Thank You Mr. Steve and Mr. James. Its really helped me to have a clear
understanding of PCA function. Now I could able to plot and capture the 3D
plots through package:rgl.

Thanks Again..


On Tue, Jul 2, 2013 at 12:11 AM, Steve Lianoglou
<lianoglou.steve@gene.com>wrote:

> Hi,
>
> On Mon, Jul 1, 2013 at 11:07 AM, Veera Venkata Satyanarayana
> <veeru.vsn@gmail.com> wrote:
> > Dear,
> > Yes, Its worked with function "prcomp":
> >> PCA_3d<-prcomp(t(Normalized_Expresso), cor=TRUE)
> >
> > Here is the Question:
> > while ploting the object PCA_3d with scatterplot3d from
> > package:scatterplot3d, the x,y,z coordinates were given as
> PCA_3d$x[,1:3],
> > but the PCA_3d$x has 7 PC components (columns) and 7 samples as rows. If
> I
> > give the first three PC components [PC1, PC2, PC3] as coordinates, I
> > insists to know about other PCs like [PC4,PC5, PC6, PC7]. Out of all
> which
> > gives the best reliable pattern of samples on the plot.
>
> I didn't actually catch the question here.
>
> Do you want to try all possible pairs of 3 axes to see which is
> "best"? Just change the `[,1:3]` to include the PC's that you like
> (you are picking 1,2,3) to see which you think does better.
>
> Generally speaking, you should expect to get the best separation using
> the first three PCs since PC1 explains more of the variance than PC2,
> and PC2 > PC3, etc. but feel free to experiment as you like.
>
> James' suggestion to use rgl is a good one -- often times spinning the
> plot around interactively is quite helpful.
>
> -steve
>
> --
> Steve Lianoglou
> Computational Biologist
> Bioinformatics and Computational Biology
> Genentech
>



-- 
Chevala. V V S Narayana
Junior Research Fellow,
NIPGR.

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