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Last data update: 2014.03.03

R: Output Significant Genes

topGene

R Documentation

Output Significant Genes

Description

Identify differentially expressed genes using rank product method

Usage


     topGene(x,cutoff=NULL,method="pfp",num.gene=NULL,logged=TRUE,logbase=2,gene.names=NULL)

Arguments

`x`	the value returned by the function RP, RP.advance or Rsum.advance
`cutoff`	threshold in pfp used to select genes
`method`	If cutoff is provided, the method needs to be selected to identify genes."pfp" uses percentage of false prediction, which is the default setting. "pval" used p-value which is less stringent than pfp
`num.gene`	number of candidate genes of interests, if cutoff is provided, this will be ignored
`logged`	if "TRUE", data has bee logged, otherwise set it to "FALSE"
`logbase`	base used when taking log, used to restore the fold change.The default value is 2, this will be ignored if logged=FALSE
`gene.names`	if "NULL", no gene name will be attached to the output table

Value

Two tables of identified genes with gene.index: index of gene in the original data set RP/Rsum: Computed rank product/sum for each gene FC:(class1/class2): Expression Fold change of class 1/ class 2. pfp: estimated pfp for each gene if the gene is used as cutoff point P.value: estimated p-value for each gene

Table 1 list genes that are up-regulated under class 2, Table 1 ist genes that are down-regulated under class 2,

Author(s)

Fangxin Hong fhong@salk.edu

References

Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P.(2004) Rank Products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letter, 57383-92

Examples


      # Load the data of Golub et al. (1999). data(golub) 
      # contains a 3051x38 gene expression
      # matrix called golub, a vector of length called golub.cl 
      # that consists of the 38 class labels,
      # and a matrix called golub.gnames whose third column 
      # contains the gene names.
      data(golub)

      #use a subset of data as example, apply the rank 
      #product method
      subset <- c(1:4,28:30)
      #Setting rand=123, to make the results reproducible,

      #identify genes 
      RP.out <- RP(golub[,subset],golub.cl[subset],rand=123)  

      #get two lists of differentially expressed genes 
      #by setting FDR (false discivery rate) =0.05

      table=topGene(RP.out,cutoff=0.05,method="pfp",logged=TRUE,logbase=2,
                   gene.names=golub.gnames[,3])
      table$Table1
      table$Table2

      #using pvalue<0.05
      topGene(RP.out,cutoff=0.05,method="pval",logged=TRUE,logbase=2,
                   gene.names=golub.gnames[,3])

      #by selecting top 10 genes

      topGene(RP.out,num.gene=10,gene.names=golub.gnames[,3])

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(RankProd)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/RankProd/topGene.Rd_%03d_medium.png", width=480, height=480)
> ### Name: topGene
> ### Title: Output Significant Genes
> ### Aliases: topGene
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
>       # Load the data of Golub et al. (1999). data(golub) 
>       # contains a 3051x38 gene expression
>       # matrix called golub, a vector of length called golub.cl 
>       # that consists of the 38 class labels,
>       # and a matrix called golub.gnames whose third column 
>       # contains the gene names.
>       data(golub)
> 
>       #use a subset of data as example, apply the rank 
>       #product method
>       subset <- c(1:4,28:30)
>       #Setting rand=123, to make the results reproducible,
> 
>       #identify genes 
>       RP.out <- RP(golub[,subset],golub.cl[subset],rand=123)  
Rank Product analysis for two-class case 
 
Starting 100 permutations... 
Computing pfp .. 
Outputing the results .. 
> 
>       #get two lists of differentially expressed genes 
>       #by setting FDR (false discivery rate) =0.05
> 
>       table=topGene(RP.out,cutoff=0.05,method="pfp",logged=TRUE,logbase=2,
+                    gene.names=golub.gnames[,3])
Table1: Genes called significant under class1 < class2 

Table2: Genes called significant under class1 > class2 

>       table$Table1
                 gene.index RP/Rsum FC:(class1/class2)    pfp P.value
HG3494-HT3688_at        436  7.3165             0.2369 0.0200   0e+00
HG2724-HT2820_at        418  7.9323             0.2689 0.0100   0e+00
D87116_at               346  8.2189             0.2605 0.0067   0e+00
HG987-HT987_at          475  8.5880             0.3031 0.0100   1e-04
D88422_at               378 10.2910             0.2684 0.0120   1e-04
>       table$Table2
                        gene.index RP/Rsum FC:(class1/class2)    pfp P.value
AFFX-HUMRGE/M10098_3_at          6  8.1213             4.3141 0.0100   0e+00
AFFX-HUMRGE/M10098_5_at          4  9.2133             4.0339 0.0100   0e+00
AFFX-HUMRGE/M10098_M_at          5  9.8437             3.8488 0.0067   0e+00
D88270_at                      377 12.7578             3.5353 0.0200   2e-04
AFFX-M27830_5_at                14 14.3431             3.2518 0.0200   2e-04
> 
>       #using pvalue<0.05
>       topGene(RP.out,cutoff=0.05,method="pval",logged=TRUE,logbase=2,
+                    gene.names=golub.gnames[,3])
Table1: Genes called significant under class1 < class2 

Table2: Genes called significant under class1 > class2 

$Table1
                         gene.index RP/Rsum FC:(class1/class2)    pfp P.value
HG3494-HT3688_at                436  7.3165             0.2369 0.0200  0.0000
HG2724-HT2820_at                418  7.9323             0.2689 0.0100  0.0000
D87116_at                       346  8.2189             0.2605 0.0067  0.0000
HG987-HT987_at                  475  8.5880             0.3031 0.0100  0.0001
D88422_at                       378 10.2910             0.2684 0.0120  0.0001
D87433_at                       350 22.2528             0.3543 0.1550  0.0019
D26579_at                       141 25.2467             0.3970 0.1886  0.0026
D26308_at                       140 32.0381             0.5013 0.3512  0.0056
J03909_at                       496 35.9833             0.4730 0.4400  0.0079
D89052_at                       381 36.2952             0.4630 0.4030  0.0081
AFFX-HUMTFRR/M11507_3_at         13 37.0859             0.5117 0.3936  0.0087
HG3286-HT3463_at                431 40.5498             0.5563 0.4775  0.0115
AFFX-HUMTFRR/M11507_M_at         12 40.7818             0.5309 0.4485  0.0117
D49817_at                       215 41.3887             0.5280 0.4393  0.0123
D10495_at                        68 41.7633             0.4748 0.4207  0.0126
HG620-HT620_at                  470 42.8890             0.5305 0.4250  0.0136
D29643_at                       155 44.0282             0.5360 0.4271  0.0145
D83735_at                       303 44.1820             0.6354 0.4100  0.0148
D00726_at                        62 44.8851             0.5530 0.4058  0.0154
D43636_at                       198 46.7786             0.5724 0.4275  0.0171
D50915_at                       229 47.9356             0.6938 0.4457  0.0187
D30755_at                       160 49.3573             0.5308 0.4618  0.0203
D14874_at                       108 49.8597             0.5693 0.4522  0.0208
D50663_at                       219 51.8963             0.5953 0.4883  0.0234
D26129_at                       139 52.6567             0.5489 0.4892  0.0245
J03040_at                       483 55.4157             0.6112 0.5469  0.0284
D00761_at                        64 56.4930             0.6040 0.5567  0.0301
AF010193_at                      52 58.0885             0.6096 0.5768  0.0323
HG2810-HT2921_at                420 59.9292             0.6070 0.6069  0.0352
D13639_at                        85 60.9586             0.8286 0.6140  0.0368
D14811_at                       105 61.4964             0.6003 0.6061  0.0376
D16469_at                       115 61.6077             0.6106 0.5894  0.0377
J04027_at                       498 62.7902             0.5894 0.5991  0.0395
HG2788-HT2896_at                419 64.1737             0.5948 0.6162  0.0419
D67029_at                       266 64.2627             0.6410 0.6011  0.0421
D10202_at                        67 65.1456             0.5783 0.6083  0.0438
D87937_at                       374 66.0403             0.6749 0.6141  0.0454
D13641_at                        87 66.8993             0.6277 0.6189  0.0470
HG4662-HT5075_at                466 67.0424             0.5835 0.6064  0.0473
D50840_at                       222 68.4732             0.6578 0.6240  0.0499

$Table2
                        gene.index RP/Rsum FC:(class1/class2)    pfp P.value
AFFX-HUMRGE/M10098_3_at          6  8.1213             4.3141 0.0100  0.0000
AFFX-HUMRGE/M10098_5_at          4  9.2133             4.0339 0.0100  0.0000
AFFX-HUMRGE/M10098_M_at          5  9.8437             3.8488 0.0067  0.0000
D88270_at                      377 12.7578             3.5353 0.0200  0.0002
AFFX-M27830_5_at                14 14.3431             3.2518 0.0200  0.0002
D50918_at                      232 23.9021             2.4853 0.1050  0.0013
D11086_at                       74 26.8974             2.3623 0.1329  0.0019
HG1078-HT1078_at               389 31.8953             2.2554 0.2200  0.0035
D83776_at                      304 35.0233             2.1143 0.2722  0.0049
D86967_at                      329 35.8569             2.1269 0.2690  0.0054
HG4036-HT4306_at               451 38.2383             2.1038 0.3027  0.0067
D87076_at                      344 38.3284             2.0678 0.2775  0.0067
D86479_at                      323 38.9372             2.0832 0.2746  0.0071
D30742_at                      159 39.6655             2.0609 0.2707  0.0076
D86983_at                      337 41.5344             1.9518 0.2953  0.0089
D83780_at                      306 45.0103             1.9869 0.3662  0.0117
D42053_at                      193 45.0889             1.9259 0.3459  0.0118
AFFX-HSAC07/X00351_3_st         17 47.0776             1.9811 0.3839  0.0138
D87465_at                      369 48.1772             1.8293 0.3963  0.0151
D86970_at                      330 52.8865             1.8973 0.5055  0.0202
J03600_at                      491 54.3202             1.6096 0.5129  0.0215
D63486_at                      248 56.5033             1.8911 0.5523  0.0243
D38524_at                      182 56.5562             1.8116 0.5300  0.0244
HG1612-HT1612_at               394 56.6243             1.8526 0.5088  0.0244
D80005_at                      285 58.1699             1.6182 0.5276  0.0264
D78012_at                      267 61.4194             1.7997 0.6019  0.0313
D63880_at                      259 63.1282             1.7530 0.6219  0.0336
HG3039-HT3200_at               425 63.2380             1.7237 0.6021  0.0337
D87078_at                      345 66.1767             1.6936 0.6710  0.0389
D87438_at                      354 67.6645             1.7374 0.6907  0.0414
D43948_at                      202 68.8017             1.7289 0.6994  0.0434
AF003743_at                     44 69.1536             1.4669 0.6875  0.0440
HG4068-HT4338_at               452 69.3503             1.5081 0.6703  0.0442
D83032_at                      297 69.4787             1.7436 0.6535  0.0444
D29642_at                      154 69.7398             1.6527 0.6406  0.0448
D50930_at                      237 70.1365             1.7148 0.6308  0.0454
HG2825-HT2949_at               421 71.8410             1.6282 0.6568  0.0486

> 
>       #by selecting top 10 genes
> 
>       topGene(RP.out,num.gene=10,gene.names=golub.gnames[,3])
Table1: Genes called significant under class1 < class2 

Table2: Genes called significant under class1 > class2 

$Table1
                 gene.index RP/Rsum FC:(class1/class2)    pfp P.value
HG3494-HT3688_at        436  7.3165             0.2369 0.0200  0.0000
HG2724-HT2820_at        418  7.9323             0.2689 0.0100  0.0000
D87116_at               346  8.2189             0.2605 0.0067  0.0000
HG987-HT987_at          475  8.5880             0.3031 0.0100  0.0001
D88422_at               378 10.2910             0.2684 0.0120  0.0001
D87433_at               350 22.2528             0.3543 0.1550  0.0019
D26579_at               141 25.2467             0.3970 0.1886  0.0026
D26308_at               140 32.0381             0.5013 0.3512  0.0056
J03909_at               496 35.9833             0.4730 0.4400  0.0079
D89052_at               381 36.2952             0.4630 0.4030  0.0081

$Table2
                        gene.index RP/Rsum FC:(class1/class2)    pfp P.value
AFFX-HUMRGE/M10098_3_at          6  8.1213             4.3141 0.0100  0.0000
AFFX-HUMRGE/M10098_5_at          4  9.2133             4.0339 0.0100  0.0000
AFFX-HUMRGE/M10098_M_at          5  9.8437             3.8488 0.0067  0.0000
D88270_at                      377 12.7578             3.5353 0.0200  0.0002
AFFX-M27830_5_at                14 14.3431             3.2518 0.0200  0.0002
D50918_at                      232 23.9021             2.4853 0.1050  0.0013
D11086_at                       74 26.8974             2.3623 0.1329  0.0019
HG1078-HT1078_at               389 31.8953             2.2554 0.2200  0.0035
D83776_at                      304 35.0233             2.1143 0.2722  0.0049
D86967_at                      329 35.8569             2.1269 0.2690  0.0054

> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>