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Pearson correlation calc

#diurnal.pastedcount.txt
genename	SRR2132408      SRR2132409      SRR2132410      SRR2132411      SRR2132412      SRR2132414      SRR2132416      SRR2132417      SRR2132418      SRR2132419      SRR2132420      SRR2132424      SRR2132425      SRR2132426      SRR2132427      SRR2132428      SRR2132429    SRR2132430       SRR2132431      SRR2132432      SRR2132433      SRR2132435      SRR2132436      SRR2132437      SRR2132440      SRR2132441      SRR2132442      SRR2132443      SRR2132444      SRR2132445      SRR2132448      SRR2132450      SRR2132451      SRR2132456    SRR2132457
Cre01.g000017.v5.5	12	7	14	2	0	6	0	0	3	4	2	13	14	4	18	46	18	2	4	10	50	26	16	14	0	4	2	2	0	0	2	6	20	32	28	
Cre01.g000033.v5.5	22	29	10	16	6	6	4	5	10	6	27	25	81	16	43	117	51	8	8	24	116	53	15	36	11	27	13	12	0	21	10	23	21	94	81	
Cre01.g000050.v5.5	114	136	236	260	252	180	236	180	328	126	318	110	316	226	224	281	96	446	73	74	180	142	120	161	234	434	182	202	76	228	122	200	304	180	150	
Cre01.g000100.v5.5	148	132	222	172	176	146	136	150	176	48	234	112	312	208	296	550	137	28	66	44	324	142	104	190	150	318	208	200	60	162	92	144	352	196	173	
Cre01.g000150.v5.5	324	222	627	558	584	388	38	38	88	46	136	74	238	74	118	112	46	1516	500	523	2166	584	310	442	416	1018	468	88	22	78	65	101	140	109	109	

library(rsgcc)
## try http:// if https:// URLs are not supported
#source("https://bioconductor.org/biocLite.R")
#biocLite("DESeq")
library(DESeq)
dct = read.table("D:/analysis/Creinhardtii/tophat/diurnal.pastedcount.txt", header=TRUE, row.names=1 )
cond <- factor(c('light', 'light', 'light', 'light', 'light', 'light', 'dark', 'dark', 'dark', 'dark', 'dark', 'dark', 'dark', 'dark', 'dark', 'dark', 'dark', 'light', 'light', 'light', 'light', 'light', 'light', 'light', 'light', 'light', 'light', 'dark', 'dark', 'dark', 'dark', 'dark', 'dark', 'dark', 'dark'))
cds = newCountDataSet(dct,conditions = cond)
cds <- estimateSizeFactors( cds )
normalizedCounts <- t( t(counts(cds)) / sizeFactors(cds) )
pcc <- cor.matrix(normalizedCounts , cpus = 2,
            cormethod = "PCC", style = "all.pairs",
            pernum = 0, sigmethod = "two.sided",
            output = "matrix")
pcc_neighbor <- cor.matrix(normalizedCounts , cpus = 5,
            cormethod = "PCC", style = "adjacent.pairs",
            pernum = 0, sigmethod = "two.sided",
            output = "matrix")