PCA plot on genes and color by batch and plate information

Question

[mohammedtoufiq91]

Hi,

I have a question about plotting PCA on genes, I have samples in rows with associated annotations (Batch and Plate information) and genes in the columns. I have performed PCA on samples and I am familiar with it. However, I am interested to plot PCA on genes now and color by batch and plate information. Please assist me with this. The example of the data is given below.

library(FactoMineR)
library("ggfortify")
library("FactoMineR")
library("factoextra")

Neg_Dct <- read.csv(file = "./Test.csv",stringsAsFactors = FALSE)
Neg_Dct 

dput(Neg_Dct)
structure(list(ID = c("S1_1", "S1_2", "S1_3", "S1_4", "S1_5", 
"S1_6", "S1_7", "S1_8", "S1_9", "S1_10", "S1_11", "S1_12", "S1_13", 
"S1_14", "S1_15", "S1_16", "S1_17", "S1_18", "S1_19", "S1_20", 
"S1_21", "S1_22", "S1_23", "S1_24", "S1_25"), Batch = c("Batch_1", 
"Batch_1", "Batch_1", "Batch_2", "Batch_2", "Batch_2", "Batch_1", 
"Batch_1", "Batch_1", "Batch_2", "Batch_2", "Batch_2", "Batch_1", 
"Batch_1", "Batch_2", "Batch_2", "Batch_2", "Batch_1", "Batch_1", 
"Batch_2", "Batch_2", "Batch_2", "Batch_1", "Batch_1", "Batch_1"
), Plate = c("Plate_1", "Plate_2", "Plate_1", "Plate_2", "Plate_1", 
"Plate_2", "Plate_1", "Plate_2", "Plate_1", "Plate_2", "Plate_1", 
"Plate_2", "Plate_1", "Plate_2", "Plate_1", "Plate_2", "Plate_1", 
"Plate_2", "Plate_1", "Plate_2", "Plate_1", "Plate_1", "Plate_2", 
"Plate_1", "Plate_2"), Gene_1 = c(2.74566, 2.41701, 1.28156, 
2.5121, 1.99305, 1.80165, 3.17652, 3.5806, 1.79384, 1.28138, 
1.89935, 2.5723, 2.14152, 1.40297, 1.88353, 2.05175, 1.97743, 
1.92165, 2.05574, 0.522052, 2.40835, 0.991803, 1.67695, 2.72437, 
1.22242), Gene_2 = c(9.21655, 9.21203, 10.876, NA, 10.222, 8.64146, 
9.5818, NA, 9.38491, NA, 8.72181, 11.4064, 9.25437, NA, 9.79516, 
9.10643, 8.64458, 9.695, 7.95871, 12.0041, 8.19037, 8.19788, 
9.03349, 6.66333, 9.83651), Gene_3 = c(1.24291, 1.21489, 0.655026, 
0.785034, 0.856161, 0.245615, 1.33763, 0.198923, 0.532218, 1.00889, 
0.186925, 1.37128, 0.905605, -0.408769, 0.453087, 1.15631, 0.410721, 
0.625778, 0.417797, 0.0365906, 0.99665, 0.244682, 0.862058, 0.609123, 
0.35954), Gene_4 = c(3.17245, 3.03454, 3.12256, 3.29299, 3.66615, 
2.9414, 3.32122, 3.58629, 2.7757, 3.07628, 2.55718, 3.29047, 
2.71563, 2.5873, 3.09971, 3.59135, 3.01682, 2.23962, 3.17484, 
2.94082, 3.336, 2.87547, 3.45458, 3.43779, 3.18705), Gene_5 = c(3.99519, 
4.35548, 4.00643, 4.02169, 3.85387, 3.54247, 3.81633, 2.82153, 
3.62866, 4.47204, 2.49623, 5.18278, 3.65158, 3.04052, 3.97154, 
4.17206, 3.21916, 3.64224, 3.42668, 3.51722, 3.83094, 2.9252, 
3.90967, 3.07917, 2.94932), Gene_6 = c(3.91788, 4.14593, 2.91717, 
3.62728, 4.02476, 5.54273, 3.39246, 3.78731, 4.07341, 4.04612, 
2.59587, 8.49205, 2.3215, 2.63217, 2.56598, 2.27716, 2.90267, 
3.99974, 3.39793, 3.27778, 3.86779, 2.95839, 4.31887, 3.5999, 
2.68092), Gene_7 = c(8.22592, 7.68958, 9.191, 7.00414, 7.82801, 
6.87766, 7.70689, NA, 6.67957, 7.62084, 7.42038, 10.2766, 6.66582, 
6.57067, 7.29663, 7.34165, 7.55647, 6.73911, 6.79017, 7.8713, 
6.6699, 7.04384, 8.14006, 6.89507, 7.50916), Gene_8 = c(4.80301, 
4.95246, 2.92675, 3.88674, 2.92132, 3.44228, 4.58713, 4.84111, 
3.05712, 3.29154, 4.03648, 4.38404, 4.16168, 2.93601, 2.84335, 
3.39719, 3.46238, 2.94928, 3.55869, 2.49082, 4.08837, 2.34332, 
3.6339, 4.55228, 2.54387), Gene_9 = c(7.8863, 6.88329, 7.9638, 
6.17448, 7.57994, 7.79241, 8.43658, 3.6191, 7.07619, 7.04978, 
6.63005, 11.5974, 7.80057, 4.03935, 8.55976, 9.19217, 7.66456, 
6.95455, 7.08865, 7.78014, 7.66009, 4.76624, 7.60779, 6.42455, 
7.34804), Gene_10 = c(4.48595, 4.729, 4.60472, 4.62689, 4.63785, 
4.76703, 4.44203, 5.78705, 3.77214, 4.47739, 3.76823, 5.44039, 
4.13479, 4.51685, 3.32923, 4.72064, 3.90275, 4.29956, 4.77556, 
4.10673, 4.03972, 4.24786, 4.99717, 5.28136, 4.38755), Gene_11 = c(NA, 
NA, 12.6253, NA, 13.8811, NA, 13.5465, 7.47741, 9.59239, NA, 
NA, NA, 12.4803, NA, 17.0783, 12.9235, 12.0078, 12.339, NA, 14.2595, 
13.9659, 11.3511, 13.4548, 14.3382, NA), Gene_12 = c(3.2572, 
3.66556, 2.91065, 2.70477, 3.31886, 2.70622, 3.32486, 2.1, 2.77425, 
3.14639, 2.12945, 4.05497, 2.65452, 2.56421, 1.96161, 3.1535, 
2.48488, 2.7353, 2.68683, 2.37054, 2.99183, 2.44665, 3.29592, 
3.05663, 2.85759), Gene_13 = c(5.88783, 5.28863, 6.08389, 5.5062, 
5.78523, 6.21819, 5.89286, 5.63686, 5.0966, 7.30503, 5.24945, 
6.58622, 5.38063, 4.83921, 4.84982, 6.29604, 5.41094, 4.48755, 
5.14973, 5.62539, 5.06188, 5.59829, 6.41845, 5.56475, 6.43989
), Gene_14 = c(5.25527, 5.82523, 5.69302, 4.79978, 5.31339, 5.32151, 
5.05776, NA, 5.41897, 6.76026, 4.46106, 7.00544, 4.17885, 4.96509, 
3.41261, 5.00248, 5.07504, 4.91208, 5.09344, 5.14337, 5.36001, 
4.56528, 5.48949, 5.31362, 4.97557), Gene_15 = c(3.22585, 2.99779, 
2.78224, 2.91718, 2.89006, 2.37089, 3.38242, 3.11585, 2.31141, 
3.03489, 2.22517, 3.737, 2.94255, 2.26009, 2.65806, 3.36151, 
2.32279, 2.28086, 2.43151, 1.95159, 3.16607, 2.81872, 2.98992, 
3.07958, 2.33871), Gene_16 = c(14.4875, 12.8944, NA, NA, NA, 
12.9755, 14.6313, NA, 13.3301, NA, NA, NA, NA, NA, 13.9834, NA, 
12.0268, NA, NA, NA, 13.7213, NA, 12.0839, 13.398, NA), Gene_17 = c(5.01994, 
6.1439, 5.99065, 6.6743, 5.97207, 6.35524, 4.66218, 7.08466, 
5.20557, 6.98409, 4.98144, 6.68725, 4.74617, 5.45292, 3.87166, 
5.4348, 5.54169, 5.92897, 5.66602, 5.65002, 4.49191, 5.75363, 
5.85605, 5.44937, 5.93573), Gene_18 = c(8.7047, 8.42065, 9.97416, 
10.1412, 8.75691, 8.60371, 8.41236, NA, 8.06832, 8.80413, 8.0637, 
9.47628, 8.25909, NA, 6.94259, 8.91285, 7.49903, 7.57374, 8.05756, 
8.55374, 8.24625, 7.9067, 8.26153, 7.31693, 7.92083), Gene_19 = c(4.24694, 
4.36666, 4.60216, 4.64256, 5.0665, 4.46013, 4.14319, 3.74303, 
4.09912, 3.85835, 4.00904, 3.81451, 4.36853, 3.59563, 4.61738, 
4.81951, 4.05342, 3.33607, 4.2358, 4.38918, 4.43542, 4.30526, 
3.56997, 3.71209, 4.24122), Gene_20 = c(3.61052, 2.098, 3.18373, 
3.53195, 3.38654, 3.80873, 3.17707, 2.81883, 2.81772, 3.5725, 
2.65038, 4.19567, 3.10054, 3.1625, 2.09974, 2.84097, 3.24043, 
3.37526, 3.08749, 2.6041, 3.03967, 2.42457, 3.32517, 3.16862, 
3.17476), Gene_21 = c(6.23736, 7.05642, 6.25908, 6.38647, 6.36372, 
7.72727, 5.8454, 6.78528, 5.79207, 5.96044, 5.64355, 7.99332, 
5.93347, 5.98171, 5.49377, 5.99234, 5.70238, 6.39785, 5.39863, 
6.43829, 5.75369, 7.20047, 6.65913, 6.01353, 6.38686), Gene_22 = c(1.81799, 
2.37183, 1.17051, 1.91855, 1.54116, 1.97239, 1.85643, 2.15549, 
1.72797, 1.42215, 1.39719, 3.54795, 1.879, 1.06311, 1.15683, 
1.5778, 1.35862, 2.09668, 1.42871, 0.867846, 1.26178, 0.404501, 
1.25818, 2.19537, 1.13493), Gene_23 = c(2.78058, 2.53238, 3.14276, 
3.08658, 3.28497, 3.35548, 2.76634, 2.50289, 2.56554, 3.36543, 
2.33317, 3.42169, 2.70564, 2.97701, 2.33172, 2.96386, 2.85909, 
2.51073, 2.76909, 2.65596, 2.59477, 2.89682, 3.27021, 2.80932, 
3.04574), Gene_24 = c(2.40581, 1.87007, 2.38937, 2.96403, 2.75568, 
2.28178, 1.71635, 1.88332, 1.84544, 2.54554, 1.68298, 2.56284, 
2.35033, 1.85742, 1.66729, 2.3666, 2.0839, 2.1535, 2.22574, 1.83554, 
2.22627, 2.26724, 2.62143, 2.3956, 2.62112), Gene_25 = c(2.69548, 
2.11143, 2.51654, 2.48409, 2.74743, 2.50751, 2.72225, 1.94942, 
2.27108, 2.65104, 1.99515, 2.95257, 2.31222, 1.95298, 2.35311, 
2.7179, 2.10779, 2.2902, 2.29184, 1.99961, 2.4081, 2.10598, 2.35059, 
2.18436, 2.1967), Gene_26 = c(10.475, 10.3477, 9.52332, 6.89309, 
10.4247, 8.5301, 9.1303, 5.0003, 9.59585, 8.75312, 8.24006, 9.60621, 
10.1721, 5.86301, 10.3064, 11.2975, 9.65141, 8.69041, 7.868, 
10.0429, 10.4186, 8.22059, 10.6262, 10.5224, 10.4781), Gene_27 = c(3.88185, 
4.57222, 4.11611, 3.97254, 4.70415, 4.95692, 4.11781, 6.1079, 
3.00593, 4.07605, 3.46867, 4.65033, 3.99537, 4.5666, 4.47645, 
4.6634, 3.89716, 3.08337, 4.05497, 4.09892, 4.39914, 3.98082, 
4.28408, 5.25143, 4.32329), Gene_28 = c(2.87576, 3.64546, 2.06121, 
3.21908, 2.41477, 3.0286, 1.91063, 5.77133, 2.85094, 1.85037, 
2.26775, 2.67921, 3.6171, 2.14517, 1.46753, 1.88992, 2.5231, 
1.33496, 2.39585, 1.73231, 4.29797, 1.23472, 2.02585, 3.7009, 
1.98129), Gene_29 = c(6.60579, 5.17272, 5.91187, 5.90883, 6.07068, 
5.22712, 6.14627, 4.50813, 5.45807, 5.21917, 6.04496, 6.72243, 
4.70608, 3.599, 4.53333, 5.7917, 6.26182, 6.32342, 5.16135, 4.99482, 
5.78061, 3.5228, 5.70011, 5.64474, 6.22901), Gene_30 = c(0.00127792, 
0.153625, -0.998857, -0.536397, -0.648177, -1.08603, -0.0586834, 
-0.954571, -0.709482, -0.813375, -1.05033, 0.0500174, -0.657315, 
-1.40147, -0.806338, -0.400814, -0.866002, -0.642641, -0.973266, 
-1.51081, -0.348259, -1.49061, -0.674944, -0.423562, -1.08939
)), class = "data.frame", row.names = c(NA, -25L))

Thank you,

Toufiq

Answer 1

[Ram]

You can autoplot (library(ggfortify) first) and use various parameters such as color and shape to customize data points.

autoplot(
    prcomp(Neg_Dct[,c(-1,-2)]),
    data = Neg_Dct,
    colour = 'Batch',
    shape = 'Plate',
    label = TRUE,
    size = 6,
    label.colour = 'black')

Or you can add a new column: Neg_Dct$Batch_Plate <- paste(Neg_Dct$Plate, Neg_Dct$Batch, sep="/") and use color=Batch_Plate (after excluding that column in the prcomp call).

Answer 2

[ATpoint]

This is not PCA-specific but rather a ggplot question itself. Please refer to the many online resources towards ggplot customization. With some dummy data:

#/ Some dummy data:
library(DESeq2)
dds <- makeExampleDESeqDataSet(n=2000, m=20)
vsd <- assay(vst(dds))

#/ PCA:
pca <- prcomp(t(vsd))
PC1=pca$x[,1]
PC2=pca$x[,2]

toPlot <- data.frame(Plate = paste0(rep("Plate", 20), seq(1,4)),
                     Batch =  paste0(rep("Batch", 20), seq(1,2)),
                     PC1, 
                     PC2)

#/ ggplot:
ggplot(data = toPlot, aes(x = PC1, y = PC2, color = Batch)) +
  geom_point(size = 3, aes(shape=Plate))

[mohammedtoufiq91]

@ATpoint and RamRS, thank you for the reply. The solution provided here is PCA on sample level. However, I am looking to plot PCA on genes and label them by Batch and Plate information. Genes on rows and samples on columns. Basically see cluster formation of genes on PCA with Batch and Plate level information (for instance figure below). I have transposed the data genes (rows) and samples (column). Does melt function of library(reshape2) help?

structure(c("Batch_1", "Plate_1", "2.745660", " 9.21655", " 1.2429100", 
"3.17245", "Batch_1", "Plate_2", "2.417010", " 9.21203", " 1.2148900", 
"3.03454", "Batch_1", "Plate_1", "1.281560", "10.87600", " 0.6550260", 
"3.12256", "Batch_2", "Plate_2", "2.512100", NA, " 0.7850340", 
"3.29299", "Batch_2", "Plate_1", "1.993050", "10.22200", " 0.8561610", 
"3.66615", "Batch_2", "Plate_2", "1.801650", " 8.64146", " 0.2456150", 
"2.94140", "Batch_1", "Plate_1", "3.176520", " 9.58180", " 1.3376300", 
"3.32122", "Batch_1", "Plate_2", "3.580600", NA, " 0.1989230", 
"3.58629", "Batch_1", "Plate_1", "1.793840", " 9.38491", " 0.5322180", 
"2.77570", "Batch_2", "Plate_2", "1.281380", NA, " 1.0088900", 
"3.07628", "Batch_2", "Plate_1", "1.899350", " 8.72181", " 0.1869250", 
"2.55718", "Batch_2", "Plate_2", "2.572300", "11.40640", " 1.3712800", 
"3.29047", "Batch_1", "Plate_1", "2.141520", " 9.25437", " 0.9056050", 
"2.71563", "Batch_1", "Plate_2", "1.402970", NA, "-0.4087690", 
"2.58730", "Batch_2", "Plate_1", "1.883530", " 9.79516", " 0.4530870", 
"3.09971", "Batch_2", "Plate_2", "2.051750", " 9.10643", " 1.1563100", 
"3.59135", "Batch_2", "Plate_1", "1.977430", " 8.64458", " 0.4107210", 
"3.01682", "Batch_1", "Plate_2", "1.921650", " 9.69500", " 0.6257780", 
"2.23962", "Batch_1", "Plate_1", "2.055740", " 7.95871", " 0.4177970", 
"3.17484", "Batch_2", "Plate_2", "0.522052", "12.00410", " 0.0365906", 
"2.94082", "Batch_2", "Plate_1", "2.408350", " 8.19037", " 0.9966500", 
"3.33600", "Batch_2", "Plate_1", "0.991803", " 8.19788", " 0.2446820", 
"2.87547", "Batch_1", "Plate_2", "1.676950", " 9.03349", " 0.8620580", 
"3.45458", "Batch_1", "Plate_1", "2.724370", " 6.66333", " 0.6091230", 
"3.43779", "Batch_1", "Plate_2", "1.222420", " 9.83651", " 0.3595400", 
"3.18705"), .Dim = c(6L, 25L), .Dimnames = list(c("Batch", "Plate", 
"Gene_1", "Gene_2", "Gene_3", "Gene_4"), c("S1_1", "S1_2", "S1_3", 
"S1_4", "S1_5", "S1_6", "S1_7", "S1_8", "S1_9", "S1_10", "S1_11", 
"S1_12", "S1_13", "S1_14", "S1_15", "S1_16", "S1_17", "S1_18", 
"S1_19", "S1_20", "S1_21", "S1_22", "S1_23", "S1_24", "S1_25"
)))

[Ram]

Transpose the matrix. Also, it helps to paste head(data.frame.object) - dput is not really readable.

[mohammedtoufiq91]

@RamRS, please find the data below:

head(Neg_Dct_v1)
       S1_1         S1_2         S1_3         S1_4         S1_5         S1_6        
Batch  "Batch_1"    "Batch_1"    "Batch_1"    "Batch_2"    "Batch_2"    "Batch_2"   
Plate  "Plate_1"    "Plate_2"    "Plate_1"    "Plate_2"    "Plate_1"    "Plate_2"   
Gene_1 "2.745660"   "2.417010"   "1.281560"   "2.512100"   "1.993050"   "1.801650"  
Gene_2 " 9.21655"   " 9.21203"   "10.87600"   NA           "10.22200"   " 8.64146"  
Gene_3 " 1.2429100" " 1.2148900" " 0.6550260" " 0.7850340" " 0.8561610" " 0.2456150"
Gene_4 "3.17245"    "3.03454"    "3.12256"    "3.29299"    "3.66615"    "2.94140"   
       S1_7         S1_8         S1_9         S1_10        S1_11        S1_12       
Batch  "Batch_1"    "Batch_1"    "Batch_1"    "Batch_2"    "Batch_2"    "Batch_2"   
Plate  "Plate_1"    "Plate_2"    "Plate_1"    "Plate_2"    "Plate_1"    "Plate_2"   
Gene_1 "3.176520"   "3.580600"   "1.793840"   "1.281380"   "1.899350"   "2.572300"  
Gene_2 " 9.58180"   NA           " 9.38491"   NA           " 8.72181"   "11.40640"  
Gene_3 " 1.3376300" " 0.1989230" " 0.5322180" " 1.0088900" " 0.1869250" " 1.3712800"
Gene_4 "3.32122"    "3.58629"    "2.77570"    "3.07628"    "2.55718"    "3.29047"   
       S1_13        S1_14        S1_15        S1_16        S1_17        S1_18       
Batch  "Batch_1"    "Batch_1"    "Batch_2"    "Batch_2"    "Batch_2"    "Batch_1"   
Plate  "Plate_1"    "Plate_2"    "Plate_1"    "Plate_2"    "Plate_1"    "Plate_2"   
Gene_1 "2.141520"   "1.402970"   "1.883530"   "2.051750"   "1.977430"   "1.921650"  
Gene_2 " 9.25437"   NA           " 9.79516"   " 9.10643"   " 8.64458"   " 9.69500"  
Gene_3 " 0.9056050" "-0.4087690" " 0.4530870" " 1.1563100" " 0.4107210" " 0.6257780"
Gene_4 "2.71563"    "2.58730"    "3.09971"    "3.59135"    "3.01682"    "2.23962"   
       S1_19        S1_20        S1_21        S1_22        S1_23        S1_24       
Batch  "Batch_1"    "Batch_2"    "Batch_2"    "Batch_2"    "Batch_1"    "Batch_1"   
Plate  "Plate_1"    "Plate_2"    "Plate_1"    "Plate_1"    "Plate_2"    "Plate_1"   
Gene_1 "2.055740"   "0.522052"   "2.408350"   "0.991803"   "1.676950"   "2.724370"  
Gene_2 " 7.95871"   "12.00410"   " 8.19037"   " 8.19788"   " 9.03349"   " 6.66333"  
Gene_3 " 0.4177970" " 0.0365906" " 0.9966500" " 0.2446820" " 0.8620580" " 0.6091230"
Gene_4 "3.17484"    "2.94082"    "3.33600"    "2.87547"    "3.45458"    "3.43779"   
       S1_25       
Batch  "Batch_1"   
Plate  "Plate_2"   
Gene_1 "1.222420"  
Gene_2 " 9.83651"  
Gene_3 " 0.3595400"
Gene_4 "3.18705"

Tranpose:

head(Neg_Dct)
    ID   Batch   Plate  Gene_1   Gene_2   Gene_3  Gene_4  Gene_5  Gene_6  Gene_7  Gene_8
1 S1_1 Batch_1 Plate_1 2.74566  9.21655 1.242910 3.17245 3.99519 3.91788 8.22592 4.80301
2 S1_2 Batch_1 Plate_2 2.41701  9.21203 1.214890 3.03454 4.35548 4.14593 7.68958 4.95246
3 S1_3 Batch_1 Plate_1 1.28156 10.87600 0.655026 3.12256 4.00643 2.91717 9.19100 2.92675
4 S1_4 Batch_2 Plate_2 2.51210       NA 0.785034 3.29299 4.02169 3.62728 7.00414 3.88674
5 S1_5 Batch_2 Plate_1 1.99305 10.22200 0.856161 3.66615 3.85387 4.02476 7.82801 2.92132
6 S1_6 Batch_2 Plate_2 1.80165  8.64146 0.245615 2.94140 3.54247 5.54273 6.87766 3.44228
   Gene_9 Gene_10 Gene_11 Gene_12 Gene_13 Gene_14 Gene_15 Gene_16 Gene_17  Gene_18 Gene_19
1 7.88630 4.48595      NA 3.25720 5.88783 5.25527 3.22585 14.4875 5.01994  8.70470 4.24694
2 6.88329 4.72900      NA 3.66556 5.28863 5.82523 2.99779 12.8944 6.14390  8.42065 4.36666
3 7.96380 4.60472 12.6253 2.91065 6.08389 5.69302 2.78224      NA 5.99065  9.97416 4.60216
4 6.17448 4.62689      NA 2.70477 5.50620 4.79978 2.91718      NA 6.67430 10.14120 4.64256
5 7.57994 4.63785 13.8811 3.31886 5.78523 5.31339 2.89006      NA 5.97207  8.75691 5.06650
6 7.79241 4.76703      NA 2.70622 6.21819 5.32151 2.37089 12.9755 6.35524  8.60371 4.46013
  Gene_20 Gene_21 Gene_22 Gene_23 Gene_24 Gene_25  Gene_26 Gene_27 Gene_28 Gene_29
1 3.61052 6.23736 1.81799 2.78058 2.40581 2.69548 10.47500 3.88185 2.87576 6.60579
2 2.09800 7.05642 2.37183 2.53238 1.87007 2.11143 10.34770 4.57222 3.64546 5.17272
3 3.18373 6.25908 1.17051 3.14276 2.38937 2.51654  9.52332 4.11611 2.06121 5.91187
4 3.53195 6.38647 1.91855 3.08658 2.96403 2.48409  6.89309 3.97254 3.21908 5.90883
5 3.38654 6.36372 1.54116 3.28497 2.75568 2.74743 10.42470 4.70415 2.41477 6.07068
6 3.80873 7.72727 1.97239 3.35548 2.28178 2.50751  8.53010 4.95692 3.02860 5.22712
      Gene_30
1  0.00127792
2  0.15362500
3 -0.99885700
4 -0.53639700
5 -0.64817700
6 -1.08603000