Ali
Thursday, January 22, 2015
Playing with GSE4051_MINI.txt file
read.delim("GSE4051_MINI.txt")## sidChar sidNum devStage gType crabHammer eggBomb poisonFang
## 1 Sample_20 20 E16 wt 10.220 7.462 7.370
## 2 Sample_21 21 E16 wt 10.020 6.890 7.177
## 3 Sample_22 22 E16 wt 9.642 6.720 7.350
## 4 Sample_23 23 E16 wt 9.652 6.529 7.040
## 5 Sample_16 16 E16 NrlKO 8.583 6.470 7.494
## 6 Sample_17 17 E16 NrlKO 10.140 7.065 7.005
## 7 Sample_6 6 E16 NrlKO 10.340 7.017 6.735
## 8 Sample_24 24 P2 wt 8.869 6.587 7.508
## 9 Sample_25 25 P2 wt 9.038 6.170 7.449
## 10 Sample_26 26 P2 wt 9.611 6.870 7.511
## 11 Sample_27 27 P2 wt 8.613 6.800 7.843
## 12 Sample_14 14 P2 NrlKO 9.572 6.138 7.250
## 13 Sample_3 3 P2 NrlKO 9.414 6.166 7.200
## 14 Sample_5 5 P2 NrlKO 8.925 6.269 7.405
## 15 Sample_8 8 P2 NrlKO 9.116 6.264 8.016
## 16 Sample_28 28 P6 wt 8.214 6.530 7.428
## 17 Sample_29 29 P6 wt 10.130 7.574 7.100
## 18 Sample_30 30 P6 wt 8.951 6.269 7.274
## 19 Sample_31 31 P6 wt 8.693 6.211 7.409
## 20 Sample_1 1 P6 NrlKO 8.920 6.286 7.378
## 21 Sample_10 10 P6 NrlKO 9.544 6.347 7.252
## 22 Sample_4 4 P6 NrlKO 8.992 6.270 7.342
## 23 Sample_7 7 P6 NrlKO 8.803 6.188 7.754
## 24 Sample_32 32 P10 wt 10.250 8.173 7.005
## 25 Sample_33 33 P10 wt 9.004 7.082 8.086
## 26 Sample_34 34 P10 wt 8.519 6.757 8.584
## 27 Sample_35 35 P10 wt 8.449 6.155 7.201
## 28 Sample_13 13 P10 NrlKO 9.838 7.228 7.459
## 29 Sample_15 15 P10 NrlKO 9.746 7.226 7.786
## 30 Sample_18 18 P10 NrlKO 10.140 7.438 7.363
## 31 Sample_19 19 P10 NrlKO 9.771 7.081 7.586
## 32 Sample_36 36 4_weeks wt 9.960 7.866 6.993
## 33 Sample_37 37 4_weeks wt 9.667 6.992 7.324
## 34 Sample_38 38 4_weeks wt 9.767 6.608 7.329
## 35 Sample_39 39 4_weeks wt 10.200 7.003 7.320
## 36 Sample_11 11 4_weeks NrlKO 9.677 7.204 6.981
## 37 Sample_12 12 4_weeks NrlKO 9.129 7.165 7.350
## 38 Sample_2 2 4_weeks NrlKO 9.744 7.107 7.075
## 39 Sample_9 9 4_weeks NrlKO 9.822 6.558 7.043
prDat <- read.table("GSE4051_MINI.txt", header = TRUE, row.names = 1)
str(prDat)## 'data.frame': 39 obs. of 6 variables:
## $ sidNum : 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 ...
nrow(prDat) ## [1] 39
ncol(prDat) ## [1] 6
head(prDat)## sidNum devStage gType crabHammer eggBomb poisonFang
## Sample_20 20 E16 wt 10.220 7.462 7.370
## Sample_21 21 E16 wt 10.020 6.890 7.177
## Sample_22 22 E16 wt 9.642 6.720 7.350
## Sample_23 23 E16 wt 9.652 6.529 7.040
## Sample_16 16 E16 NrlKO 8.583 6.470 7.494
## Sample_17 17 E16 NrlKO 10.140 7.065 7.005
tail(prDat) ## sidNum devStage gType crabHammer eggBomb poisonFang
## Sample_38 38 4_weeks wt 9.767 6.608 7.329
## Sample_39 39 4_weeks wt 10.200 7.003 7.320
## Sample_11 11 4_weeks NrlKO 9.677 7.204 6.981
## Sample_12 12 4_weeks NrlKO 9.129 7.165 7.350
## Sample_2 2 4_weeks NrlKO 9.744 7.107 7.075
## Sample_9 9 4_weeks NrlKO 9.822 6.558 7.043
names(prDat)## [1] "sidNum" "devStage" "gType" "crabHammer" "eggBomb"
## [6] "poisonFang"
str(prDat)## 'data.frame': 39 obs. of 6 variables:
## $ sidNum : 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 ...
levels(prDat$devStage)## [1] "4_weeks" "E16" "P10" "P2" "P6"
summary(prDat$devStage)## 4_weeks E16 P10 P2 P6
## 8 7 8 8 8
summary(prDat$gType)## NrlKO wt
## 19 20
table(prDat$devStage, prDat$gType)##
## NrlKO wt
## 4_weeks 4 4
## E16 3 4
## P10 4 4
## P2 4 4
## P6 4 4
Intended experimental design? Maybe to see how developmetn/life expectancy of the KO mouse differs from wt. Looks like it's the same, though
summary(prDat$crabHammer) #...etc## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 8.214 8.938 9.611 9.428 9.830 10.340
weeDat <- subset(prDat, subset = poisonFang > 7.5)
summary(weeDat)## sidNum devStage gType crabHammer eggBomb
## Min. : 7.00 4_weeks:0 NrlKO:4 Min. :8.519 Min. :6.188
## 1st Qu.:15.00 E16 :0 wt :5 1st Qu.:8.803 1st Qu.:6.587
## Median :24.00 P10 :4 Median :9.004 Median :6.800
## Mean :21.44 P2 :4 Mean :9.117 Mean :6.762
## 3rd Qu.:27.00 P6 :1 3rd Qu.:9.611 3rd Qu.:7.081
## Max. :34.00 Max. :9.771 Max. :7.226
## poisonFang
## Min. :7.508
## 1st Qu.:7.586
## Median :7.786
## Mean :7.853
## 3rd Qu.:8.016
## Max. :8.584
str(weeDat$poisonFang)## num [1:9] 7.51 7.51 7.84 8.02 7.75 ...
prDat[c("Sample_16", "Sample_38"), "eggBomb"]## [1] 6.470 6.608
quantile(prDat$eggBomb, .1)## 10%
## 6.1844
qDat <- subset(prDat, subset = eggBomb < 6.1844)
rownames(qDat)## [1] "Sample_25" "Sample_14" "Sample_3" "Sample_35"