:
2573:
2564:
2562:
2553:
2552:
2548:
2539:
2537:
2533:
2532:
2528:
2519:
2517:
2513:
2512:
2508:
2472:
2471:
2467:
2421:
2420:
2416:
2370:
2369:
2365:
2319:
2318:
2314:
2262:
2257:
2256:
2247:
2242:
2221:
2185:
2184:
2163:
2156:
2135:
2126:
2124:
2116:
2115:
2111:
2102:
2100:
2096:
2095:
2091:
2082:
2080:
2071:
2070:
2066:
2057:
2055:
2046:
2045:
2041:
1997:
1996:
1992:
1983:
1981:
1977:
1976:
1972:
1963:
1961:
1957:
1956:
1952:
1946:
1937:
1935:
1931:
1930:
1926:
1917:
1915:
1906:
1905:
1901:
1892:
1890:
1886:
1885:
1881:
1868:
1867:
1863:
1817:
1816:
1809:
1776:(Suppl 2): S2.
1763:
1762:
1753:
1715:
1714:
1710:
1703:
1690:
1689:
1685:
1662:10.1038/nbt1238
1650:Nat. Biotechnol
1647:
1646:
1642:
1617:10.1038/nbt1239
1605:Nat. Biotechnol
1598:
1597:
1590:
1581:
1580:
1576:
1540:
1539:
1535:
1499:
1498:
1494:
1448:
1447:
1443:
1407:
1406:
1402:
1363:
1362:
1358:
1349:
1347:
1339:
1338:
1334:
1282:
1281:
1277:
1268:
1266:
1262:
1261:
1257:
1248:
1246:
1237:
1236:
1232:
1223:
1221:
1217:
1216:
1212:
1203:
1201:
1195:
1194:
1190:
1144:
1143:
1136:
1131:
1118:Transcriptomics
1104:
1095:
1078:
1069:
1067:Quality control
1064:
1050:
1032:
1023:
1017:
1008:
860:
723:
689:
600:
599:
591:
584:
578:
554:
488:
452:
425:
389:
367:DNA microarrays
363:gene expression
337:
282:written in the
267:
239:
233:
191:distance matrix
183:
177:
161:
147:
138:Factor analysis
100:
73:
56:
23:
22:
15:
12:
11:
5:
2665:
2663:
2655:
2654:
2649:
2639:
2638:
2635:
2634:
2629:
2623:
2618:
2609:
2603:
2597:
2591:
2585:
2578:
2577:External links
2575:
2572:
2571:
2546:
2526:
2506:
2485:(18): 3683–5.
2479:Bioinformatics
2465:
2414:
2363:
2312:
2245:
2219:
2198:(5): 552–560.
2161:
2133:
2109:
2089:
2064:
2039:
1990:
1970:
1950:
1944:
1924:
1899:
1879:
1861:
1807:
1751:
1724:(4): 845–857.
1708:
1701:
1683:
1640:
1611:(9): 1151–61.
1588:
1574:
1553:(8): 943–949.
1547:Bioinformatics
1533:
1512:(13): i282–8.
1506:Bioinformatics
1492:
1441:
1414:Bioinformatics
1400:
1356:
1332:
1275:
1255:
1230:
1210:
1188:
1133:
1132:
1130:
1127:
1126:
1125:
1120:
1115:
1110:
1103:
1100:
1094:
1093:Spot filtering
1091:
1077:
1074:
1068:
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1063:
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1048:
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479:
478:
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470:
464:
460:
451:
448:
447:
446:
443:
439:
436:
433:
424:
423:Basic protocol
421:
391:for each gene
387:
336:
333:
325:Genevestigator
266:
263:
235:Main article:
232:
229:
225:
224:
221:
214:
179:Main article:
176:
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160:
157:
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99:
96:
72:
69:
55:
52:
24:
14:
13:
10:
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3:
2:
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2607:
2604:
2601:
2598:
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2592:
2589:
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2584:
2581:
2580:
2576:
2561:on 2009-11-24
2560:
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2547:
2536:
2530:
2527:
2516:
2510:
2507:
2502:
2498:
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2476:
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2466:
2461:
2457:
2452:
2447:
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2425:
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2406:
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2374:
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2355:
2350:
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2304:
2299:
2294:
2289:
2284:
2280:
2276:
2272:
2268:
2261:
2254:
2252:
2250:
2246:
2240:
2238:
2236:
2234:
2232:
2230:
2228:
2226:
2224:
2220:
2215:
2211:
2206:
2201:
2197:
2193:
2189:
2182:
2180:
2178:
2176:
2174:
2172:
2170:
2168:
2166:
2162:
2159:
2154:
2152:
2150:
2148:
2146:
2144:
2142:
2140:
2138:
2134:
2123:
2119:
2113:
2110:
2099:
2093:
2090:
2079:on 2011-08-17
2078:
2074:
2068:
2065:
2054:on 2007-07-05
2053:
2049:
2043:
2040:
2035:
2031:
2026:
2021:
2017:
2013:
2009:
2005:
2001:
1994:
1991:
1980:
1974:
1971:
1960:
1954:
1951:
1948:
1945:
1934:
1928:
1925:
1914:on 2007-12-30
1913:
1909:
1903:
1900:
1889:
1883:
1880:
1875:
1871:
1865:
1862:
1857:
1853:
1848:
1843:
1838:
1833:
1829:
1825:
1821:
1814:
1812:
1808:
1803:
1799:
1794:
1789:
1784:
1779:
1775:
1771:
1767:
1760:
1758:
1756:
1752:
1747:
1743:
1739:
1735:
1731:
1727:
1723:
1719:
1712:
1709:
1704:
1698:
1694:
1687:
1684:
1679:
1675:
1671:
1667:
1663:
1659:
1656:(9): 1162–9.
1655:
1651:
1644:
1641:
1636:
1632:
1627:
1622:
1618:
1614:
1610:
1606:
1602:
1595:
1593:
1589:
1584:
1578:
1575:
1570:
1566:
1561:
1556:
1552:
1548:
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1529:
1525:
1520:
1515:
1511:
1507:
1503:
1496:
1493:
1488:
1484:
1479:
1474:
1469:
1464:
1460:
1456:
1452:
1445:
1442:
1437:
1433:
1428:
1423:
1420:(2): 185–93.
1419:
1415:
1411:
1404:
1401:
1396:
1392:
1387:
1382:
1379:(2): 249–64.
1378:
1374:
1373:Biostatistics
1370:
1366:
1360:
1357:
1346:
1342:
1336:
1333:
1328:
1324:
1319:
1314:
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1290:
1286:
1279:
1276:
1265:
1259:
1256:
1244:
1240:
1234:
1231:
1220:
1214:
1211:
1199:
1192:
1189:
1184:
1180:
1175:
1170:
1165:
1160:
1156:
1152:
1148:
1141:
1139:
1135:
1128:
1124:
1121:
1119:
1116:
1114:
1111:
1109:
1106:
1105:
1101:
1099:
1092:
1090:
1088:
1084:
1075:
1073:
1066:
1061:
1056:
1053:
1046:
1043:
1040:
1037:
1034:
1033:
1029:
1019:
1013:
1012:
1010:
1009:
1005:
1000:
997:
994:
991:
988:
987:
986:
983:
981:
977:
724:
684:
597:
595:
590:
585:
575:
570:
568:
563:
558:
551:
546:
543:
540:
537:
534:
531:
528:
525:
520:
517:
514:
511:
510:
508:
505:
502:
499:
496:
493:
492:
491:
485:
483:
474:
473:
471:
468:
465:
461:
458:
454:
453:
449:
444:
440:
437:
434:
431:
427:
426:
422:
420:
418:
414:
410:
406:
402:
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383:
378:
376:
372:
368:
364:
360:
356:
352:
348:
341:
334:
332:
330:
326:
321:
317:
316:Gene Ontology
313:
309:
305:
301:
291:
287:
285:
281:
277:
273:
264:
262:
260:
256:
252:
248:
244:
238:
230:
228:
222:
219:
215:
212:
211:
210:
208:
204:
200:
196:
192:
188:
182:
174:
172:
170:
166:
158:
156:
153:
144:
142:
139:
131:
127:
124:
122:
118:
117:median polish
112:
110:
106:
97:
95:
93:
89:
81:
77:
70:
64:
60:
53:
51:
49:
45:
41:
37:
29:
19:
2563:. Retrieved
2559:the original
2549:
2538:. Retrieved
2529:
2518:. Retrieved
2509:
2482:
2478:
2468:
2431:
2427:
2417:
2380:
2376:
2366:
2329:
2325:
2315:
2270:
2266:
2195:
2191:
2125:. Retrieved
2121:
2112:
2101:. Retrieved
2092:
2081:. Retrieved
2077:the original
2067:
2056:. Retrieved
2052:the original
2042:
2007:
2003:
1993:
1982:. Retrieved
1973:
1962:. Retrieved
1953:
1947:
1936:. Retrieved
1927:
1916:. Retrieved
1912:the original
1902:
1891:. Retrieved
1882:
1873:
1864:
1827:
1823:
1773:
1769:
1721:
1717:
1711:
1692:
1686:
1653:
1649:
1643:
1608:
1604:
1577:
1550:
1546:
1536:
1509:
1505:
1495:
1458:
1454:
1444:
1417:
1413:
1403:
1376:
1372:
1365:Irizarry, RA
1359:
1348:. Retrieved
1344:
1335:
1292:
1288:
1278:
1267:. Retrieved
1258:
1247:. Retrieved
1243:the original
1233:
1222:. Retrieved
1213:
1202:. Retrieved
1191:
1154:
1150:
1096:
1079:
1070:
1030:SAM features
984:
980:Fold changes
979:
978:
598:
593:
588:
580:
573:
571:
559:
555:
544:
538:
532:
526:
518:
512:
506:
500:
495:Quantitative
494:
489:
481:
476:recommended;
467:Permutations
405:permutations
392:
385:
379:
346:
345:
296:
280:Bioconductor
268:
242:
240:
226:
184:
162:
148:
136:
125:
113:
101:
85:
57:
54:Introduction
39:
35:
34:
2647:Microarrays
2010:(r5): rs5.
2004:Sci. Signal
1089:(Genowiz).
539:Time course
450:Running SAM
359:Gilbert Chu
187:homogeneous
44:microarrays
2641:Categories
2565:2009-04-02
2540:2008-01-02
2520:2008-01-01
2127:2023-11-24
2103:2008-10-15
2083:2007-12-31
2058:2007-12-31
1984:2007-12-31
1964:2007-12-31
1938:2014-09-09
1918:2007-12-31
1893:2007-12-31
1830:(1): 497.
1350:2023-11-24
1289:Proteomics
1269:2008-01-01
1249:2008-01-02
1224:2008-01-02
1204:2007-12-26
1129:References
1123:Proteomics
1083:GeneSpring
527:Multiclass
442:Controller
430:microarray
417:Bonferroni
409:parametric
300:phenotypes
276:Moksiskaan
159:Clustering
88:Affymetrix
71:Techniques
2608:—software
2602:—software
2596:—software
2590:—software
2048:"DBI Web"
1345:MathWorks
1309:1615-9853
785:#
552:Algorithm
507:Two class
501:One class
375:R-package
329:neoplasms
259:k-medoids
251:k-medoids
2501:16076888
2460:15921534
2409:16872483
2358:17612399
2307:11309499
2214:17317331
2098:"RssGsc"
2034:23443684
1856:19038021
1802:24564555
1738:24334380
1670:17061323
1635:16964229
1569:16473874
1528:17646307
1487:21070630
1436:12538238
1395:12925520
1327:25690415
1183:16199517
1102:See also
533:Survival
513:Unpaired
428:Perform
247:centroid
2451:1173086
2434:: 129.
2400:1544358
2383:: 359.
2349:1931607
2332:: 242.
2275:Bibcode
2073:"SCOPE"
2025:3756668
1847:2632677
1793:4072854
1678:8192240
1626:3272078
1478:2998528
1461:: 553.
1318:4510819
1174:1239896
1085:), and
490:Types:
382:t-tests
312:GenBank
272:GenMAPP
255:k-means
152:t-tests
109:MA plot
92:Agilent
2499:
2458:
2448:
2407:
2397:
2356:
2346:
2305:
2295:
2212:
2032:
2022:
1870:"Home"
1854:
1844:
1800:
1790:
1746:760277
1744:
1736:
1699:
1676:
1668:
1633:
1623:
1567:
1526:
1485:
1475:
1434:
1393:
1325:
1315:
1307:
1181:
1171:
1006:Output
928:
907:
889:
880:
842:
821:
797:
788:
782:
773:
737:
721:
709:
667:
652:
622:
519:Paired
373:in an
167:, and
48:genome
2298:33173
2263:(PDF)
1742:S2CID
1674:S2CID
463:Sizes
413:ANOVA
349:is a
218:UPGMA
2497:PMID
2456:PMID
2405:PMID
2354:PMID
2303:PMID
2210:PMID
2030:PMID
1852:PMID
1798:PMID
1734:PMID
1697:ISBN
1666:PMID
1631:PMID
1565:PMID
1524:PMID
1483:PMID
1432:PMID
1391:PMID
1323:PMID
1305:ISSN
1179:PMID
586:. r
572:The
415:and
357:and
274:and
197:and
90:and
2614:in
2487:doi
2446:PMC
2436:doi
2395:PMC
2385:doi
2344:PMC
2334:doi
2293:PMC
2283:doi
2200:doi
2020:PMC
2012:doi
1842:PMC
1832:doi
1788:PMC
1778:doi
1726:doi
1658:doi
1621:PMC
1613:doi
1555:doi
1514:doi
1473:PMC
1463:doi
1422:doi
1381:doi
1313:PMC
1297:doi
1169:PMC
1159:doi
1155:102
205:or
2643::
2495:.
2483:21
2481:.
2477:.
2454:.
2444:.
2430:.
2426:.
2403:.
2393:.
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