An S4 class to represent comparisons between two steady-state conditions

Description

INSPEcT_diffsteady is a class able to store the results of the comparisons between two steady states. An object of class INSPEcT_diffsteady is created with the method "compareSteady" applied on two "INSPEcT" objects (see compareSteady).

Usage

synthesis(object)

processing(object)

degradation(object)

## S4 method for signature 'INSPEcT_diffsteady'
show(object)

## S4 method for signature 'INSPEcT_diffsteady'
synthesis(object)

## S4 method for signature 'INSPEcT_diffsteady'
processing(object)

## S4 method for signature 'INSPEcT_diffsteady'
degradation(object)

Arguments

Details

Methods associated to the class INSPEcT_diffsteady are:

• synthesis: Accessor to the synthesis rates and their comparisons.

• degradation: Accessor to the degradation rates and their comparisons.

• processing: Accessor to the processing rates and their comparisons.

• plotMA: visualization fuction for rates comparisons, see plotMA

Value

Method show for objects of class INSPEcT_model returns the number of the genes that have been modeled

Slots

synthesis

A data.frame which contains both input data and comparisons results regarding synthesis rates

degradation

A data.frame which contains both input data and comparisons results regarding degradation rates

processing

A data.frame which contains both input data and comparisons results regarding processing rates

Examples

data('simData3rep', package='INSPEcT')
data('simData3rep_2', package='INSPEcT')
diffrates <- compareSteady(simData3rep, simData3rep_2)
head(synthesis(diffrates))
head(processing(diffrates))
head(degradation(diffrates))

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(INSPEcT)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: BiocParallel
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/INSPEcT/INSPEcT_diffsteady-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: INSPEcT_diffsteady-class
> ### Title: An S4 class to represent comparisons between two steady-state
> ###   conditions
> ### Aliases: INSPEcT_diffsteady-class degradation
> ###   degradation,INSPEcT_diffsteady-method processing
> ###   processing,INSPEcT_diffsteady-method show,INSPEcT_diffsteady-method
> ###   synthesis synthesis,INSPEcT_diffsteady-method
> 
> ### ** Examples
> 
> data('simData3rep', package='INSPEcT')
> data('simData3rep_2', package='INSPEcT')
> diffrates <- compareSteady(simData3rep, simData3rep_2)
> head(synthesis(diffrates))
  condition1   variance1 condition2   variance2 samplesize1 samplesize2
1   3.854221 0.028188427   4.042793 0.055307735           3           3
2   3.049255 0.019488760   3.203442 0.010301351           3           3
3   4.354099 0.023354702   4.223092 0.010076628           3           3
4   4.213518 0.033074733   4.358450 0.048251461           3           3
5   4.032123 0.017870494   3.551907 0.043142614           3           3
6   2.423534 0.005658333   2.272448 0.001584867           3           3
  log2mean     log2fc       pval      padj
1 3.948507  0.1885720 0.32152260 0.7637116
2 3.126349  0.1541865 0.19670314 0.6602565
3 4.288595 -0.1310071 0.28240819 0.7361887
4 4.285984  0.1449326 0.42842817 0.8286812
5 3.792015 -0.4802160 0.02811316 0.3896046
6 2.347991 -0.1510857 0.03711851 0.4316105
> head(processing(diffrates))
  condition1  variance1 condition2 variance2 samplesize1 samplesize2 log2mean
1  15.203271 10.3922961  17.644199 70.427082           3           3 4.033716
2  43.991826  7.5759882  47.567206  5.606298           3           3 5.515529
3   9.036237  0.7149832   9.133954  1.792719           3           3 3.183481
4  12.453646  8.9077566  17.403105  6.436234           3           3 3.879885
5   9.412245  5.3344776   7.625994 10.621764           3           3 3.082732
6  29.621827  8.3876154  26.296310 60.171196           3           3 4.802689
      log2fc      pval      padj
1  0.2148122 0.6464040 0.9846846
2  0.1127318 0.1657527 0.8164551
3  0.0155174 0.9198490 0.9947881
4  0.4827765 0.1076860 0.7406042
5 -0.3036136 0.5005656 0.9541432
6 -0.1718002 0.5431765 0.9679932
> head(degradation(diffrates))
  condition1  variance1 condition2    variance2 samplesize1 samplesize2
1  1.2351141 0.02221613   1.185528 0.0356923424           3           3
2  2.0374740 0.19135120   1.788392 0.0655318835           3           3
3  1.7411531 0.06523060   1.528905 0.0361781861           3           3
4  1.1491568 0.01718102   1.024288 0.0221003258           3           3
5  5.0943609 1.77347847   3.341619 1.7320857077           3           3
6  0.6476028 0.01320938   0.541821 0.0002022625           3           3
    log2mean      log2fc      pval      padj
1  0.2750872 -0.05911428 0.7404902 0.9421642
2  0.9327224 -0.18811845 0.4308459 0.8395259
3  0.7062708 -0.18754450 0.3067238 0.7729986
4  0.1175988 -0.16595378 0.3411773 0.8054793
5  2.0447241 -0.60835412 0.1972160 0.6911809
6 -0.7554653 -0.25729285 0.1602124 0.6766952
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>