c("GO:0043484","regulation of RNA splicing", 0.006,-4.334, 4.564, 2.846,-1.9392,0.824,0.443),#
c("GO:0001700","", 0.003, 6.337,-3.379, 2.504,-1.0998,0.832,0.446),#
c("GO:0030721","", 0.000, 4.817, 6.552, 0.699,-1.6685,0.826,0.450),#
c("GO:0051129","", 0.045, 2.265, 7.425, 3.711,-1.8220,0.737,0.458),#
c("GO:0051340","", 0.003,-2.596, 6.152, 2.598,-2.3526,0.892,0.470),#
c("GO:0035152","", 0.001, 6.080, 0.005, 2.196,-2.7903,0.745,0.472),#
c("GO:0008340","", 0.009, 6.407,-3.413, 3.035,-1.6606,0.825,0.473),#
c("GO:0006085","", 0.004,-1.213,-1.643, 2.697,-2.2385,0.910,0.474),#
c("GO:0045478","", 0.000, 4.829, 6.460, 1.279,-1.6685,0.815,0.484),#
c("GO:0051591","", 0.005,-1.258, 0.746, 2.749,-1.7936,0.959,0.490),#
c("GO:0006213","", 0.201,-3.187,-4.297, 4.359,-1.4180,0.828,0.495),#
c("GO:0043433","", 0.009,-4.260, 4.614, 2.999,-1.0960,0.817,0.499),#
c("GO:0007276","", 0.062, 5.891,-4.046, 3.846,-1.4704,0.848,0.500),#
c("GO:0007444","", 0.009, 6.446,-3.405, 2.991,-1.7166,0.816,0.504),#
c("GO:0016044","", 0.166, 5.067, 6.954, 4.276,-1.6677,0.785,0.505),#
c("GO:0060047","", 0.013, 5.819,-3.867, 3.159,-1.1929,0.879,0.508),#
c("GO:0019953","", 0.086, 3.339,-2.209, 3.990,-1.0008,0.959,0.510),#
c("GO:0051084","", 0.014,-3.660,-0.386, 3.194,-1.0462,0.904,0.516),#
c("GO:0048754","", 0.021, 6.380,-3.373, 3.372,-1.3150,0.814,0.526),#
c("GO:0010927","", 0.019, 7.406, 2.247, 3.332,-1.1170,0.660,0.527),#
c("GO:0006413","", 0.337,-5.138, 0.300, 4.584,-1.7656,0.863,0.533),#
c("GO:0001763","", 0.026, 6.478,-3.423, 3.470,-1.3294,0.820,0.537),#
c("GO:0006412","translation", 4.967,-5.423, 0.276, 5.753,-5.2041,0.832,0.538),#
c("GO:0045429","", 0.004,-2.690, 5.902, 2.619,-1.0462,0.842,0.541),#
c("GO:0009992","", 0.000,-1.437, 6.111, 1.623,-1.6685,0.886,0.541),#
c("GO:0006112","", 0.129,-0.107,-1.299, 4.169,-1.8918,0.915,0.543),#
c("GO:0007016","", 0.002, 2.191, 7.245, 2.401,-3.1273,0.682,0.560),#
c("GO:0042692","", 0.034, 6.774,-3.330, 3.589,-2.5846,0.785,0.563),#
c("GO:0016052","", 1.399,-2.781,-2.512, 5.203,-5.3516,0.867,0.564),#
c("GO:0035317","", 0.001, 7.750, 1.636, 2.004,-1.9392,0.666,0.567),#
c("GO:0051235","", 0.032,-1.949, 6.873, 3.556,-1.9771,0.861,0.584),#
c("GO:0005977","", 0.128,-2.440,-1.665, 4.164,-1.8918,0.840,0.589),#
c("GO:0043933","", 1.085, 4.880, 6.508, 5.092,-2.5019,0.781,0.593),#
c("GO:0042775","", 0.315,-0.424,-1.022, 4.555,-2.1471,0.906,0.598),#
c("GO:0044042","", 0.249,-2.591,-1.536, 4.452,-1.3150,0.900,0.600),#
c("GO:0000302","response to reactive oxygen species", 0.096,-1.618, 1.005, 4.039,-2.2784,0.942,0.602),#
c("GO:0009791","", 0.098, 6.683,-3.550, 4.048,-1.0400,0.805,0.606),#
c("GO:0051881","", 0.003,-1.699, 6.373, 2.477,-1.1929,0.875,0.610),#
c("GO:0051187","", 0.531,-2.027,-1.695, 4.781,-5.0487,0.857,0.615),#
c("GO:0006397","", 0.620,-4.822,-0.359, 4.849,-8.9747,0.856,0.621),#
c("GO:0007265","", 0.221,-2.956, 5.997, 4.402,-2.4889,0.855,0.623),#
c("GO:0010324","", 0.005, 4.607, 6.144, 2.777,-1.5812,0.822,0.623),#
c("GO:0006122","", 0.011,-0.080,-1.282, 3.105,-1.6685,0.925,0.627),#
c("GO:0016339","", 0.001, 1.439,-0.992, 2.201,-1.0433,0.961,0.632),#
c("GO:0030030","", 0.278, 4.677, 6.193, 4.500,-2.1762,0.754,0.635),#
c("GO:0006559","", 0.019,-3.253,-4.084, 3.331,-2.2959,0.838,0.636),#
c("GO:0006911","", 0.005, 4.011, 6.037, 2.743,-1.0521,0.800,0.646),#
c("GO:0006979","response to oxidative stress", 0.231,-1.317, 0.827, 4.420,-2.0583,0.950,0.646),#
c("GO:0060537","muscle tissue development", 0.036, 7.028,-3.549, 3.618,-1.5332,0.829,0.653),#
c("GO:0050684","", 0.005,-4.394, 4.770, 2.748,-1.7936,0.831,0.653),#
c("GO:0008064","", 0.026, 2.387, 7.863, 3.464,-7.1506,0.637,0.655),#
c("GO:0016358","", 0.014, 7.794, 1.720, 3.188,-1.3150,0.635,0.658),#
c("GO:0006897","endocytosis", 0.075, 1.131, 3.355, 3.934,-1.4100,0.960,0.660),#
c("GO:0002165","", 0.010, 6.716,-3.572, 3.036,-1.3715,0.823,0.662),#
c("GO:0032268","", 0.389,-5.544, 4.690, 4.647,-2.1629,0.775,0.664),#
c("GO:0030163","", 0.408,-4.836,-1.080, 4.667,-1.3202,0.857,0.670),#
c("GO:0010033","", 0.355,-1.417, 1.182, 4.607,-1.1732,0.949,0.670),#
c("GO:0034220","", 1.198, 0.136, 2.096, 5.135,-2.0607,0.923,0.670),#
c("GO:0006570","", 0.045,-2.154,-4.133, 3.706,-1.3029,0.876,0.672),#
c("GO:0042554","", 0.002, 1.269,-1.060, 2.350,-1.0462,0.957,0.675),#
c("GO:0006119","", 0.910,-0.125,-1.330, 5.016,-4.2692,0.905,0.680),#
c("GO:0019362","", 0.466,-3.592,-4.539, 4.725,-3.9281,0.786,0.682),#
c("GO:0006631","", 0.734,-2.148,-4.054, 4.923,-1.1916,0.860,0.684),#
c("GO:0007052","", 0.006, 4.749, 6.355, 2.839,-4.1726,0.739,0.688),#
c("GO:0060711","", 0.005, 6.754,-3.633, 2.740,-1.0462,0.819,0.689),#
c("GO:0009109","", 0.518,-2.422,-2.497, 4.771,-5.2684,0.849,0.689),#
c("GO:0030865","", 0.007, 4.950, 6.686, 2.873,-1.7936,0.757,0.691),#
c("GO:0009208","", 0.044,-2.712,-4.135, 3.698,-1.0960,0.830,0.694),#
c("GO:0032989","", 0.840, 7.542, 2.233, 4.981,-1.0284,0.622,0.695),#
c("GO:0051100","", 0.024,-2.219, 5.334, 3.430,-1.0433,0.890,0.699))
one.data <- data.frame(revigo.data2);#
names(one.data) <- revigo.names;#
one.data <- one.data [(one.data$plot_X != "null" & one.data$plot_Y != "null"), ];#
one.data$plot_X <- as.numeric( as.character(one.data$plot_X) );#
one.data$plot_Y <- as.numeric( as.character(one.data$plot_Y) );#
one.data$plot_size <- as.numeric( as.character(one.data$plot_size) );#
one.data$log10_p_value <- as.numeric( as.character(one.data$log10_p_value) );#
one.data$frequency <- as.numeric( as.character(one.data$frequency) );#
one.data$uniqueness <- as.numeric( as.character(one.data$uniqueness) );#
one.data$dispensability <- as.numeric( as.character(one.data$dispensability) );#
#
p1 <- ggplot( data = one.data );#
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alpha = I(0.6) ) + scale_area();#
p1 <- p1 + scale_colour_gradientn( colours = c("blue", "green", "yellow", "red"), limits = c( min(one.data$log10_p_value), 0) );#
p1 <- p1 + geom_point( aes(plot_X, plot_Y, size = plot_size), shape = 21, fill = "transparent", colour = I (alpha ("black", 0.6) )) + scale_area();#
p1 <- p1 + scale_size( range=c(5, 30)) + theme_bw(); # + scale_fill_gradientn(colours = heat_hcl(7), limits = c(-300, 0) );#
ex <- one.data [ one.data$dispensability < 0.15, ]; #
p1 <- p1 + geom_text( data = ex, aes(plot_X, plot_Y, label = description), colour = I(alpha("black", 0.85)), size = 3 );#
p1 <- p1 + labs (y = "semantic space x", x = "semantic space y");#
p1 <- p1 + opts(legend.key = theme_blank()) ;#
one.x_range = max(one.data$plot_X) - min(one.data$plot_X);#
one.y_range = max(one.data$plot_Y) - min(one.data$plot_Y);#
p1 <- p1 + xlim(min(one.data$plot_X)-one.x_range/10,max(one.data$plot_X)+one.x_range/10);#
p1 <- p1 + ylim(min(one.data$plot_Y)-one.y_range/10,max(one.data$plot_Y)+one.y_range/10)
p1
revigo.data2<- rbind(c("GO:0006800","", 0.154, 1.858, 0.396, 4.210,-2.0916,0.993,0.000),#
c("GO:0007626"," ", 0.020,-1.112, 1.133, 3.364,-1.6635,0.971,0.000),#
c("GO:0008283","", 0.143, 0.694,-0.161, 4.213,-1.0233,0.993,0.000),#
c("GO:0008380","RNA splicing", 0.170,-4.408,-0.013, 4.287,-10.7570,0.873,0.000),#
c("GO:0016192","vesicle-mediated transport", 0.348, 0.764, 3.164, 4.598,-3.0506,0.963,0.000),#
c("GO:0022610","", 0.544, 1.207, 0.368, 4.792,-2.2067,0.993,0.000),#
c("GO:0055114","",16.676, 1.629, 0.426, 6.279,-6.6635,0.982,0.013),#
c("GO:0030716","oocyte fate determination", 0.001, 6.988,-3.504, 1.949,-2.6073,0.831,0.014),#
c("GO:0035088","", 0.002, 0.328, 0.023, 2.453,-1.3493,0.967,0.015),#
c("GO:0007163","", 0.016, 1.499, 0.542, 3.262,-1.3900,0.971,0.016),#
c("GO:0030029","", 0.106, 0.178,-0.029, 4.084,-6.2848,0.968,0.019),#
c("GO:0007010","cytoskeleton organization", 0.203, 4.735, 6.336, 4.363,-10.4486,0.734,0.020),#
c("GO:0007017","", 0.308, 0.566, 0.210, 4.546,-5.2226,0.965,0.021),#
c("GO:0000910","", 0.236, 1.601, 0.587, 4.429,-1.2966,0.966,0.021),#
c("GO:0045454","", 0.541,-2.252, 6.619, 4.790,-4.6421,0.830,0.022),#
c("GO:0007155","", 0.540, 0.897,-0.558, 4.789,-2.3218,0.949,0.024),#
c("GO:0042743","", 0.046, 0.049,-0.207, 3.718,-1.8137,0.950,0.035),#
c("GO:0017144","", 0.079, 1.661, 0.611, 3.954,-1.4425,0.953,0.036),#
c("GO:0015980","", 4.971, 0.029,-1.557, 5.753,-9.0182,0.887,0.052),#
c("GO:0006518","peptide metabolic process", 0.211, 0.787,-0.160, 4.381,-1.4665,0.950,0.054),#
c("GO:0006084","", 0.531,-0.875,-1.660, 4.782,-6.2684,0.897,0.060),#
c("GO:0006006","glucose metabolic process", 1.107,-2.536,-4.315, 5.101,-8.4908,0.805,0.065),#
c("GO:0009820","", 0.004, 0.980,-0.151, 2.614,-2.7758,0.962,0.077),#
c("GO:0051186","", 3.543, 0.040, 0.069, 5.606,-3.3125,0.937,0.077),#
c("GO:0006091","", 6.142, 0.623,-0.201, 5.845,-18.0615,0.934,0.085),#
c("GO:0043603","", 0.201,-0.518,-1.157, 4.361,-1.3966,0.926,0.108),#
c("GO:0006890","", 0.005, 1.319, 3.630, 2.757,-1.6644,0.933,0.176),#
c("GO:0048678","", 0.005,-1.154, 0.732, 2.717,-1.0101,0.968,0.214),#
c("GO:0006090","", 0.041,-2.007,-3.845, 3.667,-4.3215,0.891,0.227),#
c("GO:0006414","", 0.665,-5.221, 0.298, 4.880,-6.7905,0.856,0.251),#
c("GO:0051443","", 0.002,-4.697, 4.427, 2.332,-3.1308,0.797,0.259),#
c("GO:0008104","", 1.847, 0.620, 3.155, 5.323,-2.8214,0.936,0.277),#
c("GO:0001539","", 0.387, 0.225, 2.329, 4.645,-2.3615,0.932,0.280),#
c("GO:0010608","", 0.218,-4.398, 5.559, 4.395,-2.4001,0.860,0.289),#
c("GO:0006458","", 0.014,-3.541,-0.418, 3.201,-1.7523,0.904,0.296),#
c("GO:0015985","", 0.663, 0.574, 2.899, 4.879,-2.4501,0.923,0.297),#
c("GO:0016071","", 0.719,-6.026,-1.368, 4.913,-7.6253,0.876,0.300),#
c("GO:0018149","", 0.009,-4.101,-0.343, 3.019,-2.2912,0.901,0.306),#
c("GO:0006818","", 1.025, 0.901, 3.433, 5.067,-2.3529,0.961,0.312),#
c("GO:0021682","", 0.000, 6.949,-3.836, 1.398,-1.6685,0.841,0.315),#
c("GO:0007264","", 0.496,-2.983, 6.084, 4.753,-2.5153,0.847,0.320),#
c("GO:0010498","", 0.042,-5.261,-1.329, 3.680,-2.8750,0.847,0.321),#
c("GO:0006558","", 0.058,-2.232,-4.159, 3.818,-2.1048,0.874,0.323),#
c("GO:0006471","", 0.013,-3.683,-0.341, 3.180,-1.0960,0.899,0.339),#
c("GO:0009894","", 0.238,-3.396, 4.831, 4.433,-1.5155,0.836,0.345),#
c("GO:0006108","", 0.111,-2.053,-3.984, 4.103,-1.3029,0.885,0.346),#
c("GO:0010035","", 0.247,-1.386, 1.132, 4.449,-2.4658,0.950,0.350),#
c("GO:0007164","", 0.004, 6.764,-3.366, 2.685,-1.3497,0.853,0.359),#
c("GO:0009205","", 4.768,-3.673,-3.935, 5.735,-4.6946,0.775,0.369),#
c("GO:0006749","", 0.072,-2.360,-4.572, 3.915,-1.5837,0.878,0.372),#
c("GO:0006769","", 0.000,-1.715,-3.752, 0.954,-2.4131,0.874,0.373),#
c("GO:0006396","", 2.591,-5.116,-0.789, 5.470,-1.4256,0.857,0.395),#
c("GO:0007566","", 0.003, 6.449,-3.641, 2.507,-1.6644,0.834,0.397),#
c("GO:0051289","", 0.007, 4.930, 6.755, 2.888,-1.7869,0.804,0.403),#
c("GO:0019991","", 0.000, 4.749, 6.216, 1.690,-1.1969,0.816,0.413),#
c("GO:0006457","", 0.973,-4.392,-0.306, 5.045,-5.6180,0.875,0.426),#
c("GO:0044087","regulation of cellular component biogenesis", 0.077, 2.289, 7.845, 3.943,-3.2426,0.769,0.428),#
c("GO:0043484","regulation of RNA splicing", 0.006,-4.334, 4.564, 2.846,-1.9392,0.824,0.443),#
c("GO:0001700","", 0.003, 6.337,-3.379, 2.504,-1.0998,0.832,0.446),#
c("GO:0030721","", 0.000, 4.817, 6.552, 0.699,-1.6685,0.826,0.450),#
c("GO:0051129","", 0.045, 2.265, 7.425, 3.711,-1.8220,0.737,0.458),#
c("GO:0051340","", 0.003,-2.596, 6.152, 2.598,-2.3526,0.892,0.470),#
c("GO:0035152","", 0.001, 6.080, 0.005, 2.196,-2.7903,0.745,0.472),#
c("GO:0008340","", 0.009, 6.407,-3.413, 3.035,-1.6606,0.825,0.473),#
c("GO:0006085","", 0.004,-1.213,-1.643, 2.697,-2.2385,0.910,0.474),#
c("GO:0045478","", 0.000, 4.829, 6.460, 1.279,-1.6685,0.815,0.484),#
c("GO:0051591","", 0.005,-1.258, 0.746, 2.749,-1.7936,0.959,0.490),#
c("GO:0006213","", 0.201,-3.187,-4.297, 4.359,-1.4180,0.828,0.495),#
c("GO:0043433","", 0.009,-4.260, 4.614, 2.999,-1.0960,0.817,0.499),#
c("GO:0007276","", 0.062, 5.891,-4.046, 3.846,-1.4704,0.848,0.500),#
c("GO:0007444","", 0.009, 6.446,-3.405, 2.991,-1.7166,0.816,0.504),#
c("GO:0016044","", 0.166, 5.067, 6.954, 4.276,-1.6677,0.785,0.505),#
c("GO:0060047","", 0.013, 5.819,-3.867, 3.159,-1.1929,0.879,0.508),#
c("GO:0019953","", 0.086, 3.339,-2.209, 3.990,-1.0008,0.959,0.510),#
c("GO:0051084","", 0.014,-3.660,-0.386, 3.194,-1.0462,0.904,0.516),#
c("GO:0048754","", 0.021, 6.380,-3.373, 3.372,-1.3150,0.814,0.526),#
c("GO:0010927","", 0.019, 7.406, 2.247, 3.332,-1.1170,0.660,0.527),#
c("GO:0006413","", 0.337,-5.138, 0.300, 4.584,-1.7656,0.863,0.533),#
c("GO:0001763","", 0.026, 6.478,-3.423, 3.470,-1.3294,0.820,0.537),#
c("GO:0006412","", 4.967,-5.423, 0.276, 5.753,-5.2041,0.832,0.538),#
c("GO:0045429","", 0.004,-2.690, 5.902, 2.619,-1.0462,0.842,0.541),#
c("GO:0009992","", 0.000,-1.437, 6.111, 1.623,-1.6685,0.886,0.541),#
c("GO:0006112","energy reserve metabolic process", 0.129,-0.107,-1.299, 4.169,-1.8918,0.915,0.543),#
c("GO:0007016","", 0.002, 2.191, 7.245, 2.401,-3.1273,0.682,0.560),#
c("GO:0042692","", 0.034, 6.774,-3.330, 3.589,-2.5846,0.785,0.563),#
c("GO:0016052","", 1.399,-2.781,-2.512, 5.203,-5.3516,0.867,0.564),#
c("GO:0035317","", 0.001, 7.750, 1.636, 2.004,-1.9392,0.666,0.567),#
c("GO:0051235","", 0.032,-1.949, 6.873, 3.556,-1.9771,0.861,0.584),#
c("GO:0005977","glycogen metabolic process", 0.128,-2.440,-1.665, 4.164,-1.8918,0.840,0.589),#
c("GO:0043933","", 1.085, 4.880, 6.508, 5.092,-2.5019,0.781,0.593),#
c("GO:0042775","", 0.315,-0.424,-1.022, 4.555,-2.1471,0.906,0.598),#
c("GO:0044042","", 0.249,-2.591,-1.536, 4.452,-1.3150,0.900,0.600),#
c("GO:0000302","response to reactive oxygen species", 0.096,-1.618, 1.005, 4.039,-2.2784,0.942,0.602),#
c("GO:0009791","", 0.098, 6.683,-3.550, 4.048,-1.0400,0.805,0.606),#
c("GO:0051881","", 0.003,-1.699, 6.373, 2.477,-1.1929,0.875,0.610),#
c("GO:0051187","", 0.531,-2.027,-1.695, 4.781,-5.0487,0.857,0.615),#
c("GO:0006397","mRNA processing", 0.620,-4.822,-0.359, 4.849,-8.9747,0.856,0.621),#
c("GO:0007265","Ras protein signal transduction", 0.221,-2.956, 5.997, 4.402,-2.4889,0.855,0.623),#
c("GO:0010324","", 0.005, 4.607, 6.144, 2.777,-1.5812,0.822,0.623),#
c("GO:0006122","", 0.011,-0.080,-1.282, 3.105,-1.6685,0.925,0.627),#
c("GO:0016339","", 0.001, 1.439,-0.992, 2.201,-1.0433,0.961,0.632),#
c("GO:0030030","", 0.278, 4.677, 6.193, 4.500,-2.1762,0.754,0.635),#
c("GO:0006559","", 0.019,-3.253,-4.084, 3.331,-2.2959,0.838,0.636),#
c("GO:0006911","", 0.005, 4.011, 6.037, 2.743,-1.0521,0.800,0.646),#
c("GO:0006979","", 0.231,-1.317, 0.827, 4.420,-2.0583,0.950,0.646),#
c("GO:0060537","", 0.036, 7.028,-3.549, 3.618,-1.5332,0.829,0.653),#
c("GO:0050684","regulation of mRNA processing", 0.005,-4.394, 4.770, 2.748,-1.7936,0.831,0.653),#
c("GO:0008064","", 0.026, 2.387, 7.863, 3.464,-7.1506,0.637,0.655),#
c("GO:0016358","", 0.014, 7.794, 1.720, 3.188,-1.3150,0.635,0.658),#
c("GO:0006897","endocytosis", 0.075, 1.131, 3.355, 3.934,-1.4100,0.960,0.660),#
c("GO:0002165","", 0.010, 6.716,-3.572, 3.036,-1.3715,0.823,0.662),#
c("GO:0032268","", 0.389,-5.544, 4.690, 4.647,-2.1629,0.775,0.664),#
c("GO:0030163","", 0.408,-4.836,-1.080, 4.667,-1.3202,0.857,0.670),#
c("GO:0010033","", 0.355,-1.417, 1.182, 4.607,-1.1732,0.949,0.670),#
c("GO:0034220","", 1.198, 0.136, 2.096, 5.135,-2.0607,0.923,0.670),#
c("GO:0006570","", 0.045,-2.154,-4.133, 3.706,-1.3029,0.876,0.672),#
c("GO:0042554","", 0.002, 1.269,-1.060, 2.350,-1.0462,0.957,0.675),#
c("GO:0006119","", 0.910,-0.125,-1.330, 5.016,-4.2692,0.905,0.680),#
c("GO:0019362","", 0.466,-3.592,-4.539, 4.725,-3.9281,0.786,0.682),#
c("GO:0006631","", 0.734,-2.148,-4.054, 4.923,-1.1916,0.860,0.684),#
c("GO:0007052","", 0.006, 4.749, 6.355, 2.839,-4.1726,0.739,0.688),#
c("GO:0060711","", 0.005, 6.754,-3.633, 2.740,-1.0462,0.819,0.689),#
c("GO:0009109","", 0.518,-2.422,-2.497, 4.771,-5.2684,0.849,0.689),#
c("GO:0030865","", 0.007, 4.950, 6.686, 2.873,-1.7936,0.757,0.691),#
c("GO:0009208","", 0.044,-2.712,-4.135, 3.698,-1.0960,0.830,0.694),#
c("GO:0032989","cellular component morphogenesis", 0.840, 7.542, 2.233, 4.981,-1.0284,0.622,0.695),#
c("GO:0051100","", 0.024,-2.219, 5.334, 3.430,-1.0433,0.890,0.699))
one.data <- data.frame(revigo.data2);#
names(one.data) <- revigo.names;#
one.data <- one.data [(one.data$plot_X != "null" & one.data$plot_Y != "null"), ];#
one.data$plot_X <- as.numeric( as.character(one.data$plot_X) );#
one.data$plot_Y <- as.numeric( as.character(one.data$plot_Y) );#
one.data$plot_size <- as.numeric( as.character(one.data$plot_size) );#
one.data$log10_p_value <- as.numeric( as.character(one.data$log10_p_value) );#
one.data$frequency <- as.numeric( as.character(one.data$frequency) );#
one.data$uniqueness <- as.numeric( as.character(one.data$uniqueness) );#
one.data$dispensability <- as.numeric( as.character(one.data$dispensability) );#
#
p1 <- ggplot( data = one.data );#
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alpha = I(0.6) ) + scale_area();#
p1 <- p1 + scale_colour_gradientn( colours = c("blue", "green", "yellow", "red"), limits = c( min(one.data$log10_p_value), 0) );#
p1 <- p1 + geom_point( aes(plot_X, plot_Y, size = plot_size), shape = 21, fill = "transparent", colour = I (alpha ("black", 0.6) )) + scale_area();#
p1 <- p1 + scale_size( range=c(5, 30)) + theme_bw(); # + scale_fill_gradientn(colours = heat_hcl(7), limits = c(-300, 0) );#
ex <- one.data [ one.data$dispensability < 0.15, ]; #
p1 <- p1 + geom_text( data = ex, aes(plot_X, plot_Y, label = description), colour = I(alpha("black", 0.85)), size = 3 );#
p1 <- p1 + labs (y = "semantic space x", x = "semantic space y");#
p1 <- p1 + opts(legend.key = theme_blank()) ;#
one.x_range = max(one.data$plot_X) - min(one.data$plot_X);#
one.y_range = max(one.data$plot_Y) - min(one.data$plot_Y);#
p1 <- p1 + xlim(min(one.data$plot_X)-one.x_range/10,max(one.data$plot_X)+one.x_range/10);#
p1 <- p1 + ylim(min(one.data$plot_Y)-one.y_range/10,max(one.data$plot_Y)+one.y_range/10)
p1
one.data <- data.frame(revigo.data);
#
names(one.data) <- revigo.names;
#
one.data <- one.data [(one.data$plot_X != "null" & one.data$plot_Y != "null"), ];
#
one.data$plot_X <- as.numeric( as.character(one.data$plot_X) );
#
one.data$plot_Y <- as.numeric( as.character(one.data$plot_Y) );
#
one.data$plot_size <- as.numeric( as.character(one.data$plot_size) );
#
one.data$log10_p_value <- as.numeric( as.character(one.data$log10_p_value) );
#
one.data$frequency <- as.numeric( as.character(one.data$frequency) );
#
one.data$uniqueness <- as.numeric( as.character(one.data$uniqueness) );
#
one.data$dispensability <- as.numeric( as.character(one.data$dispensability) )
p1 <- ggplot( data = one.data );
#
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alpha = I(0.6) ) + scale_area();
#
p1 <- p1 + scale_colour_gradientn( colours = c("blue", "green", "yellow", "red"), limits = c( min(one.data$log10_p_value), 0) );
#
p1 <- p1 + geom_point( aes(plot_X, plot_Y, size = plot_size), shape = 21, fill = "transparent", colour = I (alpha ("black", 0.6) )) + scale_area();
#
p1 <- p1 + scale_size( range=c(5, 30)) + theme_bw(); # + scale_fill_gradientn(colours = heat_hcl(7), limits = c(-300, 0) );
#
ex <- one.data [ one.data$dispensability < 0.15, ]; 
#
p1 <- p1 + geom_text( data = ex, aes(plot_X, plot_Y, label = description), colour = I(alpha("black", 0.85)), size = 3 );
#
p1 <- p1 + labs (y = "semantic space x", x = "semantic space y");
#
p1 <- p1 + opts(legend.key = theme_blank()) ;
#
one.x_range = max(one.data$plot_X) - min(one.data$plot_X);
#
one.y_range = max(one.data$plot_Y) - min(one.data$plot_Y);
#
p1 <- p1 + xlim(min(one.data$plot_X)-one.x_range/10,max(one.data$plot_X)+one.x_range/10);
#
p1 <- p1 + ylim(min(one.data$plot_Y)-one.y_range/10,max(one.data$plot_Y)+one.y_range/10)
p1
revigo.data2<- rbind(c("GO:0006800","", 0.154, 1.858, 0.396, 4.210,-2.0916,0.993,0.000),#
c("GO:0007626"," ", 0.020,-1.112, 1.133, 3.364,-1.6635,0.971,0.000),#
c("GO:0008283","", 0.143, 0.694,-0.161, 4.213,-1.0233,0.993,0.000),#
c("GO:0008380","RNA splicing", 0.170,-4.408,-0.013, 4.287,-10.7570,0.873,0.000),#
c("GO:0016192","vesicle-mediated transport", 0.348, 0.764, 3.164, 4.598,-3.0506,0.963,0.000),#
c("GO:0022610","", 0.544, 1.207, 0.368, 4.792,-2.2067,0.993,0.000),#
c("GO:0055114","",16.676, 1.629, 0.426, 6.279,-6.6635,0.982,0.013),#
c("GO:0030716","oocyte fate determination", 0.001, 6.988,-3.504, 1.949,-2.6073,0.831,0.014),#
c("GO:0035088","", 0.002, 0.328, 0.023, 2.453,-1.3493,0.967,0.015),#
c("GO:0007163","", 0.016, 1.499, 0.542, 3.262,-1.3900,0.971,0.016),#
c("GO:0030029","", 0.106, 0.178,-0.029, 4.084,-6.2848,0.968,0.019),#
c("GO:0007010","cytoskeleton organization", 0.203, 4.735, 6.336, 4.363,-10.4486,0.734,0.020),#
c("GO:0007017","", 0.308, 0.566, 0.210, 4.546,-5.2226,0.965,0.021),#
c("GO:0000910","", 0.236, 1.601, 0.587, 4.429,-1.2966,0.966,0.021),#
c("GO:0045454","cell redox homeostasis", 0.541,-2.252, 6.619, 4.790,-4.6421,0.830,0.022),#
c("GO:0007155","", 0.540, 0.897,-0.558, 4.789,-2.3218,0.949,0.024),#
c("GO:0042743","", 0.046, 0.049,-0.207, 3.718,-1.8137,0.950,0.035),#
c("GO:0017144","", 0.079, 1.661, 0.611, 3.954,-1.4425,0.953,0.036),#
c("GO:0015980","energy derivation by oxidation of organic compounds", 4.971, 0.029,-1.557, 5.753,-9.0182,0.887,0.052),#
c("GO:0006518","peptide metabolic process", 0.211, 0.787,-0.160, 4.381,-1.4665,0.950,0.054),#
c("GO:0006084","", 0.531,-0.875,-1.660, 4.782,-6.2684,0.897,0.060),#
c("GO:0006006","glucose metabolic process", 1.107,-2.536,-4.315, 5.101,-8.4908,0.805,0.065),#
c("GO:0009820","", 0.004, 0.980,-0.151, 2.614,-2.7758,0.962,0.077),#
c("GO:0051186","", 3.543, 0.040, 0.069, 5.606,-3.3125,0.937,0.077),#
c("GO:0006091","", 6.142, 0.623,-0.201, 5.845,-18.0615,0.934,0.085),#
c("GO:0043603","cellular amide metabolic process", 0.201,-0.518,-1.157, 4.361,-1.3966,0.926,0.108),#
c("GO:0006890","", 0.005, 1.319, 3.630, 2.757,-1.6644,0.933,0.176),#
c("GO:0048678","", 0.005,-1.154, 0.732, 2.717,-1.0101,0.968,0.214),#
c("GO:0006090","", 0.041,-2.007,-3.845, 3.667,-4.3215,0.891,0.227),#
c("GO:0006414","", 0.665,-5.221, 0.298, 4.880,-6.7905,0.856,0.251),#
c("GO:0051443","", 0.002,-4.697, 4.427, 2.332,-3.1308,0.797,0.259),#
c("GO:0008104","", 1.847, 0.620, 3.155, 5.323,-2.8214,0.936,0.277),#
c("GO:0001539","", 0.387, 0.225, 2.329, 4.645,-2.3615,0.932,0.280),#
c("GO:0010608","", 0.218,-4.398, 5.559, 4.395,-2.4001,0.860,0.289),#
c("GO:0006458","", 0.014,-3.541,-0.418, 3.201,-1.7523,0.904,0.296),#
c("GO:0015985","", 0.663, 0.574, 2.899, 4.879,-2.4501,0.923,0.297),#
c("GO:0016071","", 0.719,-6.026,-1.368, 4.913,-7.6253,0.876,0.300),#
c("GO:0018149","", 0.009,-4.101,-0.343, 3.019,-2.2912,0.901,0.306),#
c("GO:0006818","", 1.025, 0.901, 3.433, 5.067,-2.3529,0.961,0.312),#
c("GO:0021682","", 0.000, 6.949,-3.836, 1.398,-1.6685,0.841,0.315),#
c("GO:0007264","", 0.496,-2.983, 6.084, 4.753,-2.5153,0.847,0.320),#
c("GO:0010498","", 0.042,-5.261,-1.329, 3.680,-2.8750,0.847,0.321),#
c("GO:0006558","", 0.058,-2.232,-4.159, 3.818,-2.1048,0.874,0.323),#
c("GO:0006471","", 0.013,-3.683,-0.341, 3.180,-1.0960,0.899,0.339),#
c("GO:0009894","", 0.238,-3.396, 4.831, 4.433,-1.5155,0.836,0.345),#
c("GO:0006108","", 0.111,-2.053,-3.984, 4.103,-1.3029,0.885,0.346),#
c("GO:0010035","", 0.247,-1.386, 1.132, 4.449,-2.4658,0.950,0.350),#
c("GO:0007164","", 0.004, 6.764,-3.366, 2.685,-1.3497,0.853,0.359),#
c("GO:0009205","", 4.768,-3.673,-3.935, 5.735,-4.6946,0.775,0.369),#
c("GO:0006749","", 0.072,-2.360,-4.572, 3.915,-1.5837,0.878,0.372),#
c("GO:0006769","", 0.000,-1.715,-3.752, 0.954,-2.4131,0.874,0.373),#
c("GO:0006396","", 2.591,-5.116,-0.789, 5.470,-1.4256,0.857,0.395),#
c("GO:0007566","", 0.003, 6.449,-3.641, 2.507,-1.6644,0.834,0.397),#
c("GO:0051289","", 0.007, 4.930, 6.755, 2.888,-1.7869,0.804,0.403),#
c("GO:0019991","", 0.000, 4.749, 6.216, 1.690,-1.1969,0.816,0.413),#
c("GO:0006457","", 0.973,-4.392,-0.306, 5.045,-5.6180,0.875,0.426),#
c("GO:0044087","regulation of cellular component biogenesis", 0.077, 2.289, 7.845, 3.943,-3.2426,0.769,0.428),#
c("GO:0043484","regulation of RNA splicing", 0.006,-4.334, 4.564, 2.846,-1.9392,0.824,0.443),#
c("GO:0001700","", 0.003, 6.337,-3.379, 2.504,-1.0998,0.832,0.446),#
c("GO:0030721","", 0.000, 4.817, 6.552, 0.699,-1.6685,0.826,0.450),#
c("GO:0051129","", 0.045, 2.265, 7.425, 3.711,-1.8220,0.737,0.458),#
c("GO:0051340","", 0.003,-2.596, 6.152, 2.598,-2.3526,0.892,0.470),#
c("GO:0035152","", 0.001, 6.080, 0.005, 2.196,-2.7903,0.745,0.472),#
c("GO:0008340","", 0.009, 6.407,-3.413, 3.035,-1.6606,0.825,0.473),#
c("GO:0006085","", 0.004,-1.213,-1.643, 2.697,-2.2385,0.910,0.474),#
c("GO:0045478","", 0.000, 4.829, 6.460, 1.279,-1.6685,0.815,0.484),#
c("GO:0051591","", 0.005,-1.258, 0.746, 2.749,-1.7936,0.959,0.490),#
c("GO:0006213","", 0.201,-3.187,-4.297, 4.359,-1.4180,0.828,0.495),#
c("GO:0043433","", 0.009,-4.260, 4.614, 2.999,-1.0960,0.817,0.499),#
c("GO:0007276","", 0.062, 5.891,-4.046, 3.846,-1.4704,0.848,0.500),#
c("GO:0007444","", 0.009, 6.446,-3.405, 2.991,-1.7166,0.816,0.504),#
c("GO:0016044","", 0.166, 5.067, 6.954, 4.276,-1.6677,0.785,0.505),#
c("GO:0060047","", 0.013, 5.819,-3.867, 3.159,-1.1929,0.879,0.508),#
c("GO:0019953","", 0.086, 3.339,-2.209, 3.990,-1.0008,0.959,0.510),#
c("GO:0051084","", 0.014,-3.660,-0.386, 3.194,-1.0462,0.904,0.516),#
c("GO:0048754","", 0.021, 6.380,-3.373, 3.372,-1.3150,0.814,0.526),#
c("GO:0010927","", 0.019, 7.406, 2.247, 3.332,-1.1170,0.660,0.527),#
c("GO:0006413","", 0.337,-5.138, 0.300, 4.584,-1.7656,0.863,0.533),#
c("GO:0001763","", 0.026, 6.478,-3.423, 3.470,-1.3294,0.820,0.537),#
c("GO:0006412","", 4.967,-5.423, 0.276, 5.753,-5.2041,0.832,0.538),#
c("GO:0045429","", 0.004,-2.690, 5.902, 2.619,-1.0462,0.842,0.541),#
c("GO:0009992","", 0.000,-1.437, 6.111, 1.623,-1.6685,0.886,0.541),#
c("GO:0006112","energy reserve metabolic process", 0.129,-0.107,-1.299, 4.169,-1.8918,0.915,0.543),#
c("GO:0007016","", 0.002, 2.191, 7.245, 2.401,-3.1273,0.682,0.560),#
c("GO:0042692","", 0.034, 6.774,-3.330, 3.589,-2.5846,0.785,0.563),#
c("GO:0016052","", 1.399,-2.781,-2.512, 5.203,-5.3516,0.867,0.564),#
c("GO:0035317","", 0.001, 7.750, 1.636, 2.004,-1.9392,0.666,0.567),#
c("GO:0051235","", 0.032,-1.949, 6.873, 3.556,-1.9771,0.861,0.584),#
c("GO:0005977","glycogen metabolic process", 0.128,-2.440,-1.665, 4.164,-1.8918,0.840,0.589),#
c("GO:0043933","", 1.085, 4.880, 6.508, 5.092,-2.5019,0.781,0.593),#
c("GO:0042775","", 0.315,-0.424,-1.022, 4.555,-2.1471,0.906,0.598),#
c("GO:0044042","", 0.249,-2.591,-1.536, 4.452,-1.3150,0.900,0.600),#
c("GO:0000302","response to reactive oxygen species", 0.096,-1.618, 1.005, 4.039,-2.2784,0.942,0.602),#
c("GO:0009791","", 0.098, 6.683,-3.550, 4.048,-1.0400,0.805,0.606),#
c("GO:0051881","", 0.003,-1.699, 6.373, 2.477,-1.1929,0.875,0.610),#
c("GO:0051187","", 0.531,-2.027,-1.695, 4.781,-5.0487,0.857,0.615),#
c("GO:0006397","mRNA processing", 0.620,-4.822,-0.359, 4.849,-8.9747,0.856,0.621),#
c("GO:0007265","Ras protein signal transduction", 0.221,-2.956, 5.997, 4.402,-2.4889,0.855,0.623),#
c("GO:0010324","", 0.005, 4.607, 6.144, 2.777,-1.5812,0.822,0.623),#
c("GO:0006122","", 0.011,-0.080,-1.282, 3.105,-1.6685,0.925,0.627),#
c("GO:0016339","", 0.001, 1.439,-0.992, 2.201,-1.0433,0.961,0.632),#
c("GO:0030030","", 0.278, 4.677, 6.193, 4.500,-2.1762,0.754,0.635),#
c("GO:0006559","", 0.019,-3.253,-4.084, 3.331,-2.2959,0.838,0.636),#
c("GO:0006911","", 0.005, 4.011, 6.037, 2.743,-1.0521,0.800,0.646),#
c("GO:0006979","", 0.231,-1.317, 0.827, 4.420,-2.0583,0.950,0.646),#
c("GO:0060537","", 0.036, 7.028,-3.549, 3.618,-1.5332,0.829,0.653),#
c("GO:0050684","regulation of mRNA processing", 0.005,-4.394, 4.770, 2.748,-1.7936,0.831,0.653),#
c("GO:0008064","", 0.026, 2.387, 7.863, 3.464,-7.1506,0.637,0.655),#
c("GO:0016358","", 0.014, 7.794, 1.720, 3.188,-1.3150,0.635,0.658),#
c("GO:0006897","endocytosis", 0.075, 1.131, 3.355, 3.934,-1.4100,0.960,0.660),#
c("GO:0002165","", 0.010, 6.716,-3.572, 3.036,-1.3715,0.823,0.662),#
c("GO:0032268","", 0.389,-5.544, 4.690, 4.647,-2.1629,0.775,0.664),#
c("GO:0030163","", 0.408,-4.836,-1.080, 4.667,-1.3202,0.857,0.670),#
c("GO:0010033","", 0.355,-1.417, 1.182, 4.607,-1.1732,0.949,0.670),#
c("GO:0034220","", 1.198, 0.136, 2.096, 5.135,-2.0607,0.923,0.670),#
c("GO:0006570","", 0.045,-2.154,-4.133, 3.706,-1.3029,0.876,0.672),#
c("GO:0042554","", 0.002, 1.269,-1.060, 2.350,-1.0462,0.957,0.675),#
c("GO:0006119","", 0.910,-0.125,-1.330, 5.016,-4.2692,0.905,0.680),#
c("GO:0019362","", 0.466,-3.592,-4.539, 4.725,-3.9281,0.786,0.682),#
c("GO:0006631","", 0.734,-2.148,-4.054, 4.923,-1.1916,0.860,0.684),#
c("GO:0007052","", 0.006, 4.749, 6.355, 2.839,-4.1726,0.739,0.688),#
c("GO:0060711","", 0.005, 6.754,-3.633, 2.740,-1.0462,0.819,0.689),#
c("GO:0009109","", 0.518,-2.422,-2.497, 4.771,-5.2684,0.849,0.689),#
c("GO:0030865","", 0.007, 4.950, 6.686, 2.873,-1.7936,0.757,0.691),#
c("GO:0009208","", 0.044,-2.712,-4.135, 3.698,-1.0960,0.830,0.694),#
c("GO:0032989","cellular component morphogenesis", 0.840, 7.542, 2.233, 4.981,-1.0284,0.622,0.695),#
c("GO:0051100","", 0.024,-2.219, 5.334, 3.430,-1.0433,0.890,0.699))
one.data <- data.frame(revigo.data2);#
names(one.data) <- revigo.names;#
one.data <- one.data [(one.data$plot_X != "null" & one.data$plot_Y != "null"), ];#
one.data$plot_X <- as.numeric( as.character(one.data$plot_X) );#
one.data$plot_Y <- as.numeric( as.character(one.data$plot_Y) );#
one.data$plot_size <- as.numeric( as.character(one.data$plot_size) );#
one.data$log10_p_value <- as.numeric( as.character(one.data$log10_p_value) );#
one.data$frequency <- as.numeric( as.character(one.data$frequency) );#
one.data$uniqueness <- as.numeric( as.character(one.data$uniqueness) );#
one.data$dispensability <- as.numeric( as.character(one.data$dispensability) );#
#
p1 <- ggplot( data = one.data );#
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alpha = I(0.6) ) + scale_area();#
p1 <- p1 + scale_colour_gradientn( colours = c("blue", "green", "yellow", "red"), limits = c( min(one.data$log10_p_value), 0) );#
p1 <- p1 + geom_point( aes(plot_X, plot_Y, size = plot_size), shape = 21, fill = "transparent", colour = I (alpha ("black", 0.6) )) + scale_area();#
p1 <- p1 + scale_size( range=c(5, 30)) + theme_bw(); # + scale_fill_gradientn(colours = heat_hcl(7), limits = c(-300, 0) );#
ex <- one.data [ one.data$dispensability < 0.15, ]; #
p1 <- p1 + geom_text( data = ex, aes(plot_X, plot_Y, label = description), colour = I(alpha("black", 0.85)), size = 3 );#
p1 <- p1 + labs (y = "semantic space x", x = "semantic space y");#
p1 <- p1 + opts(legend.key = theme_blank()) ;#
one.x_range = max(one.data$plot_X) - min(one.data$plot_X);#
one.y_range = max(one.data$plot_Y) - min(one.data$plot_Y);#
p1 <- p1 + xlim(min(one.data$plot_X)-one.x_range/10,max(one.data$plot_X)+one.x_range/10);#
p1 <- p1 + ylim(min(one.data$plot_Y)-one.y_range/10,max(one.data$plot_Y)+one.y_range/10)
p1
no.peps<-c(1000,5000,10000,12000,500,15000,50000,30000,40000,70000)#
no.prots<-c(587,1344,1773,1864,331,1936,2658,2400,2516,2833)
plot(no.peps, no.prots, xlab="Number of Sequenced Peptides", ylab='Number of Unique Proteins Identified', pch=19)
citation()
source('http://bioconductor.org/biocLite.R')
biocLite('DESeq')
library(lattice)
library(locfit)
biocLite('DESeq')
source('http://bioconductor.org/biocLite.R')
biocLite('DESeq')
source('http://bioconductor.org/biocLite.R')
biocLite('DESeq')
library(vegdist)
source('http://bioconductor.org/biocLite.R')
biocLite('goseq')
citation()
library(pheatmap)
?pheatmap
?metaMDS
library(vegan)
?metaMDS
citation('vegan')
citation()
revigo.names <- c("term_ID","description","frequency_%","plot_X","plot_Y","plot_size","log10_p_value","uniqueness","dispensability");
revigo.data <- rbind(c("GO:0007156","homophilic cell adhesion", 0.056, 2.343,-0.125, 4.022,-1.2257,0.603,0.000),
c("GO:0009611","response to wounding", 0.223,-5.266, 1.816, 4.622,-1.3865,0.698,0.000),
#
c("GO:0030866","cortical actin cytoskeleton organization", 0.004, 4.020, 4.646, 2.916,-1.0511,0.513,0.141),
#
c("GO:0051651","maintenance of location in cell", 0.022, 3.440,-3.668, 3.619,-1.2257,0.436,0.156),
#
c("GO:0006355","regulation of transcription, DNA-dependent", 8.764,-0.510,-6.210, 6.216,-1.3387,0.728,0.284),
#
c("GO:0045216","cell-cell junction organization", 0.015, 5.908, 2.870, 3.437,-1.0511,0.551,0.322),
#
c("GO:0006954","inflammatory response", 0.173,-4.942, 3.120, 4.510,-1.2425,0.698,0.481),
#
c("GO:0030865","cortical cytoskeleton organization", 0.005, 4.733, 4.340, 2.976,-1.0511,0.512,0.608),
#
c("GO:0046907","intracellular transport", 0.834, 5.396,-1.507, 5.194,-1.0755,0.417,0.611))
one.data <- data.frame(revigo.data);
names(one.data) <- revigo.names;
one.data <- one.data [(one.data$plot_X != "null" & one.data$plot_Y != "null"), ];
#
one.data$plot_X <- as.numeric( as.character(one.data$plot_X) );
#
one.data$plot_Y <- as.numeric( as.character(one.data$plot_Y) );
#
one.data$plot_size <- as.numeric( as.character(one.data$plot_size) );
#
one.data$log10_p_value <- as.numeric( as.character(one.data$log10_p_value) );
#
one.data$frequency <- as.numeric( as.character(one.data$frequency) );
#
one.data$uniqueness <- as.numeric( as.character(one.data$uniqueness) );
#
one.data$dispensability <- as.numeric( as.character(one.data$dispensability) )
p1 <- ggplot( data = one.data );
library(ggplot)
library(ggplot2)
p1 <- ggplot( data = one.data );
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alpha = I(0.6) ) + scale_area()
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alpha = I(0.6) ) + scale_size_area();
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, alpha = I(0.6) ) + scale_size_area();
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, alpha = I(0.6) ) + scale_size_area()
)
?scale_size_area
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, alpha = I(0.6) ) + scale_size_area();
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alpha = I(0.6) );
p1 <- p1 + scale_colour_gradientn( colours = c("blue", "green", "yellow", "red"), limits = c( min(one.data$log10_p_value), 0) );
p1 <- p1 + geom_point( aes(plot_X, plot_Y, size = plot_size), shape = 21, fill = "transparent", colour = I (alpha ("black", 0.6) )) + scale_area();
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alph = I(0.6) );
p1 <- p1 + scale_colour_gradientn( colours = c("blue", "green", "yellow", "red"), limits = c( min(one.data$log10_p_value), 0) );
p1 <- p1 + geom_point( aes(plot_X, plot_Y, size = plot_size), shape = 21, fill = "transparent", colour = I (alph ("black", 0.6) )) + scale_area();
library(scales)
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alpha = I(0.6) );
p1 <- p1 + scale_colour_gradientn( colours = c("blue", "green", "yellow", "red"), limits = c( min(one.data$log10_p_value), 0) );
p1 <- p1 + geom_point( aes(plot_X, plot_Y, size = plot_size), shape = 21, fill = "transparent", colour = I (alpha ("black", 0.6) )) + scale_area();
rm(p1)
plot.mort<-par(mfrow=c(1,2))
ggplot(df) + geom_step(aes(x=Jour, y=Inf1), colour='darkred') + geom_step(aes(x=Jour, y=Inf2), colour='red') + geom_step(aes(x=Jour, y=Inf3), colour='deeppink3') + scale_y_continuous(name='Number of Mortalities') + scale_x_continuous(name='Day')
library(ggplot2)
ggplot(df) + geom_step(aes(x=Jour, y=Inf1), colour='darkred') + geom_step(aes(x=Jour, y=Inf2), colour='red') + geom_step(aes(x=Jour, y=Inf3), colour='deeppink3') + scale_y_continuous(name='Number of Mortalities') + scale_x_continuous(name='Day')
library(qvalue)
citation(qvalue)
citation()
citation('qvalue')
citation()
citation("vegan")
citation("qvalue")
?pichart
?piechart
oyster.norm<-c(0.25, 0.25, 0.25, 0.25)
piechart(osyter.norm)
?pie
pie(oyster.norm)
pie(oyster.norm, labels=c('growth and development', 'maintenance of pH', 'response to stress', 'shell deposition'), col=c('deeppink3', 'goldenrod1', 'slateblue4', 'darkolivegreen'))
pie(oyster.norm, labels=c('growth and development', 'maintenance of pH', 'response to stress', 'shell deposition'), col=c('deeppink3', 'goldenrod1', 'slateblue4', 'darkgreen'))
oyster.oa<-c(0.07, 0.4, 0.4, 0.13)
pie(oyster.oa, labels=c('growth and development', 'maintenance of pH', 'response to stress', 'shell deposition'), col=c('deeppink3', 'goldenrod1', 'slateblue4', 'darkgreen'))
pie(oyster.oa, labels=c('growth and \ndevelopment', 'maintenance of pH', 'response to stress', 'shell deposition'), col=c('deeppink3', 'goldenrod1', 'slateblue4', 'darkgreen'))
pie(oyster.norm, labels=c('growth and \ndevelopment', 'maintenance of pH', 'response to stress', 'shell deposition'), col=c('deeppink3', 'goldenrod1', 'slateblue4', 'darkgreen'))
100/6
?pie
normphys<-c(rep(1,6))
pie(normphys)
oaphys<-c(5,1,1,1,6,10)
phys.lab<-c('Lipid metabolism', 'Glucose metabolism', 'Muscle growth', 'Immune response', 'Antioxidant response', 'Cellular stress')
physcol<-c('deeppink3', 'green3', 'darkviolet', 'blue3', 'goldenrod2', 'orangered2')
pie(normphys, labels=phys.lab, col=physcol
)
pie(oaphys, labels=phys.lab, col=physcol)
mechphys<-c(0.2, 5, 1, 10)
mechoa<-c(6,1,10,3)
mechlab<-c('Antioxidant response', 'Apoptosis', 'Conversion of stored carbohydrates', 'Cellular Stress')
mechcol<-c('goldenrod2','gray26','green3', 'orangered2')
pie(mechphys, labels=mechlab, col=mechcol)
mechcol<-c('goldenrod2','slategray','green3', 'orangered2')
pie(mechphys, labels=mechlab, col=mechcol)
mechlab<-c('Antioxidant \nresponse', 'Apoptosis', 'Conversion of \nstored carbohydrates', 'Cellular Stress')
pie(mechphys, labels=mechlab, col=mechcol)
pie(mechoa, labels=mechlab, col=mechcol)
setwd('/Users/emmatimminsschiffman/Documents/Dissertation/secondary stress/glycogen')
glyc<-read.csv('glycogen.csv', header=T)
Glycogen.per.mg<-glyc$Tissue.mass*(200/glyc$Glycogen)
glyc2<-cbind(glyc, Glycogen.per.mg)
glyc2
min(glyc2$Glycogen.per.mg)
max(glyc2$Glycogen.per.mg)
mean(glyc2$Glycogen.per.mg)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,#
                      conf.interval=.95, .drop=TRUE) {#
    require(plyr)#
#
    # New version of length which can handle NA's: if na.rm==T, don't count them#
    length2 <- function (x, na.rm=FALSE) {#
        if (na.rm) sum(!is.na(x))#
        else       length(x)#
    }#
#
    # This does the summary. For each group's data frame, return a vector with#
    # N, mean, and sd#
    datac <- ddply(data, groupvars, .drop=.drop,#
      .fun = function(xx, col) {#
        c(N    = length2(xx[[col]], na.rm=na.rm),#
          mean = mean   (xx[[col]], na.rm=na.rm),#
          sd   = sd     (xx[[col]], na.rm=na.rm)#
        )#
      },#
      measurevar#
    )#
# Rename the "mean" column    #
    datac <- rename(datac, c("mean" = measurevar))#
#
    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean#
#
    # Confidence interval multiplier for standard error#
    # Calculate t-statistic for confidence interval: #
    # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1#
    ciMult <- qt(conf.interval/2 + .5, datac$N-1)#
    datac$ci <- datac$se * ciMult#
#
    return(datac)#
}
summarySE(glyc2, measurevar='Glycogen.per.mg')
glyc.rm<-(glyc2$Glycogen.per.mg[-20])
plot(y=glyc.rm, x=treatment.rm, xaxp=c(400, 2800, 6), col=c(rep('blue', 8), rep('forestgreen', 8), rep('orangered', 7)), cex=1.25)
treatment.rm<-(glyc2$Treatment2[-20])
plot(y=glyc.rm, x=treatment.rm, xaxp=c(400, 2800, 6), col=c(rep('blue', 8), rep('forestgreen', 8), rep('orangered', 7)), cex=1.25)
glyc.rm
treatment.rm
treatment.rm<-(glyc2$Treatment2[-20])
treatment.rm
glyc2$Treatment2[-20]
head(glyc2)
treatment.rm<-(glyc2$Treatment[-20])
plot(y=glyc.rm, x=treatment.rm, xaxp=c(400, 2800, 6), col=c(rep('blue', 8), rep('forestgreen', 8), rep('orangered', 7)), cex=1.25)
plot(y=glyc.rm, x=treatment.rm, type='p', xaxp=c(400, 2800, 6), col=c(rep('blue', 8), rep('forestgreen', 8), rep('orangered', 7)), cex=1.25)
Treatment2<-as.numeric(c(rep('400', 8), rep('800', 8), rep('2800', 8)))
glyc2<-cbind(glyc2, Treatment2)
treatment.rm<-(glyc2$Treatment2[-20])
plot(y=glyc.rm, x=treatment.rm, xaxp=c(400, 2800, 6), col=c(rep('blue', 8), rep('forestgreen', 8), rep('orangered', 7)), cex=1.25)
plot(y=glyc.rm, x=treatment.rm, xaxp=c(400, 2800, 6), col=c(rep('blue', 8), rep('forestgreen', 8), rep('orangered', 7)), cex=1.25, ylab='Glycogen content (µg per mg tissue)', xlab='pCO2 (µatm)', xaxp=c(400, 2800, 6))
setwd('/Users/emmatimminsschiffman/Documents/Dissertation/secondary stress/glycogen')
glyc<-read.csv('glycogen.csv', header=T)
Glycogen.per.mg<-glyc$Tissue.mass*(200/glyc$Glycogen)
glyc2<-cbind(glyc, Glycogen.per.mg)
head(glyc2)
glyc2
glyc.rm<-(glyc2$Glycogen.per.mg[-20])
max(glyc.rm$Glycogen.per.mg)
head(glyc.rm)
max(glyc.rm)
min(glyc.rm)
setwd('/Users/emmatimminsschiffman/Documents/Dissertation/secondary stress/glycogen')
glyc<-read.csv('glycogen.csv', header=T)
Glycogen.per.mg<-glyc$Tissue.mass*(200/glyc$Glycogen)
glyc2<-cbind(glyc, Glycogen.per.mg)
glyc.rm<-(glyc2$Glycogen.per.mg[-20])
mean(glyc.rm)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,#
                      conf.interval=.95, .drop=TRUE) {#
    require(plyr)#
#
    # New version of length which can handle NA's: if na.rm==T, don't count them#
    length2 <- function (x, na.rm=FALSE) {#
        if (na.rm) sum(!is.na(x))#
        else       length(x)#
    }#
#
    # This does the summary. For each group's data frame, return a vector with#
    # N, mean, and sd#
    datac <- ddply(data, groupvars, .drop=.drop,#
      .fun = function(xx, col) {#
        c(N    = length2(xx[[col]], na.rm=na.rm),#
          mean = mean   (xx[[col]], na.rm=na.rm),#
          sd   = sd     (xx[[col]], na.rm=na.rm)#
        )#
      },#
      measurevar#
    )#
# Rename the "mean" column    #
    datac <- rename(datac, c("mean" = measurevar))#
#
    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean#
#
    # Confidence interval multiplier for standard error#
    # Calculate t-statistic for confidence interval: #
    # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1#
    ciMult <- qt(conf.interval/2 + .5, datac$N-1)#
    datac$ci <- datac$se * ciMult#
#
    return(datac)#
}
summarySE(glyc.rm, measurevar='glyc.rm')
library(Rmisc)
CI(glyc.rm)
6961.271-5555.379