R图被裁剪

时间:2016-07-07 15:31:15

标签: r heatmap

这是一个基本问题,你会认为R总会自动化。我正在制作一个热图(基本的东西,png()到heatmap.2()到dev.off()),我的行标签在右侧被切掉了,还有一小部分的树状图。左:

there's supposed to be five numbers after the final letter of the labels

手动调整边距最终会产生合理的输出,但问题在于这是考虑到生物学家用户的自动化管道的一部分。当然有一些自动化的方法,对吧?我的意思是,在OSX上的RGui中,图形窗口的快速重塑会自动修复布局,因此必须可行。谢谢,很抱歉打扰你。

编辑:重现此可视化的步骤:

输入数据:

Time,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48
CATMA1A23000,0.21129,0.48322,0.54234,0.93714,0.51816,0.11103,0.38304,0.97183,0.61381,-0.13775,0.32575,0.85221,0.1921,1.8692,0.4117,0.51977,0.74019,0.98932,0.023536,0.8849,0.75141,0.42846,0.03839,0.50295,0.38849,-0.95087,0.55017,-0.14813,0.79036,0.40978,-0.33048,0.81173,0.60585,0.014413,0.86586,0.063947,1.1195,0.25425,-0.090325,1.3035,0.90609,0.62866,0.83168,0.88655,0.4843,0.60098,0.26669,0.66605,0.78468,-0.76709,0.29615,-0.8911,0.5022,-0.93834,-0.065904,0.58003,0.41217,-0.99012,-0.032054,0.69119,1.1791,-0.0059458,0.81002,0.76421,1.302,0.77637,0.7824,1.192,-0.27005,-0.24698,1.8706,0.33513,0.38592,1.1987,0.48569,0.7104,2.4307,0.68148,0.11407,0.65619,0.94468,1.245,0.44146,0.71048,0.96389,1.7647,0.22729,0.96875,-0.2738,1.2975,2.0503,0.52507,2.282,1.0773,0.44489,0.22504
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CATMA2C47274,2.2048,1.6574,0.96502,0.15092,0.5251,0.40351,0.90106,-0.12415,0.48846,0.03794,0.86292,0.27027,0.71848,1.8535,0.47188,-0.067504,0.68578,0.28113,0.17559,0.29659,0.067618,0.79368,-0.36732,0.80945,2.4297,1.3272,1.3741,0.005024,0.57369,0.6195,0.17845,1.3991,0.71594,-0.76358,0.74421,0.078431,0.97596,0.14151,-0.77085,0.64926,-0.24602,1.3478,0.49879,0.90549,0.78024,0.94685,0.023589,0.07446,1.3597,0.19311,1.1883,-0.20756,0.59042,0.031659,0.3591,1.1757,0.97314,-1.0133,0.92409,1.2264,-0.0095851,-0.47315,1.3107,1.3967,0.80332,0.5494,1.0394,1.4519,0.50253,-0.65282,1.5689,0.17996,1.5938,0.28124,0.93511,0.10344,3.6906,0.56448,-0.40754,0.75218,0.10346,-0.37737,-0.53789,0.57109,0.29602,-0.64821,-0.52805,0.34899,0.50146,0.61456,2.1785,-0.41,2.135,0.7426,0.3307,0.64542
CATMA2C47710,3.9021,3.7272,3.709,3.494,3.7557,3.7883,3.8697,3.4952,3.6365,3.6948,4.0954,3.7629,4.1282,3.6108,4.2769,3.3689,3.5232,3.7488,3.6237,4.0805,3.855,3.8724,4.2706,3.5584,3.9576,4.1324,3.857,3.6508,3.7609,3.7885,3.6256,3.8022,3.7189,3.7417,3.6357,3.6448,4.2841,3.8731,3.6047,3.7792,3.5703,3.8284,3.8098,3.8824,4.2441,3.6127,3.8253,3.529,3.6078,4.0917,3.6794,3.7977,3.8231,3.9369,3.6087,3.7634,3.6419,4.0052,3.7083,4.0191,3.3211,3.9133,3.74,4.0801,4.0999,3.6312,3.7747,3.7768,3.7977,3.9409,3.9423,3.6708,4.1773,3.666,3.8267,3.8832,4.0462,3.8007,3.3932,4.0945,3.9901,4.1036,4.1064,3.8178,3.7936,3.8638,4.3395,3.5859,3.5698,3.7885,4.4263,4.0292,3.6857,4.1197,4.0262,3.7753
CATMA3A13690,5.2022,5.6481,5.7765,4.8253,6.0581,6.2026,5.2711,6.4481,6.0929,6.3395,5.3806,4.9156,5.3634,4.5556,5.3547,5.5214,5.6947,5.9289,5.6269,5.5406,6.0508,5.8498,5.6275,5.4422,5.3293,5.7543,5.1577,6.3626,5.429,5.7245,5.7554,5.4575,5.2244,6.2312,6.0425,5.0317,5.4494,5.5252,5.5124,5.6588,5.0015,5.7454,5.6711,6.132,5.4789,6.1668,5.6978,5.113,4.3572,5.6177,5.9557,6.0099,5.2522,5.5598,6.2427,5.4703,5.4275,5.9159,5.3029,4.1094,5.927,5.9223,5.1257,5.3835,5.6579,5.5849,5.8674,4.9425,5.2144,5.5382,4.7165,5.5688,5.2766,6.4298,5.9438,5.7105,4.757,5.9097,5.3665,5.8336,5.8393,5.6104,5.4702,5.4028,5.0756,5.632,5.9963,5.7889,5.7521,5.5561,4.0685,6.3026,4.9628,6.176,5.7256,4.8529
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CATMA5A09500,6.2838,5.1076,6.6209,5.7153,5.9395,5.3601,4.2366,5.189,3.3074,4.8701,5.0357,5.0951,6.2843,5.691,5.5054,5.9949,5.0834,5.1484,4.5884,4.805,4.458,5.0816,4.7264,5.3661,5.0645,5.8293,5.2877,5.6602,5.3336,4.7329,4.843,5.1901,3.8431,4.683,5.7106,4.0774,6.5689,5.8061,5.3445,5.2905,3.837,4.6271,4.2277,4.6165,4.0081,4.5176,4.259,4.6363,4.8557,3.9828,5.9728,5.4698,5.4102,4.5802,5.0247,4.8967,4.0116,2.6636,5.4684,4.3454,6.6893,6.5624,6.1464,5.4444,5.3309,5.6682,5.5012,4.3162,2.9028,3.3906,4.6684,4.914,5.3601,5.5138,6.2191,5.8043,5.8165,5.1155,3.8681,4.9268,4.0232,6.1699,4.6327,5.7364,6.1934,6.7188,5.0755,5.7058,5.3703,4.3921,3.2108,4.9503,4.1003,4.8225,4.6701,4.7166
CATMA5A12680,1.7891,1.362,1.4303,0.61973,0.54124,1.2733,0.7775,0.66788,1.7338,0.63125,1.5852,0.91616,1.0712,0.81919,0.90152,0.77799,1.0835,0.32735,2.0868,0.5895,1.1445,0.64545,0.71776,0.33929,1.42,1.0729,1.4879,0.86379,0.95453,1.2928,0.97753,1.1738,0.79397,0.31563,0.86345,0.67595,1.3501,1.079,1.2034,1.2432,1.3696,1.1473,0.72528,0.57199,0.85965,0.65339,0.47614,0.43219,1.363,2.4026,0.98947,0.85204,1.2591,2.3151,1.6775,1.6555,1.0371,2.5586,-0.097071,0.91744,1.068,1.6819,1.0122,0.99911,0.75267,1.2195,0.42175,0.2554,0.4819,0.80025,0.44517,0.32442,1.9863,1.5019,1.2893,1.0669,1.6881,1.7581,1.6505,1.943,2.141,1.018,0.68207,0.84392,1.1452,1.009,1.1376,1.0572,1.9258,1.7039,0.10261,1.4549,0.94693,0.77945,0.049658,0.84178
CATMA5A14990,0.41247,0.51378,0.375,-0.24526,0.027658,-0.015345,-0.028278,-0.2643,0.3905,0.12299,0.94334,0.11482,0.50109,-0.55162,0.10768,-0.26433,-0.31064,-0.76526,1.0147,-0.73178,0.3323,-0.13657,-0.14013,-0.17878,0.31259,0.36723,0.55499,-0.2664,0.31862,0.60225,0.078919,0.18282,0.23014,-0.27835,0.16392,0.58131,1.052,0.062394,0.064929,-0.11918,-0.2167,-0.26018,-0.21649,-0.032523,-0.05763,-0.62775,-0.29148,0.13633,0.72715,1.535,0.08501,0.76137,0.54934,1.5163,-0.26612,0.58127,0.30339,1.8464,-0.17161,0.30309,-0.070279,0.28016,-0.036628,0.67464,0.34753,0.26499,-0.17856,-0.61162,-0.17941,-0.01017,0.21112,0.026389,0.64566,-0.39685,0.31403,-0.21637,1.1253,0.41434,0.77759,0.20421,0.70657,0.53273,-0.67276,-0.65656,0.6997,-0.06428,-0.13546,-0.15055,0.99804,1.4673,-0.47155,-0.056436,0.84609,-0.23068,-0.55114,0.68975
CATMA5A25890,-0.23072,0.82252,0.32512,0.13284,0.14406,0.005071,0.40953,-0.11252,-0.6367,-0.048073,0.21325,-0.046316,-0.35078,1.1023,-0.019882,-0.55825,-0.34621,0.1114,0.030302,-0.08142,0.19157,0.5152,-0.034952,0.20984,1.0547,0.4816,0.31078,0.14731,0.31811,-0.27275,-0.47217,-0.043207,0.29109,0.41192,-1.069,0.23357,-0.17453,0.015661,-0.26869,0.14587,0.19336,0.5926,-0.1583,0.61242,0.18635,0.01207,-0.21772,0.13986,1.9305,0.3349,0.62954,-0.04455,1.121,-0.14155,-0.33772,-0.42633,0.41828,-0.29794,-0.7107,1.4786,-0.37442,-0.45371,0.16175,-0.057764,-0.11803,-0.037838,0.25868,0.88605,0.33225,-0.24331,0.93084,-0.33266,0.9762,0.24755,0.30857,0.14661,1.8351,0.40313,0.45084,-0.52105,-0.31271,-0.37122,-0.28615,-0.0023692,-0.43072,-0.24417,-0.5184,0.13874,-0.32606,-0.1705,2.1396,-0.77657,2.3056,0.32096,0.29337,-0.034746
CATMA5A31940,4.4307,4.8735,4.7911,5.8201,5.9587,5.576,5.9084,5.849,5.2832,5.5423,5.0275,4.5254,4.5054,3.0696,5.6512,4.3986,4.9856,4.9222,4.951,4.5506,4.2185,4.3184,5.4783,4.4515,3.9397,5.6269,5.0988,6.1254,5.7446,5.6146,5.452,5.2047,5.3527,5.93,4.8931,5.5529,3.9876,4.1072,4.0583,5.4324,5.4681,4.392,4.5189,5.0629,4.9347,5.2165,4.6681,4.7428,4.1539,6.3376,5.353,6.2896,5.5844,5.1654,5.4391,5.3933,5.261,5.6974,4.5142,3.9851,3.569,4.0946,4.6662,5.1085,5.0319,4.4598,4.8672,3.7326,5.3032,5.0448,4.7771,4.2209,4.1886,4.284,5.4681,5.8331,4.7683,5.6614,5.3313,5.2017,5.1002,5.0642,4.4867,4.8576,4.1454,4.0971,5.0368,4.845,4.753,5.5496,2.9838,5.1394,3.342,4.1246,5.0121,4.618

适当的代码(BHC来自Bioconductor):

library(argparse)
library(RColorBrewer)
library(BHC)
library(parallel)
library(gplots)

#data prep and clustering, to get dendrograms etc as shown in plot
data = read.csv('input.csv',header=TRUE,row.names=1,check.names=FALSE)
genes = rownames(data)
samples = colnames(data)
data = data.matrix(data)
standardisedData = (data-mean(data))/sd(data)
samples = as.numeric(samples)
hc = bhc(standardisedData, genes, timePoints=samples, dataType='time-course', verbose=TRUE)

#the plotting proper
png('heatmap.png',width=6,height=6,units='in',res=300)
heatmap.2(standardisedData, Colv=NA, Rowv=hc, tracecol=NA, scale="none", col=brewer.pal(11,'RdBu'))
#heatmap.2(standardisedData, Colv=NA, Rowv=hc, tracecol=NA, scale="none", col=brewer.pal(11,'RdBu'), margins=c(3,10)) <- this fixes it on a single-case basis
dev.off()

1 个答案:

答案 0 :(得分:2)

我建议只给它一个相当宽的默认边距,这样即使标签较长也应该剪掉它,例如:

heatmap.2(standardisedData, Colv=NA, Rowv=hc, tracecol=NA, scale="none", col=brewer.pal(11,'RdBu'), margins=c(14, 14))

默认边距大小为(5,5)。上面增加了列和行边距,但你也可以只增加一个或另一个。

如果您想更精确,可以尝试使用max(strwidth(rownames(standardisedData)))来确定最大标签所需的空间量,然后将其转换为par(mar=*)所需的单位(heatmap.2() 1}}用于指定绘图边距。)

我认为这应该可以解决问题:

# determine margins to use
png('/tmp/trash', width=6,height=6,units='in',res=300)
plot.new()
margin_width = max(strwidth(rownames(standardisedData), units='inches')) * par('fin')[1]
dev.off()

#the plotting proper
png('heatmap.png',width=6,height=6,units='in',res=300)
heatmap.2(standardisedData, Colv=NA, Rowv=hc, tracecol=NA, scale="none", col=brewer.pal(11,'RdBu'), margins=c(5, margin_width))
dev.off()