198:, 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:
65:
52:
17:
144:
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.
307:. 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
279:
329:
119:
971:(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.
964:
39: – 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.
103:
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
311:
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
129:
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.
47:
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
1086:
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
1069:
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
143:
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
138:
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
48:
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.
91:
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
286:
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
590:
464:
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
451:
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
358:, 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
1060:
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.
299:-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
545:
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.
258:
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,
430:
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
96:. 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
216:
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.
1705:
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".
396:
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
83:, 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.
959:{\displaystyle \mathrm {False\ discovery\ rate\ (FDR)={\frac {Median\ (or\ 90^{th}\ percentile)\ of\ \#\ of\ falsely\ called\ genes}{Number\ of\ genes\ called\ significant}}} }
267:
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
551:
556:
316:
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
987:
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
2177:"Integration of statistical inference methods and a novel control measure to improve sensitivity and specificity of data analysis in expression profiling studies"
536:— no explicit response parameter is specified; the user specifies eigengene (principal component) of the expression data and treats it as a quantitative response
1075:
2232:<Zhang, S. (2007). "A comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performance." BMC Bioinformatics 8: 230.
238:. 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
115:
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.
2543:
275:. The frequently cited SAM module and other microarray tools are available through Stanford University. Another set is available from Harvard and MIT.
242:) have been shown to produce good results for gene expression data (at least better than hierarchical clustering methods). Empirical comparisons of
183:
35:, which allow researchers to investigate the expression state of a large number of genes – in many cases, an organism's entire
1071:
2609:
1637:
Guo L, Lobenhofer EK, Wang C, et al. (2006). "Rat toxicogenomic study reveals analytical consistency across microarray platforms".
2146:
Chu, G., Narasimhan, B, Tibshirani, R, Tusher, V. "SAM "Significance
Analysis of Microarrays" Users Guide and technical document."
187:
68:
130:
According to the
Affycomp benchmark FARMS outperformed all other summarizations methods with respect to sensitivity and specificity.
1896:
1227:
2036:
1590:"The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements"
1689:
112:, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.
1101:
504:— measurement units are different in the two groups; e.g. control and treatment groups with samples from different patients
234:
groups. Grouping is done by minimizing the sum of the squares of distances between the data and the corresponding cluster
530:— each experimental units is measured at more than one time point; experimental units fall into a one or two class design
175:
2309:
Dinu, I. P.; JD; Mueller, T; Liu, Q; Adewale, AJ; Jhangri, GS; Einecke, G; Famulski, KS; Halloran, P; Yasui, Y. (2007).
510:— same experimental units are measured in the two groups; e.g. samples before and after treatment from the same patients
2640:
384:, which measures the strength of the relationship between gene expression and a response variable. This analysis uses
2620:
1807:
de Souto, Marcilio C. P.; Costa, Ivan G.; de Araujo, Daniel S. A.; Ludermir, Teresa B.; Schliep, Alexander (2008).
1003:
Positive gene set — higher expression of most genes in the gene set correlates with higher values of the phenotype
518:— more than two groups with each containing different experimental units; generalization of two class unpaired type
1009:
Negative gene set — lower expression of most genes in the gene set correlates with higher values of the phenotype
392:. The response variable describes and groups the data based on experimental conditions. In this method, repeated
108:. The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.
1329:
264:
2061:
2362:"Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data"
1399:"A comparison of normalization methods for high density oligonucleotide array data based on variance and bias"
397:
2614:
2547:
1491:"Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks"
1136:"Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles"
400:
assumptions about the distribution of individual genes. This is an advantage over other techniques (e.g.,
104:
that does not take advantage of these mismatch spots but still must summarize the perfect matches through
1353:
2571:
471:
the number of permutations is set by the user when imputing correct values for the data set to run SAM
64:
2604:
2594:
385:
20:
Example of an approximately 40,000 probe spotted oligo microarray with enlarged inset to show detail.
2040:
2572:
ArrayExplorer - Compare microarray side by side to find the one that best suits your research needs
1186:
363:
1358:"Exploration, normalization, and summaries of high density oligonucleotide array probe level data"
169:
153:
1043:
Can work with blocked design for when treatments are applied within different batches of arrays
272:
109:
152:
Clustering is a data mining technique used to group genes having similar expression patterns.
2635:
2263:
1096:
308:
2523:
1900:
421:
experiments — DNA microarray with oligo and cDNA primers, SNP arrays, protein arrays, etc.
8:
1231:
389:
370:
359:
292:
100:
of the data. MA plots can be produced using programs and languages such as R and MATLAB.
2267:
1356:; Hobbs, B; Collin, F; Beazer-Barclay, YD; Antonellis, KJ; Scherf, U; Speed, TP (2003).
250:, hierarchical methods and, different distance measures can be found in the literature.
2439:
2412:
2388:
2361:
2337:
2310:
2013:
1989:"Protein Complex-Based Analysis Framework for High-Throughput Data Sets. 6, rs5 (2013)"
1835:
1808:
1781:
1754:
1730:
1662:
1614:
1589:
1466:
1439:
1306:
1273:
1162:
1135:
296:
230:
K-means clustering is an algorithm for grouping genes or samples based on pattern into
225:
195:
157:
1415:
1398:
1184:
1024:
Data from Oligo or cDNA arrays, SNP array, protein arrays, etc. can be utilized in SAM
282:
Example of FunRich tool output. Image shows the result of comparing 4 different genes.
2600:
2576:
2485:
2464:"Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis"
2444:
2393:
2342:
2291:
2286:
2248:
2198:
2018:
1840:
1786:
1722:
1685:
1654:
1619:
1553:
1512:
1471:
1420:
1379:
1311:
1293:
1207:
1167:
343:
2480:
2463:
2413:"Considerations when using the significance analysis of microarrays (SAM) algorithm"
1548:
1531:
1507:
1490:
1374:
1357:
2475:
2434:
2424:
2383:
2373:
2332:
2322:
2281:
2271:
2188:
2008:
2000:
1830:
1820:
1776:
1766:
1714:
1666:
1646:
1609:
1601:
1543:
1502:
1461:
1451:
1410:
1369:
1301:
1285:
1157:
1147:
191:
93:
1734:
2249:"Significance analysis of microarrays applied to the ionizing radiation response"
2147:
1330:"Create intensity versus ratio scatter plot of microarray data - MATLAB mairplot"
1106:
351:
179:
126:
2503:
178:
clusters. Hierarchical clustering consists of two separate phases. Initially, a
1947:
1771:
1755:"On the selection of appropriate distances for gene expression data clustering"
434:
List
Differentially Expressed Genes (Positively and Negatively Expressed Genes)
355:
313:
2193:
2176:
2004:
1440:"Algorithm-driven Artifacts in median polish summarization of Microarray data"
2629:
1456:
1297:
369:
SAM identifies statistically significant genes by carrying out gene specific
304:
105:
2429:
2378:
2327:
1825:
1152:
1033:
Uses data permutation to estimates False Discovery Rate for multiple testing
448:
for academic and non-academic users after completion of a registration step.
51:
2489:
2448:
2397:
2346:
2295:
2276:
2202:
2022:
1844:
1790:
1726:
1682:
Bioinformatics and computational biology solutions using R and Bioconductor
1658:
1623:
1557:
1516:
1475:
1424:
1383:
1352:
1315:
1289:
1171:
990:
Estimate the false discovery rate based on expected versus observed values
455:
393:
268:
1272:
Gatto, Laurent; Breckels, Lisa M.; Naake, Thomas; Gibb, Sebastian (2015).
445:
2577:
FARMS - Factor Analysis for Robust Microarray Summarization, an R package
347:
2589:
ArrayMining.net - web-application for online analysis of microarray data
1718:
1036:
Reports local false discovery rate (the FDR for genes having a similar d
133:
1572:"Affycomp III: A Benchmark for Affymetrix GeneChip Expression Measures"
1111:
418:
405:
339:
182:
containing all the pairwise distances between the genes is calculated.
174:
Hierarchical clustering is a statistical method for finding relatively
76:
32:
2106:
317:
288:
247:
239:
27:
are used in interpreting the data generated from experiments on DNA (
2065:
1650:
1605:
1988:
1252:
235:
190:
are often used as dissimilarity estimates, but other methods, like
1046:
Can adjust threshold determining number of gene called significant
981:
For each permutation compute the ordered null (unaffected) scores
2086:
1753:
Jaskowiak, Pablo A; Campello, Ricardo JGB; Costa, Ivan G (2014).
1707:
IEEE/ACM Transactions on Computational Biology and Bioinformatics
984:
Plot the ordered test statistic against the expected null scores
300:
260:
243:
97:
80:
1858:
568:
constant is chosen to minimize the coefficient of variation of
140:
36:
1806:
524:— data of a time until an event (for example death or relapse)
16:
2588:
1987:
Vinayagam A, Hu Y, Kulkarni M, Roesel C, et al. (2013).
1809:"Clustering cancer gene expression data: a comparative study"
1571:
408:), which assume equal variance and/or independence of genes.
401:
323:
206:
1876:
1532:"A new summarization method for affymetrix probe level data"
1185:
Dr. Leming Shi, National Center for Toxicological Research.
1967:
1396:
1274:"Visualization of proteomics data using R and Bioconductor"
2311:"Improving gene set analysis of microarray data by SAM-GS"
1921:
1529:
550:
492:— tests whether the mean gene expression differs from zero
278:
122:
Flowchart showing how the MAS5 algorithm by Agilent works.
2582:
1986:
2615:
GeneChip® Expression Analysis-Data Analysis Fundamentals
1134:
Subramanian A, Tamayo P, Mootha VK, et al. (2005).
555:
424:
Input Expression Analysis in Microsoft Excel — see below
1488:
1437:
1133:
1050:
118:
1752:
1704:
1271:
1087:
expression value below a certain intensity threshold.
328:
1397:
Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003).
593:
134:
Identification of significant differential expression
212:
Complete linkage (maximum method, furthest neighbor)
2247:Tusher, V. G.; Tibshirani, R.; et al. (2001).
160:are widely used techniques in microarray analysis.
1253:"LIMMA Library: Linear Models for Microarray Data"
958:
354:are statistically significant. With the advent of
1636:
1027:Correlates expression data to clinical parameters
202:Single linkage (minimum method, nearest neighbor)
2627:
2583:StatsArray - Online Microarray Analysis Services
2359:
2308:
2246:
1748:
1746:
1744:
1438:Giorgi FM, Bolger AM, Lohse M, Usadel B (2010).
312:responds to a variety of experimental contexts.
86:
2256:Proceedings of the National Academy of Sciences
1489:Lim WK, Wang K, Lefebvre C, Califano A (2007).
581:is equal to the expression levels (x) for gene
2595:FunRich - Perform gene set enrichment analysis
2410:
1588:Shi L, Reid LH, Jones WD, et al. (2006).
1530:Hochreiter S, Clevert DA, Obermayer K (2006).
1741:
1698:
1684:. New York: Springer Science+Business Media.
1208:"GenUs BioSystems - Services - Data Analysis"
978:Order test statistics according to magnitude
458:are calculated based on the number of samples
55:The steps required in a microarray experiment
2461:
1802:
1800:
1587:
2107:"SAM: Significance Analysis of Microarrays"
1187:"MicroArray Quality Control (MAQC) Project"
303:and curated databases such as Biocarta and
342:, established in 2001 by Virginia Tusher,
336:Significance analysis of microarrays (SAM)
324:Significance analysis of microarrays (SAM)
163:
69:National Center for Toxicological Research
2479:
2438:
2428:
2387:
2377:
2360:Jeffery, I. H.; DG; Culhane, AC. (2006).
2336:
2326:
2285:
2275:
2192:
2012:
1922:"FunRich: Functional Enrichment Analysis"
1834:
1824:
1797:
1780:
1770:
1679:
1673:
1613:
1547:
1506:
1465:
1455:
1414:
1373:
1305:
1161:
1151:
1064:
444:SAM is available for download online at
327:
291:. One such method of analysis, known as
277:
117:
63:
50:
15:
2504:"J. Craig Venter Institute -- Software"
2411:Larsson, O. W. C; Timmons, JA. (2005).
2175:Zang, S.; Guo, R.; et al. (2007).
2174:
1680:Gentleman, Robert; et al. (2005).
446:http://www-stat.stanford.edu/~tibs/SAM/
75:Most microarray manufacturers, such as
2628:
2621:Duke data_analysis_fundamentals_manual
1968:"BioCarta - Charting Pathways of Life"
253:
2242:
2240:
2238:
2228:
2226:
2224:
2222:
2220:
2218:
2216:
2214:
2212:
2170:
2168:
2166:
2164:
2162:
2160:
2158:
2156:
2154:
2142:
2140:
2138:
2136:
2134:
2132:
2130:
2128:
2126:
1583:
1581:
350:, for determining whether changes in
219:
2601:Comparative Transcriptomics Analysis
2544:"Ocimum Biosolutions | Genowiz"
1129:
1127:
1102:Significance analysis of microarrays
1051:Error correction and quality control
1030:Correlates expression data with time
974:The SAM algorithm can be stated as:
427:Run SAM as a Microsoft Excel Add-Ins
1189:. U.S. Food and Drug Administration
474:
13:
2404:
2353:
2302:
2235:
2209:
2151:
2123:
1897:"Ariadne Genomics: Pathway Studio"
1578:
1055:
949:
946:
943:
940:
937:
934:
931:
928:
925:
922:
919:
913:
910:
907:
904:
901:
898:
892:
889:
886:
883:
880:
874:
871:
865:
862:
859:
856:
853:
850:
845:
842:
839:
836:
833:
827:
824:
821:
818:
815:
812:
806:
803:
800:
797:
794:
791:
788:
782:
779:
773:
767:
764:
755:
752:
749:
746:
743:
740:
737:
734:
731:
728:
720:
717:
706:
703:
694:
691:
688:
685:
682:
679:
667:
664:
661:
652:
649:
646:
643:
637:
634:
631:
628:
625:
622:
619:
616:
613:
607:
604:
601:
598:
595:
486:— real-valued (such as heart rate)
388:, since the data may not follow a
14:
2652:
2605:Reference Module in Life Sciences
2565:
2181:Journal of Biomedical Informatics
1124:
1081:
585:under y experimental conditions.
411:
71:scientist reviews microarray data
1228:"Agilent | DNA Microarrays"
554:
549:
2536:
2516:
2496:
2455:
2099:
2079:
2054:
2029:
1980:
1960:
1940:
1934:
1914:
1889:
1869:
1851:
1630:
1564:
1523:
1482:
1431:
1416:10.1093/bioinformatics/19.2.185
1018:
42:
2524:"Agilent | GeneSpring GX"
1390:
1346:
1322:
1265:
1245:
1220:
1200:
1178:
1070:available from TIGR, Agilent (
758:
700:
670:
658:
438:
25:Microarray analysis techniques
1:
2481:10.1093/bioinformatics/bti605
2462:Wilson CL, Miller CJ (2005).
1549:10.1093/bioinformatics/btl033
1508:10.1093/bioinformatics/btm201
1375:10.1093/biostatistics/4.2.249
1117:
1040:as that gene) and miss rates
147:
87:Aggregation and normalization
59:
1140:Proc. Natl. Acad. Sci. U.S.A
540:
7:
1090:
498:— two sets of measurements
10:
2657:
1772:10.1186/1471-2105-15-S2-S2
223:
167:
2641:Bioinformatics algorithms
2610:SAM download instructions
2194:10.1016/j.jbi.2007.01.002
2005:10.1126/scisignal.2003629
994:
386:non-parametric statistics
373:and computes a statistic
1457:10.1186/1471-2105-11-553
295:Analysis (GSEA), uses a
2430:10.1186/1471-2105-6-129
2379:10.1186/1471-2105-7-359
2328:10.1186/1471-2105-8-242
1826:10.1186/1471-2105-9-497
1153:10.1073/pnas.0506580102
170:Hierarchical clustering
164:Hierarchical clustering
154:Hierarchical clustering
2277:10.1073/pnas.091062498
1290:10.1002/pmic.201400392
1000:Significant gene sets
960:
332:
283:
273:R programming language
188:Spearman's correlation
123:
110:Quantile normalization
72:
56:
21:
2111:tibshirani.su.domains
1065:Background correction
961:
340:statistical technique
331:
281:
184:Pearson's correlation
121:
67:
54:
19:
1234:on December 22, 2007
1097:Microarray databases
1076:Ocimum Bio Solutions
591:
309:transcription factor
31:), RNA, and protein
2268:2001PNAS...98.5116G
1877:"Ingenuity Systems"
1719:10.1109/TCBB.2013.9
461:Block Permutations
390:normal distribution
360:Stanford University
293:Gene Set Enrichment
254:Pattern recognition
2417:BMC Bioinformatics
2366:BMC Bioinformatics
2315:BMC Bioinformatics
1948:"Software - Broad"
1813:BMC Bioinformatics
1759:BMC Bioinformatics
1444:BMC Bioinformatics
956:
333:
297:Kolmogorov-Smirnov
284:
226:k-means clustering
220:K-means clustering
196:Euclidean distance
192:Manhattan distance
158:k-means clustering
124:
73:
57:
29:Gene chip analysis
22:
1691:978-0-387-29362-2
953:
918:
897:
879:
870:
832:
811:
787:
778:
772:
763:
727:
711:
699:
657:
642:
612:
534:Pattern discovery
344:Robert Tibshirani
205:Average linkage (
2648:
2559:
2558:
2556:
2555:
2546:. Archived from
2540:
2534:
2533:
2531:
2530:
2520:
2514:
2513:
2511:
2510:
2500:
2494:
2493:
2483:
2459:
2453:
2452:
2442:
2432:
2408:
2402:
2401:
2391:
2381:
2357:
2351:
2350:
2340:
2330:
2306:
2300:
2299:
2289:
2279:
2262:(9): 5116–5121.
2253:
2244:
2233:
2230:
2207:
2206:
2196:
2172:
2149:
2144:
2121:
2120:
2118:
2117:
2103:
2097:
2096:
2094:
2093:
2083:
2077:
2076:
2074:
2073:
2064:. Archived from
2058:
2052:
2051:
2049:
2048:
2039:. Archived from
2033:
2027:
2026:
2016:
1984:
1978:
1977:
1975:
1974:
1964:
1958:
1957:
1955:
1954:
1944:
1938:
1932:
1931:
1929:
1928:
1918:
1912:
1911:
1909:
1908:
1899:. Archived from
1893:
1887:
1886:
1884:
1883:
1873:
1867:
1866:
1863:biostat.ucsf.edu
1855:
1849:
1848:
1838:
1828:
1804:
1795:
1794:
1784:
1774:
1750:
1739:
1738:
1702:
1696:
1695:
1677:
1671:
1670:
1634:
1628:
1627:
1617:
1585:
1576:
1575:
1568:
1562:
1561:
1551:
1527:
1521:
1520:
1510:
1486:
1480:
1479:
1469:
1459:
1435:
1429:
1428:
1418:
1394:
1388:
1387:
1377:
1350:
1344:
1343:
1341:
1340:
1326:
1320:
1319:
1309:
1284:(8): 1375–1389.
1269:
1263:
1262:
1260:
1259:
1249:
1243:
1242:
1240:
1239:
1230:. Archived from
1224:
1218:
1217:
1215:
1214:
1204:
1198:
1197:
1195:
1194:
1182:
1176:
1175:
1165:
1155:
1146:(43): 15545–50.
1131:
965:
963:
962:
957:
955:
954:
952:
916:
895:
877:
868:
848:
830:
809:
785:
776:
770:
761:
725:
724:
723:
709:
697:
677:
655:
640:
610:
558:
553:
475:Response formats
94:local regression
2656:
2655:
2651:
2650:
2649:
2647:
2646:
2645:
2626:
2625:
2617:(by Affymetrix)
2568:
2563:
2562:
2553:
2551:
2542:
2541:
2537:
2528:
2526:
2522:
2521:
2517:
2508:
2506:
2502:
2501:
2497:
2460:
2456:
2409:
2405:
2358:
2354:
2307:
2303:
2251:
2245:
2236:
2231:
2210:
2173:
2152:
2145:
2124:
2115:
2113:
2105:
2104:
2100:
2091:
2089:
2085:
2084:
2080:
2071:
2069:
2060:
2059:
2055:
2046:
2044:
2035:
2034:
2030:
1985:
1981:
1972:
1970:
1966:
1965:
1961:
1952:
1950:
1946:
1945:
1941:
1935:
1926:
1924:
1920:
1919:
1915:
1906:
1904:
1895:
1894:
1890:
1881:
1879:
1875:
1874:
1870:
1857:
1856:
1852:
1805:
1798:
1765:(Suppl 2): S2.
1751:
1742:
1703:
1699:
1692:
1678:
1674:
1651:10.1038/nbt1238
1639:Nat. Biotechnol
1635:
1631:
1606:10.1038/nbt1239
1594:Nat. Biotechnol
1586:
1579:
1570:
1569:
1565:
1528:
1524:
1487:
1483:
1436:
1432:
1395:
1391:
1351:
1347:
1338:
1336:
1328:
1327:
1323:
1270:
1266:
1257:
1255:
1251:
1250:
1246:
1237:
1235:
1226:
1225:
1221:
1212:
1210:
1206:
1205:
1201:
1192:
1190:
1183:
1179:
1132:
1125:
1120:
1107:Transcriptomics
1093:
1084:
1067:
1058:
1056:Quality control
1053:
1039:
1021:
1012:
1006:
997:
849:
716:
712:
678:
676:
594:
592:
589:
588:
580:
573:
567:
543:
477:
441:
414:
378:
356:DNA microarrays
352:gene expression
326:
271:written in the
256:
228:
222:
180:distance matrix
172:
166:
150:
136:
127:Factor analysis
89:
62:
45:
12:
11:
5:
2654:
2644:
2643:
2638:
2624:
2623:
2618:
2612:
2607:
2598:
2592:
2586:
2580:
2574:
2567:
2566:External links
2564:
2561:
2560:
2535:
2515:
2495:
2474:(18): 3683–5.
2468:Bioinformatics
2454:
2403:
2352:
2301:
2234:
2208:
2187:(5): 552–560.
2150:
2122:
2098:
2078:
2053:
2028:
1979:
1959:
1939:
1933:
1913:
1888:
1868:
1850:
1796:
1740:
1713:(4): 845–857.
1697:
1690:
1672:
1629:
1600:(9): 1151–61.
1577:
1563:
1542:(8): 943–949.
1536:Bioinformatics
1522:
1501:(13): i282–8.
1495:Bioinformatics
1481:
1430:
1403:Bioinformatics
1389:
1345:
1321:
1264:
1244:
1219:
1199:
1177:
1122:
1121:
1119:
1116:
1115:
1114:
1109:
1104:
1099:
1092:
1089:
1083:
1082:Spot filtering
1080:
1066:
1063:
1057:
1054:
1052:
1049:
1048:
1047:
1044:
1041:
1037:
1034:
1031:
1028:
1025:
1020:
1017:
1016:
1015:
1014:
1013:
1010:
1007:
1004:
996:
993:
992:
991:
988:
985:
982:
979:
951:
948:
945:
942:
939:
936:
933:
930:
927:
924:
921:
915:
912:
909:
906:
903:
900:
894:
891:
888:
885:
882:
876:
873:
867:
864:
861:
858:
855:
852:
847:
844:
841:
838:
835:
829:
826:
823:
820:
817:
814:
808:
805:
802:
799:
796:
793:
790:
784:
781:
775:
769:
766:
760:
757:
754:
751:
748:
745:
742:
739:
736:
733:
730:
722:
719:
715:
708:
705:
702:
696:
693:
690:
687:
684:
681:
675:
672:
669:
666:
663:
660:
654:
651:
648:
645:
639:
636:
633:
630:
627:
624:
621:
618:
615:
609:
606:
603:
600:
597:
576:
571:
565:
542:
539:
538:
537:
531:
525:
519:
513:
512:
511:
505:
493:
487:
476:
473:
469:
468:
467:
466:
459:
453:
449:
440:
437:
436:
435:
432:
428:
425:
422:
413:
412:Basic protocol
410:
380:for each gene
376:
325:
322:
314:Genevestigator
255:
252:
224:Main article:
221:
218:
214:
213:
210:
203:
168:Main article:
165:
162:
149:
146:
135:
132:
88:
85:
61:
58:
44:
41:
9:
6:
4:
3:
2:
2653:
2642:
2639:
2637:
2634:
2633:
2631:
2622:
2619:
2616:
2613:
2611:
2608:
2606:
2602:
2599:
2596:
2593:
2590:
2587:
2584:
2581:
2578:
2575:
2573:
2570:
2569:
2550:on 2009-11-24
2549:
2545:
2539:
2525:
2519:
2505:
2499:
2491:
2487:
2482:
2477:
2473:
2469:
2465:
2458:
2450:
2446:
2441:
2436:
2431:
2426:
2422:
2418:
2414:
2407:
2399:
2395:
2390:
2385:
2380:
2375:
2371:
2367:
2363:
2356:
2348:
2344:
2339:
2334:
2329:
2324:
2320:
2316:
2312:
2305:
2297:
2293:
2288:
2283:
2278:
2273:
2269:
2265:
2261:
2257:
2250:
2243:
2241:
2239:
2229:
2227:
2225:
2223:
2221:
2219:
2217:
2215:
2213:
2204:
2200:
2195:
2190:
2186:
2182:
2178:
2171:
2169:
2167:
2165:
2163:
2161:
2159:
2157:
2155:
2148:
2143:
2141:
2139:
2137:
2135:
2133:
2131:
2129:
2127:
2112:
2108:
2102:
2088:
2082:
2068:on 2011-08-17
2067:
2063:
2057:
2043:on 2007-07-05
2042:
2038:
2032:
2024:
2020:
2015:
2010:
2006:
2002:
1998:
1994:
1990:
1983:
1969:
1963:
1949:
1943:
1937:
1923:
1917:
1903:on 2007-12-30
1902:
1898:
1892:
1878:
1872:
1864:
1860:
1854:
1846:
1842:
1837:
1832:
1827:
1822:
1818:
1814:
1810:
1803:
1801:
1792:
1788:
1783:
1778:
1773:
1768:
1764:
1760:
1756:
1749:
1747:
1745:
1736:
1732:
1728:
1724:
1720:
1716:
1712:
1708:
1701:
1693:
1687:
1683:
1676:
1668:
1664:
1660:
1656:
1652:
1648:
1645:(9): 1162–9.
1644:
1640:
1633:
1625:
1621:
1616:
1611:
1607:
1603:
1599:
1595:
1591:
1584:
1582:
1573:
1567:
1559:
1555:
1550:
1545:
1541:
1537:
1533:
1526:
1518:
1514:
1509:
1504:
1500:
1496:
1492:
1485:
1477:
1473:
1468:
1463:
1458:
1453:
1449:
1445:
1441:
1434:
1426:
1422:
1417:
1412:
1409:(2): 185–93.
1408:
1404:
1400:
1393:
1385:
1381:
1376:
1371:
1368:(2): 249–64.
1367:
1363:
1362:Biostatistics
1359:
1355:
1349:
1335:
1331:
1325:
1317:
1313:
1308:
1303:
1299:
1295:
1291:
1287:
1283:
1279:
1275:
1268:
1254:
1248:
1233:
1229:
1223:
1209:
1203:
1188:
1181:
1173:
1169:
1164:
1159:
1154:
1149:
1145:
1141:
1137:
1130:
1128:
1123:
1113:
1110:
1108:
1105:
1103:
1100:
1098:
1095:
1094:
1088:
1079:
1077:
1073:
1062:
1045:
1042:
1035:
1032:
1029:
1026:
1023:
1022:
1008:
1002:
1001:
999:
998:
989:
986:
983:
980:
977:
976:
975:
972:
970:
966:
713:
673:
586:
584:
579:
574:
564:
559:
557:
552:
547:
535:
532:
529:
526:
523:
520:
517:
514:
509:
506:
503:
500:
499:
497:
494:
491:
488:
485:
482:
481:
480:
472:
463:
462:
460:
457:
454:
450:
447:
443:
442:
433:
429:
426:
423:
420:
416:
415:
409:
407:
403:
399:
395:
391:
387:
383:
379:
372:
367:
365:
361:
357:
353:
349:
345:
341:
337:
330:
321:
319:
315:
310:
306:
305:Gene Ontology
302:
298:
294:
290:
280:
276:
274:
270:
266:
262:
251:
249:
245:
241:
237:
233:
227:
217:
211:
208:
204:
201:
200:
199:
197:
193:
189:
185:
181:
177:
171:
161:
159:
155:
145:
142:
131:
128:
120:
116:
113:
111:
107:
106:median polish
101:
99:
95:
84:
82:
78:
70:
66:
53:
49:
40:
38:
34:
30:
26:
18:
2552:. Retrieved
2548:the original
2538:
2527:. Retrieved
2518:
2507:. Retrieved
2498:
2471:
2467:
2457:
2420:
2416:
2406:
2369:
2365:
2355:
2318:
2314:
2304:
2259:
2255:
2184:
2180:
2114:. Retrieved
2110:
2101:
2090:. Retrieved
2081:
2070:. Retrieved
2066:the original
2056:
2045:. Retrieved
2041:the original
2031:
1996:
1992:
1982:
1971:. Retrieved
1962:
1951:. Retrieved
1942:
1936:
1925:. Retrieved
1916:
1905:. Retrieved
1901:the original
1891:
1880:. Retrieved
1871:
1862:
1853:
1816:
1812:
1762:
1758:
1710:
1706:
1700:
1681:
1675:
1642:
1638:
1632:
1597:
1593:
1566:
1539:
1535:
1525:
1498:
1494:
1484:
1447:
1443:
1433:
1406:
1402:
1392:
1365:
1361:
1354:Irizarry, RA
1348:
1337:. Retrieved
1333:
1324:
1281:
1277:
1267:
1256:. Retrieved
1247:
1236:. Retrieved
1232:the original
1222:
1211:. Retrieved
1202:
1191:. Retrieved
1180:
1143:
1139:
1085:
1068:
1059:
1019:SAM features
973:
969:Fold changes
968:
967:
587:
582:
577:
569:
562:
560:
548:
544:
533:
527:
521:
515:
507:
501:
495:
489:
484:Quantitative
483:
478:
470:
465:recommended;
456:Permutations
394:permutations
381:
374:
368:
335:
334:
285:
269:Bioconductor
257:
231:
229:
215:
173:
151:
137:
125:
114:
102:
90:
74:
46:
43:Introduction
28:
24:
23:
2636:Microarrays
1999:(r5): rs5.
1993:Sci. Signal
1078:(Genowiz).
528:Time course
439:Running SAM
348:Gilbert Chu
176:homogeneous
33:microarrays
2630:Categories
2554:2009-04-02
2529:2008-01-02
2509:2008-01-01
2116:2023-11-24
2092:2008-10-15
2072:2007-12-31
2047:2007-12-31
1973:2007-12-31
1953:2007-12-31
1927:2014-09-09
1907:2007-12-31
1882:2007-12-31
1819:(1): 497.
1339:2023-11-24
1278:Proteomics
1258:2008-01-01
1238:2008-01-02
1213:2008-01-02
1193:2007-12-26
1118:References
1112:Proteomics
1072:GeneSpring
516:Multiclass
431:Controller
419:microarray
406:Bonferroni
398:parametric
289:phenotypes
265:Moksiskaan
148:Clustering
77:Affymetrix
60:Techniques
2597:—software
2591:—software
2585:—software
2579:—software
2037:"DBI Web"
1334:MathWorks
1298:1615-9853
774:#
541:Algorithm
496:Two class
490:One class
364:R-package
318:neoplasms
248:k-medoids
240:k-medoids
2490:16076888
2449:15921534
2398:16872483
2347:17612399
2296:11309499
2203:17317331
2087:"RssGsc"
2023:23443684
1845:19038021
1791:24564555
1727:24334380
1659:17061323
1624:16964229
1558:16473874
1517:17646307
1476:21070630
1425:12538238
1384:12925520
1316:25690415
1172:16199517
1091:See also
522:Survival
502:Unpaired
417:Perform
236:centroid
2440:1173086
2423:: 129.
2389:1544358
2372:: 359.
2338:1931607
2321:: 242.
2264:Bibcode
2062:"SCOPE"
2014:3756668
1836:2632677
1782:4072854
1667:8192240
1615:3272078
1467:2998528
1450:: 553.
1307:4510819
1163:1239896
1074:), and
479:Types:
371:t-tests
301:GenBank
261:GenMAPP
244:k-means
141:t-tests
98:MA plot
81:Agilent
2488:
2447:
2437:
2396:
2386:
2345:
2335:
2294:
2284:
2201:
2021:
2011:
1859:"Home"
1843:
1833:
1789:
1779:
1735:760277
1733:
1725:
1688:
1665:
1657:
1622:
1612:
1556:
1515:
1474:
1464:
1423:
1382:
1314:
1304:
1296:
1170:
1160:
995:Output
917:
896:
878:
869:
831:
810:
786:
777:
771:
762:
726:
710:
698:
656:
641:
611:
508:Paired
362:in an
156:, and
37:genome
2287:33173
2252:(PDF)
1731:S2CID
1663:S2CID
452:Sizes
402:ANOVA
338:is a
207:UPGMA
2486:PMID
2445:PMID
2394:PMID
2343:PMID
2292:PMID
2199:PMID
2019:PMID
1841:PMID
1787:PMID
1723:PMID
1686:ISBN
1655:PMID
1620:PMID
1554:PMID
1513:PMID
1472:PMID
1421:PMID
1380:PMID
1312:PMID
1294:ISSN
1168:PMID
575:. r
561:The
404:and
346:and
263:and
186:and
79:and
2603:in
2476:doi
2435:PMC
2425:doi
2384:PMC
2374:doi
2333:PMC
2323:doi
2282:PMC
2272:doi
2189:doi
2009:PMC
2001:doi
1831:PMC
1821:doi
1777:PMC
1767:doi
1715:doi
1647:doi
1610:PMC
1602:doi
1544:doi
1503:doi
1462:PMC
1452:doi
1411:doi
1370:doi
1302:PMC
1286:doi
1158:PMC
1148:doi
1144:102
194:or
2632::
2484:.
2472:21
2470:.
2466:.
2443:.
2433:.
2419:.
2415:.
2392:.
2382:.
2368:.
2364:.
2341:.
2331:.
2317:.
2313:.
2290:.
2280:.
2270:.
2260:98
2258:.
2254:.
2237:^
2211:^
2197:.
2185:40
2183:.
2179:.
2153:^
2125:^
2109:.
2017:.
2007:.
1995:.
1991:.
1861:.
1839:.
1829:.
1815:.
1811:.
1799:^
1785:.
1775:.
1763:15
1761:.
1757:.
1743:^
1729:.
1721:.
1711:10
1709:.
1661:.
1653:.
1643:24
1641:.
1618:.
1608:.
1598:24
1596:.
1592:.
1580:^
1552:.
1540:22
1538:.
1534:.
1511:.
1499:23
1497:.
1493:.
1470:.
1460:.
1448:11
1446:.
1442:.
1419:.
1407:19
1405:.
1401:.
1378:.
1364:.
1360:.
1332:.
1310:.
1300:.
1292:.
1282:15
1280:.
1276:.
1166:.
1156:.
1142:.
1138:.
1126:^
714:90
366:.
320:.
246:,
2557:.
2532:.
2512:.
2492:.
2478::
2451:.
2427::
2421:6
2400:.
2376::
2370:7
2349:.
2325::
2319:8
2298:.
2274::
2266::
2205:.
2191::
2119:.
2095:.
2075:.
2050:.
2025:.
2003::
1997:6
1976:.
1956:.
1930:.
1910:.
1885:.
1865:.
1847:.
1823::
1817:9
1793:.
1769::
1737:.
1717::
1694:.
1669:.
1649::
1626:.
1604::
1574:.
1560:.
1546::
1519:.
1505::
1478:.
1454::
1427:.
1413::
1386:.
1372::
1366:4
1342:.
1318:.
1288::
1261:.
1241:.
1216:.
1196:.
1174:.
1150::
1038:i
1011:y
1005:y
950:t
947:n
944:a
941:c
938:i
935:f
932:i
929:n
926:g
923:i
920:s
914:d
911:e
908:l
905:l
902:a
899:c
893:s
890:e
887:n
884:e
881:g
875:f
872:o
866:r
863:e
860:b
857:m
854:u
851:N
846:s
843:e
840:n
837:e
834:g
828:d
825:e
822:l
819:l
816:a
813:c
807:y
804:l
801:e
798:s
795:l
792:a
789:f
783:f
780:o
768:f
765:o
759:)
756:e
753:l
750:i
747:t
744:n
741:e
738:c
735:r
732:e
729:p
721:h
718:t
707:r
704:o
701:(
695:n
692:a
689:i
686:d
683:e
680:M
674:=
671:)
668:R
665:D
662:F
659:(
653:e
650:t
647:a
644:r
638:y
635:r
632:e
629:v
626:o
623:c
620:s
617:i
614:d
608:e
605:s
602:l
599:a
596:F
583:i
578:i
572:i
570:d
566:o
563:s
382:j
377:j
375:d
232:K
209:)
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
_(6009042166).jpg)
