下面的数据显示了我的数据集中我的差异表达基因的一个子集。这些基因在整个时间过程中发生了很大的变化。在热图中显示这些变化的最佳方式是什么？目前我按时使用了聚类，但这没有意义，因为时间点的顺序发生了变化。
data <- scale(t(data))
ord <- hclust( dist(data, method = "euclidean"), method = "ward.D" )$order
ord
pd <- as.data.frame( data )
pd$Time <- sub("_.*", "", rownames(pd))
pd.m <- melt( pd, id.vars = "Time", variable.name = "Gene" )
pd.m$Gene <- factor( pd.m$Gene, levels = colnames(data), labels = seq_along( colnames(data)))
pd.m$Time <- factor( pd.m$Time, levels = rownames(data)[ord], labels = c("0h", "0.25h", "0.5h","1h","2h","3h","6h","12h","24h","48h") )
ggplot( pd.m, aes(Time, Gene) ) +
geom_tile(aes(fill = value)) +
scale_fill_gradient2(low=muted("blue"), high=muted("red"))+
theme_bw(base_size=35)
>data
T0h T0.25h T0.5h T1h T2h T3h T6h T12h T24h T48h
Zfp36 1.4865812288 2.6043878378 4.8689927391 4.1300412283 0.7450087968 0.7713905325 0.8124991048 0.8125375813 0.8485126972 1.1435088656
Zfp280b 0.9944961671 1.0700016622 1.0103583427 1.1964824049 1.7168366859 2.0090685521 3.032261758 3.1992004436 2.8816123816 2.9532484654
Wnt10a 0.9666023491 1.0162380083 0.9614756075 0.9030372853 0.7296350831 0.5100091305 0.3281906511 0.1929538685 0.3161538323 0.4472075971
Tubb6 0.773026973 0.6863716312 1.0567880048 1.2530635772 1.8387545499 1.798876894 1.9228454894 2.1432050761 3.3566166185 4.4095121951
Traf1 0.9176444999 1.021628435 1.0986947079 1.7444983776 3.438084831 3.224964476 3.995648176 2.3820049673 3.9615498278 3.8126049821
Tnnt2 1.0469740386 1.0943645555 1.1985950576 1.1656448198 1.3443223506 1.4115769544 2.5978415439 5.1319084185 9.8189312038 9.457881731
Tnni1 0.9864693701 1.0785384292 1.1476357238 1.0349459833 1.567381317 2.2025710419 3.4358495267 5.103047655 9.985488919 20.0192136587
Tnfrsf4 1.2492930798 1.0162603212 0.9605667001 0.9468147908 1.6461500799 1.594368932 2.0316281031 2.4524863296 9.2491614825 28.3656565537
Tnfaip3 1.2442369707 1.3314047193 3.6147375395 7.7354847705 2.6055600198 1.921362861 1.6675192174 1.6919707466 2.9086955296 1.8126504397
Tmsb15l 1.0437754712 1.5097793496 0.9964473482 0.9451662922 0.9946716693 1.0881863157 2.5481610656 4.578915281 9.6648576979 7.7858353113
Tmsb15b1 1.4689646936 1.0970619838 1.0543473567 0.9996272565 0.7878169695 1.1232652928 1.5810437179 3.5429329874 5.3806633953 4.7922381632
Tgfbr1 1.0575736219 1.0762901809 1.050321571 1.0055099796 1.3902478839 2.1355628325 1.9951348253 2.2676079884 2.3157728453 1.589314888
Tcf7 0.9186328927 1.3113061292 1.1304477795 0.9620289023 1.2755503226 1.6071567601 3.9608176211 7.5981324706 16.8025400932 11.8166138542
Tbc1d4 1.0514882242 1.1565754337 1.0985017304 1.3477676003 1.8445440671 2.3163671771 4.1501027841 7.7829729056 15.9883991423 9.8701882178
Stx7 1.0583686828 1.1959983862 1.0899016216 0.9756041294 1.0718347332 1.2894210564 2.0716932521 2.8584645772 2.9309124833 1.9368737651
Stat5a 0.622421436 1.2521011743 1.0890858469 1.1310147162 3.2359526598 6.261309674 4.7429003302 6.5539420691 4.030914972 3.2573424026
Stat1 1.0515240236 1.061443807 1.0102023121 1.294762475 1.7174431097 1.9247333419 2.5372727928 2.8181673501 4.1226857746 3.9881069864
Snora43 1.0456584952 0.637303406 0.4244687671 0.534207782 1.1080780771 0.4979388136 0.7182666819 0.8479585974 0.527605895 0.4454108518
Snora17 1.3668421991 0.4191899548 1.3344891971 0.7605709565 0.524177884 0.0685795565 0.4258424626 0.9716807859 1.0057806377 0.3370142736
Smim3 1.1147162 1.1074677495 1.0727113328 2.0634746855 2.3686930467 2.6375777601 3.4050944877 3.2762517395 3.117333758 2.1349005833
Slfn2 0.9734016936 0.8843688943 1.2054198469 2.2124164999 3.5752356576 3.166019186 1.8238585775 1.1366382465 1.2227102787 1.2364190381
Slc31a2 1.0410994382 1.0986653942 1.0315848391 1.0187033707 1.9513304164 2.3268717859 3.3292186817 2.9822086017 4.4204752548 5.2098934767
Slc2a6 0.983239298 0.9374693065 1.0584895405 0.8965611911 1.414544752 1.7199248254 3.7959318834 5.0964959369 6.4818150501 3.9822057745
Sik1 0.9325762509 1.1444413482 1.4482955438 2.9840642789 1.9395469378 1.8732216597 1.8568949551 1.3288510767 2.5299688789 1.9844465836
Samsn1 1.1025210084 1.2018873338 1.1709091433 1.4264952662 3.4340577537 3.2225597653 3.4763743131 2.9845601436 3.9763520761 4.3593453909
Rpl35a 1.3612486578 1.3138228005 1.4851049083 1.365775112 1.0658495107 1.1957021314 1.0383633938 1.1635873539 1.2068327983 1.1114226948
Rnf24 1.0481455978 0.9843012465 0.9766833202 2.8101081895 4.3449285295 3.4819045146 2.9533494556 3.2399315529 4.8043257117 4.6027138104
Rnf19b 0.9825699431 1.1572674659 1.6142324793 4.4559780259 6.2028815276 5.2901600077 7.1300136799 9.8172970292 11.2631004948 7.9485767529
Rhof 0.9617198024 0.8346538473 1.082632788 1.5272740126 2.2429413199 2.2529688429 2.0781265694 1.5635142555 1.5837230158 1.8384527521
Rhob 1.0503455128 1.3516933203 1.5627471133 1.6921539751 1.3248423504 1.1680815599 1.1715931918 1.1635789649 1.7950699546 2.0622614847
Rab27a 1.06700291 1.166797397 1.0183202477 1.0608133391 1.070299815 1.1137665866 2.1832486646 2.5072311162 3.3523224479 4.2658624198
Rab21 1.089920477 1.0830468487 1.0365556777 1.1241175934 1.125720552 1.0408736284 1.672449883 1.856593373 2.319063034 2.2512076227
我想要这样的东西：
