Oooo thank you for your detailed input. I will definitely try this out.
How would you plot the results?
Hello all,
This is the 3rd thread concerning this issue and we have made steady progress. I sincerely appreciate the inputs of cpad0112 and h.mon for their efforts
Have: * RNA expression data for 12 time points, 1 replicate * List of genes of interest and their expression data http://m.uploadedit.com/bbtc/1512924884525.txt [Sample data, does not contain all 100 genes]
Want:
Expression pattern clustering like this where x axis is time and y axis is relative expression (or something like it)

Have tried: * Timecourse (R) which apparently does not accept if there is only 1 replicate [C: Cannot get result from TIMECOURSE (R PROGRAM)]
Question: Do you know of ANOTHER program that can achieve a similar result?
Possible solutions: 1) Take polynomial trend line of each line (gene) of expression data, cluster them accordingly manually. Problem: do not know how to get polynomial equation of each line of data using R.
A simple way to go about it may be k-means clustering, like
dat<- read.table('http://m.uploadedit.com/bbtc/1512924884525.txt')
kmeans(dat, centers= 3)
To get a reasonable guess for the number of clusters you could look at the between_SS / total_SS statistic, start with a small number and increase until the between_SS / total_SS stops increasing sharply.
Another useful method may be non-negative matrix factorization as implemented in e.g. the NMF package. The vignettes of NMF are quite useful. An example could be:
library(NMF)
dat<- read.table('http://m.uploadedit.com/bbtc/1512924884525.txt')
xnmf<- nmf(dat, rank= 4) # Again, you need to "guess" the number of "pseudogenes"
xnmf@fit@H
The H matrix essentially gives you a small set of "pesudogenes" that together describe well the (much larger) full set of genes.
Oooo thank you for your detailed input. I will definitely try this out.
How would you plot the results?
ANSWER: DOWNLOAD MeV SOFTWARE USE K-MEANS CLUSTERING, PEARSON.
YOU GET THE FOLLOWING: 
GOOD ENOUGH FOR ME.
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