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0489 -2.0773 0.7711 -0.0304 -0.764 ...## $ CHR : Factor w/ 24 levels ""chr1"",""chr10"",..: 1 1 1 1 1 1 1 1 1 1 ...## $ pos : int [1:439918] 15865 534242 710097 714177 720865 758829 763119 779995 805102 805338 ...## $ betafc: num [1:439918] 0.00039 -0.04534 0.01594 0.00251 -0.00869 ...## $ indfdr: num [1:439918] 0.994 0.565 0.872 0.997 0.873 ...## - attr(*, ""row.names"")= int [1:439918] 425663 55771 233635 431055 235233 185639 266099 7424 229446 345572 ...## - attr(*, ""class"")= chr ""annot"" Once we have the relevant statistics for the individual CpGs, we can then use the dmrcate function to combine them to identify differentially methylated regions. The main output table DMRs$results contains all of the regions found, along with Luminespib concentration their genomic annotations and p-values. DMRs Fleroxacin chr7...## Fitting chr8...## selleck products Fitting chr9...## Fitting chrX...## Fitting chrY...## Demarcating regions...## Done! head (DMRs$results) ## coord no.cpgs minfdr Stouffer## 457 chr17:57915665-57918682 12 4.957890e-91 6.639928e-10## 733 chr3:114012316-114012912 5 1.622885e-180 1.515378e-07## 469 chr17:74639731-74640078 6 9.516873e-90 1.527961e-07## 1069 chrX:49121205-49122718 6 6.753751e-84 2.936984e-07## 492 chr18:21452730-21453131 7 5.702319e-115 7.674943e-07## 186 chr10:135202522-135203200 6 1.465070e-65 7.918224e-07## maxbetafc meanbetafc## 457 0.3982862 0.3131611## 733 0.5434277 0.4251622## 469 -0.2528645 -0.1951904## 1069 0.4529088 0.3006242## 492 -0.3867474 -0.2546089## 186 0.2803157 0.2293419 As for the probe-wise analysis, it is advisable to visualise the results to ensure that they make sense. The regions can easily be viewed using the DMR.plot function provided in the DMRcate package ( Figure 10). Figure 10. DMRcate provides a function for plotting differentially methylated regions in their genomic context.