Abrar — Feb 7, 2014, 9:40 PM
library(lattice)
kDat <- read.table("../data/GSE4051_MINI.txt", header = TRUE, row.names = 1)
str(kDat)
'data.frame': 39 obs. of 6 variables:
$ sample : int 20 21 22 23 16 17 6 24 25 26 ...
$ devStage : Factor w/ 5 levels "4_weeks","E16",..: 2 2 2 2 2 2 2 4 4 4 ...
$ gType : Factor w/ 2 levels "NrlKO","wt": 2 2 2 2 1 1 1 2 2 2 ...
$ crabHammer: num 10.22 10.02 9.64 9.65 8.58 ...
$ eggBomb : num 7.46 6.89 6.72 6.53 6.47 ...
$ poisonFang: num 7.37 7.18 7.35 7.04 7.49 ...
table(kDat$devStage)
4_weeks E16 P10 P2 P6
8 7 8 8 8
table(kDat$gType)
NrlKO wt
19 20
with(kDat, table(devStage, gType))
gType
devStage NrlKO wt
4_weeks 4 4
E16 3 4
P10 4 4
P2 4 4
P6 4 4
xyplot( eggBomb + poisonFang ~ crabHammer, kDat, outer = TRUE, grid = TRUE, groups = gType, auto.key = TRUE)
nDat <- with(kDat, data.frame(sample, devStage, gType, crabHammer,probeset = factor(rep(c("eggBomb", "poisonFang"), each = nrow(kDat))),geneExp = c(eggBomb, poisonFang)))
str(nDat)
'data.frame': 78 obs. of 6 variables:
$ sample : int 20 21 22 23 16 17 6 24 25 26 ...
$ devStage : Factor w/ 5 levels "4_weeks","E16",..: 2 2 2 2 2 2 2 4 4 4 ...
$ gType : Factor w/ 2 levels "NrlKO","wt": 2 2 2 2 1 1 1 2 2 2 ...
$ crabHammer: num 10.22 10.02 9.64 9.65 8.58 ...
$ probeset : Factor w/ 2 levels "eggBomb","poisonFang": 1 1 1 1 1 1 1 1 1 1 ...
$ geneExp : num 7.46 6.89 6.72 6.53 6.47 ...
xyplot(geneExp ~ crabHammer | probeset, nDat,grid = TRUE,groups = devStage, auto.key = TRUE)
stripplot(~ geneExp, nDat)
oDat <-with(kDat,data.frame(sample, devStage, gType,probeset = factor(rep(c("crabHammer", "eggBomb","poisonFang"), each = nrow(kDat))),geneExp = c(crabHammer, eggBomb, poisonFang)))
stripplot(~ geneExp, oDat)
stripplot( ~ geneExp | probeset, oDat, layout = c(nlevels(oDat$probeset), 1))
stripplot(geneExp ~ devStage | probeset, oDat, layout = c(nlevels(oDat$probeset), 1), groups = gType, auto.key = TRUE, grid = TRUE,type = 'b')
jBw <- 0.2
jn <- 400
densityplot(~ geneExp, oDat,groups = gType, auto.key = TRUE,bw = jBw, n = jn,main = paste("bw =", jBw, ", n =", jn))
prDat <- read.table("../data/GSE4051_data.tsv")
str(prDat)
'data.frame': 29949 obs. of 39 variables:
$ Sample_20: num 7.24 9.48 10.01 8.36 8.59 ...
$ Sample_21: num 7.41 10.02 10.04 8.37 8.62 ...
$ Sample_22: num 7.17 9.85 9.91 8.4 8.52 ...
$ Sample_23: num 7.07 10.13 9.91 8.49 8.64 ...
$ Sample_16: num 7.38 7.64 8.42 8.36 8.51 ...
$ Sample_17: num 7.34 10.03 10.24 8.37 8.89 ...
$ Sample_6 : num 7.24 9.71 10.17 8.84 8.54 ...
$ Sample_24: num 7.11 9.75 9.39 8.37 8.36 ...
$ Sample_25: num 7.19 9.16 10.11 8.2 8.5 ...
$ Sample_26: num 7.18 9.49 9.41 8.73 8.39 ...
$ Sample_27: num 7.21 8.64 9.43 8.33 8.43 ...
$ Sample_14: num 7.09 9.56 9.88 8.57 8.59 ...
$ Sample_3 : num 7.16 9.55 9.84 8.33 8.5 ...
$ Sample_5 : num 7.08 9.32 9.24 8.3 8.48 ...
$ Sample_8 : num 7.11 8.24 9.13 8.13 8.33 ...
$ Sample_28: num 7.34 8.27 9.47 8.38 8.4 ...
$ Sample_29: num 7.66 10.03 9.88 8.56 8.69 ...
$ Sample_30: num 7.26 9.27 10.54 8.15 8.55 ...
$ Sample_31: num 7.31 9.26 10.1 8.37 8.49 ...
$ Sample_1 : num 7.15 9.87 9.68 8.28 8.5 ...
$ Sample_10: num 7.28 10.29 9.91 8.42 8.68 ...
$ Sample_4 : num 7.18 10.16 9.72 8.32 8.5 ...
$ Sample_7 : num 7.15 8.95 9.3 8.17 8.41 ...
$ Sample_32: num 7.54 9.53 9.92 8.78 8.57 ...
$ Sample_33: num 7.01 8.97 9.22 8.42 8.53 ...
$ Sample_34: num 6.81 8.83 9.39 8.1 8.32 ...
$ Sample_35: num 7.15 9.22 10.06 8.35 8.45 ...
$ Sample_13: num 7.33 9.33 9.75 8.43 8.48 ...
$ Sample_15: num 7.12 9.15 9.84 8.32 8.21 ...
$ Sample_18: num 7.21 9.49 10.03 8.55 8.5 ...
$ Sample_19: num 7.21 9.21 9.59 8.31 8.31 ...
$ Sample_36: num 7.25 9.66 9.51 8.49 8.42 ...
$ Sample_37: num 7.04 8.38 9.21 8.75 8.26 ...
$ Sample_38: num 7.37 9.44 9.48 8.49 8.34 ...
$ Sample_39: num 7.13 8.73 9.53 8.65 8.28 ...
$ Sample_11: num 7.42 9.83 10 8.6 8.43 ...
$ Sample_12: num 7.11 9.71 9.43 8.43 8.5 ...
$ Sample_2 : num 7.35 9.66 9.91 8.4 8.37 ...
$ Sample_9 : num 7.32 9.8 9.85 8.4 8.46 ...
set.seed(1)
(yo <- sample(1:nrow(prDat), size = 50))
[1] 7952 11145 17156 27198 6040 26902 28287 19786 18837 1850 6167
[12] 5286 20568 11499 23046 14899 21481 29690 11375 23269 27975 6350
[23] 19503 3758 7997 11555 401 11442 26023 10184 14424 17938 14766
[34] 5571 24751 19997 23759 3229 21647 12302 24554 19353 23416 16540
[45] 15842 23605 698 14271 21897 20713
hDat <- prDat[yo, ]
prDes <- readRDS("../data/GSE4051_design.rds")
hDat <- as.matrix(t(hDat))
rownames(hDat) <- with(prDes,paste(devStage, gType, sidChar, sep="_"))
str(hDat)
num [1:39, 1:50] 8.3 8.33 8.43 8.49 8.51 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:39] "E16_wt_Sample_20" "E16_wt_Sample_21" "E16_wt_Sample_22" "E16_wt_Sample_23" ...
..$ : chr [1:50] "1426822_at" "1431375_s_at" "1440076_at" "1456157_at" ...
heatmap(hDat, Rowv = NA, Colv = NA, scale="none", margins = c(5, 8))
library(RColorBrewer)
display.brewer.all()
set.seed(924)
(yo <- sample(1:ncol(prDat), size = 2))
[1] 25 24
y <- prDat[[yo[1]]]
z <- prDat[[yo[2]]]
str(y)
num [1:29949] 7.01 8.97 9.22 8.42 8.53 ...
xyplot(y ~ z, asp = 1)
smoothScatter(y ~ z, asp = 1)
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
install.packages("hexbin")
Error: trying to use CRAN without setting a mirror
library(hexbin)
Loading required package: grid
hexbinplot(y ~ z)
set.seed(3)
(yo <- sample(1:ncol(prDat), size = 4))
[1] 7 31 15 12
pairDat <- subset(prDat, select = yo)
pairs(pairDat, panel = function(...) smoothScatter(..., add=TRUE))
prDat <- read.table("../data/GSE4051_data.tsv")
str(prDat, max.level=0)
'data.frame': 29949 obs. of 39 variables:
set.seed(4)
(yo <- sample(1:ncol(prDat), size = 20))
[1] 23 1 11 10 29 9 24 35 30 3 22 34 31 25 37 26 39 13 21 16
hDat <- prDat[yo, ]
str(hDat)
'data.frame': 20 obs. of 39 variables:
$ Sample_20: num 10.88 7.24 8.17 9.21 9.85 ...
$ Sample_21: num 10.72 7.41 9.01 10.1 10.53 ...
$ Sample_22: num 10.57 7.17 8.28 9.82 9.63 ...
$ Sample_23: num 10.59 7.07 8.4 9.92 10.31 ...
$ Sample_16: num 9.12 7.38 7.51 8.22 8.9 ...
$ Sample_17: num 10.84 7.34 8.17 9.6 8.93 ...
$ Sample_6 : num 10.7 7.24 8.21 9.59 8.98 ...
$ Sample_24: num 9.65 7.11 8.29 9.3 9.52 ...
$ Sample_25: num 10.8 7.19 8.06 9.95 8.96 ...
$ Sample_26: num 9.8 7.18 8.17 8.77 8.48 ...
$ Sample_27: num 10.41 7.21 8.13 9.38 8.73 ...
$ Sample_14: num 10.54 7.09 8.39 10.12 8.69 ...
$ Sample_3 : num 10.42 7.16 8.27 10.07 8.53 ...
$ Sample_5 : num 10.15 7.08 8.31 9.94 8.64 ...
$ Sample_8 : num 10.22 7.11 7.68 9.22 8.49 ...
$ Sample_28: num 10 7.34 7.92 8.35 8.57 ...
$ Sample_29: num 10.55 7.66 8.29 9.03 8.17 ...
$ Sample_30: num 10.9 7.26 8.09 9.74 8.33 ...
$ Sample_31: num 10.25 7.31 8.1 8.9 8.41 ...
$ Sample_1 : num 10.71 7.15 7.84 9.82 8.04 ...
$ Sample_10: num 10.76 7.28 8.14 10.1 8.11 ...
$ Sample_4 : num 10.52 7.18 8.15 9.94 8.09 ...
$ Sample_7 : num 10.55 7.15 7.77 9.29 8.2 ...
$ Sample_32: num 10.64 7.54 8.59 8.81 7.7 ...
$ Sample_33: num 10.21 7.01 7.53 8.15 8.32 ...
$ Sample_34: num 10.11 6.81 7.7 8.52 8.06 ...
$ Sample_35: num 10.37 7.15 7.99 9.3 8.05 ...
$ Sample_13: num 10.9 7.33 8.17 8.44 8.08 ...
$ Sample_15: num 10.97 7.12 7.87 8.42 8.03 ...
$ Sample_18: num 10.94 7.21 8.58 9.09 8.04 ...
$ Sample_19: num 10.85 7.21 7.99 8.52 8.14 ...
$ Sample_36: num 10.28 7.25 8.72 9.13 7.89 ...
$ Sample_37: num 9.71 7.04 8.13 8.13 7.97 ...
$ Sample_38: num 10.22 7.37 8.27 8.4 7.55 ...
$ Sample_39: num 9.92 7.13 8.33 8.07 7.69 ...
$ Sample_11: num 10.86 7.42 8.02 9.06 8 ...
$ Sample_12: num 10.83 7.11 7.82 8.82 8.15 ...
$ Sample_2 : num 11.11 7.35 7.7 9.02 7.7 ...
$ Sample_9 : num 10.76 7.32 8.77 9.29 9.86 ...