I have been away.

The rpkm values are on log2 scale, hence the negative values.

Your plot shows, in my opinion, a large gc bias effect.

Kasper

On Tuesday, July 17, 2012, Sermsawat Tunlaya-Anukit wrote:

> I would like to normlization populus RNA seq with package CQN. I follow the
> code as show in manual, but after run i got the RPKM as minus(see detail
> below). What is the meaning of minus in RPKM? Or i done something wrong in
> my code? I try to visulize with cqn plot, and i think my data are not bias
> by GC content or lenght. I attached file of cqn plot for your information.
> Do you think i need to use your method to normailze data?
>
> Sincerely yours,
> Sermsawat T.
>
> #####normalize by CQN
> > #find GC content from phytozome populus genome
> > library(Rsamtools)
> > seq <- scanFa("pop.fa")
> > alph <- alphabetFrequency(seq, as.prob=TRUE)
> > gc <- rowSums(alph[,c("G", "C")])
> >
> > library(cqn)
> > library(scales)
> > raw <- read.table("raw.txt", header=FALSE)
> > cqn.raw <- cqn(raw, lengths = width(seq), x = gc, sizeFactors =
> colSums(raw), verbose = TRUE)
> RQ fit ..................
> SQN .
> >
> > #cqn plot
> > par(mfrow=c(1,2))
> > cqnplot(cqn.raw, n = 1, xlab = "GC content", lty = 1, ylim = c(1,7))
> > cqnplot(cqn.raw, n = 2, xlab = "length", lty = 1, ylim = c(1,7))
> >
> > #normalizedvalues
> > RPKM.cqn <- cqn.raw$y + cqn.raw$offset
> > head(RPKM.cqn)
>              V1          V2         V3        V4         V5         V6
> [1,] -2.3157759 -0.05011048 -1.5628979 -2.792141 -1.5042294 -2.1660771
> [2,]  3.3638330  3.46092091  3.5447488  3.333357  3.1484934  3.4531245
> [3,] -0.1924864  0.38536951  0.8976638 -1.134989 -0.1924864  0.4854206
> [4,] -2.2808497 -0.50606822 -1.5613805 -2.802241 -1.4820398 -3.1508760
> [5,] -2.2813593 -0.50602611 -1.5615417 -2.802341 -1.4822924 -3.1510596
> [6,]  4.6875943  4.68565806  2.6239807  3.531895  3.5440903  1.9465045
>             V7          V8          V9       V10        V11        V12
> [1,] -3.417837 -0.05011048 -0.05011048 -1.203393 -0.5058032 -1.4579155
> [2,]  2.900329  3.07780614  3.41975117  3.693540  3.3387816  3.3901923
> [3,] -1.369314 -0.05011048  0.58067142  0.437804  1.0192374  0.8445695
> [4,] -3.459816 -0.38013895 -0.05011048 -1.182797 -0.5064885 -1.4613012
> [5,] -3.459801 -0.38013895 -0.05011048 -1.183303 -0.5066405  0.1602583
> [6,]  2.282431  2.19639308  1.39946079  4.753675  4.8337840  2.7983964
>            V13       V14        V15        V16        V17        V18
> [1,] -4.303745 -1.844632 -3.1044275 -3.4759833 -0.1924864 -1.2015133
> [2,]  3.116199  2.942757  3.2217711  2.9451114  2.8716179  3.4992808
> [3,] -1.627192 -1.779755 -1.5114489 -0.8413003 -0.1924864  0.3091839
> [4,] -3.289903 -2.870566 -3.1055409 -3.5218687 -0.3801390 -1.2399416
> [5,] -4.290241 -2.870453 -2.1055097 -3.5218168 -0.3801390 -1.2400014
> [6,]  3.685705  3.460004  0.8445695  2.4490306  2.6279081  0.5358322
> > sessionInfo()
> R version 2.15.1 (2012-06-22)
> Platform: x86_64-pc-mingw32/x64 (64-bit)
> locale:
> [1] LC_COLLATE=English_United States.1252
> [2] LC_CTYPE=English_United States.1252
> [3] LC_MONETARY=English_United States.1252
> [4] LC_NUMERIC=C
> [5] LC_TIME=English_United States.1252
> attached base packages:
> [1] splines   stats     graphics  grDevices utils     datasets  methods
> [8] base
> other attached packages:
>  [1] scales_0.2.1          Rsamtools_1.8.5       Biostrings_2.24.1
>  [4] GenomicRanges_1.8.7   IRanges_1.14.4        BiocGenerics_0.2.0
>  [7] cqn_1.2.0             quantreg_4.81         SparseM_0.96
> [10] preprocessCore_1.18.0 nor1mix_1.1-3         mclust_3.5
> loaded via a namespace (and not attached):
>  [1] bitops_1.0-4.1     colorspace_1.1-1   dichromat_1.2-4
> labeling_0.1
>  [5] munsell_0.3        plyr_1.7.1         RColorBrewer_1.0-5
> stats4_2.15.1
>  [9] stringr_0.6        zlibbioc_1.2.0
>

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