Supplementary_Data_30
Denis
2023-07-10
RT-qPCR analysis - SRY - outlier identification by the IQR method in R
rm(list=ls())library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors1 SRY
1.1 SRY_iPS07
1.1.1 SRY_iPS07-M1_36h00-Mut
Input = ("
names values block
Mut 16.31 iPS07-82-M1-1
Mut 16.43 iPS07-82-M1-2
Mut 16.09 iPS07-82-M1-3
Mut 15.44 iPS07-82-M1-4
Mut 15.63 iPS07-82-M1-5"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data1 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data1)
Data1b<- Data1|> select(block, names, values, values_wo_outliers)
Data1b## block names values values_wo_outliers
## 1 iPS07-82-M1-1 Mut 16.31 16.31
## 2 iPS07-82-M1-2 Mut 16.43 16.43
## 3 iPS07-82-M1-3 Mut 16.09 16.09
## 4 iPS07-82-M1-4 Mut 15.44 15.44
## 5 iPS07-82-M1-5 Mut 15.63 15.631.1.2 SRY_iPS07-M2_48h00-Wt
Input = ("
names values block
WT 13.38 iPS07-45-M2_48
WT 14.10 iPS07-45-M2_48
WT 13.37 iPS07-45-M2_48
WT 13.60 iPS07-45-M2_48
WT 13.82 iPS07-45-M2_48
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data2 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data2)
Data2b<- Data2|> select(block, names, values, values_wo_outliers)
Data2b## block names values values_wo_outliers
## 1 iPS07-45-M2_48 WT 13.38 13.38
## 2 iPS07-45-M2_48 WT 14.10 14.10
## 3 iPS07-45-M2_48 WT 13.37 13.37
## 4 iPS07-45-M2_48 WT 13.60 13.60
## 5 iPS07-45-M2_48 WT 13.82 13.821.1.3 SRY_iPS07-M2_48h00-Mut
Input = ("
names values block
Mut 13.48 iPS07-82-M2_48
Mut 13.53 iPS07-82-M2_48
Mut 13.46 iPS07-82-M2_48
Mut 13.82 iPS07-82-M2_48
Mut 13.53 iPS07-82-M2_48
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data3 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data3)
Data3b<- Data3|> select(block, names, values, values_wo_outliers)
Data3b## block names values values_wo_outliers
## 1 iPS07-82-M2_48 Mut 13.48 13.48
## 2 iPS07-82-M2_48 Mut 13.53 13.53
## 3 iPS07-82-M2_48 Mut 13.46 13.46
## 4 iPS07-82-M2_48 Mut 13.82 NA
## 5 iPS07-82-M2_48 Mut 13.53 13.531.2 SRY_iPS09
1.2.1 SRY_iPS09-M1_36h00-Wt
Input = ("
names values block
WT 11.57 iPS09-45-M1_36h00
WT 11.98 iPS09-45-M1_36h00
WT 12.69 iPS09-45-M1_36h00
WT 12.18 iPS09-45-M1_36h00
WT 11.37 iPS09-45-M1_36h00
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data4 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data4)
Data4b<- Data4|> select(block, names, values, values_wo_outliers)
Data4b## block names values values_wo_outliers
## 1 iPS09-45-M1_36h00 WT 11.57 11.57
## 2 iPS09-45-M1_36h00 WT 11.98 11.98
## 3 iPS09-45-M1_36h00 WT 12.69 12.69
## 4 iPS09-45-M1_36h00 WT 12.18 12.18
## 5 iPS09-45-M1_36h00 WT 11.37 11.371.2.2 SRY_iPS09-M1_36h00-Mut
Input = ("
names values block
Mut 11.65 iPS09-82-M1_36h00
Mut 12.56 iPS09-82-M1_36h00
Mut 12.90 iPS09-82-M1_36h00
Mut 12.60 iPS09-82-M1_36h00
Mut 12.56 iPS09-82-M1_36h00
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data5 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data5)
Data5b<- Data5|> select(block, names, values, values_wo_outliers)
Data5b## block names values values_wo_outliers
## 1 iPS09-82-M1_36h00 Mut 11.65 NA
## 2 iPS09-82-M1_36h00 Mut 12.56 12.56
## 3 iPS09-82-M1_36h00 Mut 12.90 NA
## 4 iPS09-82-M1_36h00 Mut 12.60 12.60
## 5 iPS09-82-M1_36h00 Mut 12.56 12.561.2.3 SRY_iPS09-M2_24h00-Wt
Input = ("
names values block
WT 9.22 iPS09-45-M2_24h00
WT 8.91 iPS09-45-M2_24h00
WT 9.16 iPS09-45-M2_24h00
WT 9.07 iPS09-45-M2_24h00
WT 8.97 iPS09-45-M2_24h00
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data6 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data6)
Data6b<- Data6|> select(block, names, values, values_wo_outliers)
Data6b## block names values values_wo_outliers
## 1 iPS09-45-M2_24h00 WT 9.22 9.22
## 2 iPS09-45-M2_24h00 WT 8.91 8.91
## 3 iPS09-45-M2_24h00 WT 9.16 9.16
## 4 iPS09-45-M2_24h00 WT 9.07 9.07
## 5 iPS09-45-M2_24h00 WT 8.97 8.971.2.4 SRY_iPS09-M2_24h00-Mut
Input = ("
names values block
Mut 8.82 iPS09-82-M2_24h00
Mut 9.13 iPS09-82-M2_24h00
Mut 8.85 iPS09-82-M2_24h00
Mut 8.85 iPS09-82-M2_24h00
Mut 8.76 iPS09-82-M2_24h00
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data7 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data7)
Data7b<- Data7|> select(block, names, values, values_wo_outliers)
Data7b## block names values values_wo_outliers
## 1 iPS09-82-M2_24h00 Mut 8.82 8.82
## 2 iPS09-82-M2_24h00 Mut 9.13 NA
## 3 iPS09-82-M2_24h00 Mut 8.85 8.85
## 4 iPS09-82-M2_24h00 Mut 8.85 8.85
## 5 iPS09-82-M2_24h00 Mut 8.76 NA1.2.5 SRY_iPS09-M3_24h00-Wt
Input = ("
names values block
WT 12.28 iPS09-45-M3_24h00
WT 12.69 iPS09-45-M3_24h00
WT 12.39 iPS09-45-M3_24h00
WT 12.55 iPS09-45-M3_24h00
WT 12.40 iPS09-45-M3_24h00
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data8 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data8)
Data8b<- Data8|> select(block, names, values, values_wo_outliers)
Data8b## block names values values_wo_outliers
## 1 iPS09-45-M3_24h00 WT 12.28 12.28
## 2 iPS09-45-M3_24h00 WT 12.69 12.69
## 3 iPS09-45-M3_24h00 WT 12.39 12.39
## 4 iPS09-45-M3_24h00 WT 12.55 12.55
## 5 iPS09-45-M3_24h00 WT 12.40 12.401.2.6 SRY_iPS09-M3_48h00-Wt
Input = ("
names values block
WT 13.51 iPS09-45-M3_48h00
WT 13.19 iPS09-45-M3_48h00
WT 14.25 iPS09-45-M3_48h00
WT 14.45 iPS09-45-M3_48h00
WT 13.71 iPS09-45-M3_48h00
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data9 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data9)
Data9b<- Data9|> select(block, names, values, values_wo_outliers)
Data9b## block names values values_wo_outliers
## 1 iPS09-45-M3_48h00 WT 13.51 13.51
## 2 iPS09-45-M3_48h00 WT 13.19 13.19
## 3 iPS09-45-M3_48h00 WT 14.25 14.25
## 4 iPS09-45-M3_48h00 WT 14.45 14.45
## 5 iPS09-45-M3_48h00 WT 13.71 13.711.2.7 SRY_iPS09-M3_48h00-Mut
Input = ("
names values block
Mut 14.14 iPS09-82-M3_48h00
Mut 13.56 iPS09-82-M3_48h00
Mut 13.31 iPS09-82-M3_48h00
Mut 14.14 iPS09-82-M3_48h00
Mut 13.74 iPS09-82-M3_48h00
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data10 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data10)
Data10b<- Data10|> select(block, names, values, values_wo_outliers)
Data10b## block names values values_wo_outliers
## 1 iPS09-82-M3_48h00 Mut 14.14 14.14
## 2 iPS09-82-M3_48h00 Mut 13.56 13.56
## 3 iPS09-82-M3_48h00 Mut 13.31 13.31
## 4 iPS09-82-M3_48h00 Mut 14.14 14.14
## 5 iPS09-82-M3_48h00 Mut 13.74 13.741.2.8 SRY_iPS09-iPS-Wt
Input = ("
names values block
WT 11.20 iPS07-45-iPS
WT 12.53 iPS07-45-iPS
WT 13.02 iPS07-45-iPS
WT 12.29 iPS07-45-iPS
WT 11.50 iPS07-45-iPS
WT 12.32 iPS07-45-iPS
WT 11.81 iPS07-45-iPS
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data11 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data11)
Data11b<- Data11|> select(block, names, values, values_wo_outliers)
Data11b## block names values values_wo_outliers
## 1 iPS07-45-iPS WT 11.20 11.20
## 2 iPS07-45-iPS WT 12.53 12.53
## 3 iPS07-45-iPS WT 13.02 13.02
## 4 iPS07-45-iPS WT 12.29 12.29
## 5 iPS07-45-iPS WT 11.50 11.50
## 6 iPS07-45-iPS WT 12.32 12.32
## 7 iPS07-45-iPS WT 11.81 11.811.2.9 SRY_iPS09-iPS-Mut
Input = ("
names values block
Mut 8.65 iPS09-82-iPS
Mut 9.23 iPS09-82-iPS
Mut 8.22 iPS09-82-iPS
Mut 8.16 iPS09-82-iPS
Mut 9.15 iPS09-82-iPS
Mut 9.13 iPS09-82-iPS
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data12 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data12)
Data12b<- Data12|> select(block, names, values, values_wo_outliers)
Data12b## block names values values_wo_outliers
## 1 iPS09-82-iPS Mut 8.65 8.65
## 2 iPS09-82-iPS Mut 9.23 9.23
## 3 iPS09-82-iPS Mut 8.22 8.22
## 4 iPS09-82-iPS Mut 8.16 8.16
## 5 iPS09-82-iPS Mut 9.15 9.15
## 6 iPS09-82-iPS Mut 9.13 9.131.3 SRY_iPS12
1.3.1 SRY_iPS12-M1_36h00-Wt
Input = ("
names values block
WT 11.93 iPS12_45_M1_36_P
WT 12.47 iPS12_45_M1_36_P
WT 11.89 iPS12_45_M1_36_P
WT 12.46 iPS12_45_M1_36_P
WT 12.27 iPS12_45_M1_36_P
WT 12.06 iPS12_45_M1_36_P
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data13 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data13)
Data13b<- Data13|> select(block, names, values, values_wo_outliers)
Data13b## block names values values_wo_outliers
## 1 iPS12_45_M1_36_P WT 11.93 11.93
## 2 iPS12_45_M1_36_P WT 12.47 12.47
## 3 iPS12_45_M1_36_P WT 11.89 11.89
## 4 iPS12_45_M1_36_P WT 12.46 12.46
## 5 iPS12_45_M1_36_P WT 12.27 12.27
## 6 iPS12_45_M1_36_P WT 12.06 12.061.3.2 SRY_iPS12-M1_36h00-Mut
Input = ("
names values block
Mut 13.10 iPS12_82_M1_36_P
Mut 12.99 iPS12_82_M1_36_P
Mut 13.43 iPS12_82_M1_36_P
Mut 13.29 iPS12_82_M1_36_P
Mut 13.25 iPS12_82_M1_36_P
Mut 13.26 iPS12_82_M1_36_P
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data14 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data14)
Data14b<- Data14|> select(block, names, values, values_wo_outliers)
Data14b## block names values values_wo_outliers
## 1 iPS12_82_M1_36_P Mut 13.10 13.10
## 2 iPS12_82_M1_36_P Mut 12.99 12.99
## 3 iPS12_82_M1_36_P Mut 13.43 13.43
## 4 iPS12_82_M1_36_P Mut 13.29 13.29
## 5 iPS12_82_M1_36_P Mut 13.25 13.25
## 6 iPS12_82_M1_36_P Mut 13.26 13.261.3.3 SRY_iPS12-M2_06h00-Wt
Input = ("
names values block
WT 8.01 iPS12_45_M2_06_P
WT 8.29 iPS12_45_M2_06_P
WT 8.23 iPS12_45_M2_06_P
WT 8.47 iPS12_45_M2_06_P
WT 7.89 iPS12_45_M2_06_P
WT NA iPS12_45_M2_06_P
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data15 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data15)
Data15b<- Data15|> select(block, names, values, values_wo_outliers)
Data15b## block names values values_wo_outliers
## 1 iPS12_45_M2_06_P WT 8.01 8.01
## 2 iPS12_45_M2_06_P WT 8.29 8.29
## 3 iPS12_45_M2_06_P WT 8.23 8.23
## 4 iPS12_45_M2_06_P WT 8.47 8.47
## 5 iPS12_45_M2_06_P WT 7.89 7.89
## 6 iPS12_45_M2_06_P WT NA NA1.3.4 SRY_iPS12-M2_06h00-Mut
Input = ("
names values block
Mut 5.46 iPS12_82_M2_06_P
Mut 9.20 iPS12_82_M2_06_P
Mut 9.17 iPS12_82_M2_06_P
Mut 8.84 iPS12_82_M2_06_P
Mut 9.09 iPS12_82_M2_06_P
Mut 8.58 iPS12_82_M2_06_P
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data16 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data16)
Data16b<- Data16|> select(block, names, values, values_wo_outliers)
Data16b## block names values values_wo_outliers
## 1 iPS12_82_M2_06_P Mut 5.46 NA
## 2 iPS12_82_M2_06_P Mut 9.20 9.20
## 3 iPS12_82_M2_06_P Mut 9.17 9.17
## 4 iPS12_82_M2_06_P Mut 8.84 8.84
## 5 iPS12_82_M2_06_P Mut 9.09 9.09
## 6 iPS12_82_M2_06_P Mut 8.58 8.581.3.5 SRY_iPS12-M2_12h00-Wt_vs_Mut
Input = ("
names values block
WT 8.27 iPS12_45_M2_12_P
WT 10.34 iPS12_45_M2_12_P
WT 10.60 iPS12_45_M2_12_P
WT 10.36 iPS12_45_M2_12_P
WT 10.35 iPS12_45_M2_12_P
WT 10.13 iPS12_45_M2_12_P
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data17 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data17)
Data17b<- Data17|> select(block, names, values, values_wo_outliers)
Data17b## block names values values_wo_outliers
## 1 iPS12_45_M2_12_P WT 8.27 NA
## 2 iPS12_45_M2_12_P WT 10.34 10.34
## 3 iPS12_45_M2_12_P WT 10.60 10.60
## 4 iPS12_45_M2_12_P WT 10.36 10.36
## 5 iPS12_45_M2_12_P WT 10.35 10.35
## 6 iPS12_45_M2_12_P WT 10.13 10.131.3.6 SRY_iPS12-M2_12h00-Mut
Input = ("
names values block
Mut 9.91 iPS12_82_M2_12_P
Mut 10.25 iPS12_82_M2_12_P
Mut 9.67 iPS12_82_M2_12_P
Mut 9.81 iPS12_82_M2_12_P
Mut 10.32 iPS12_82_M2_12_P
Mut 9.89 iPS12_82_M2_12_P
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data18 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data18)
Data18b<- Data18|> select(block, names, values, values_wo_outliers)
Data18b## block names values values_wo_outliers
## 1 iPS12_82_M2_12_P Mut 9.91 9.91
## 2 iPS12_82_M2_12_P Mut 10.25 10.25
## 3 iPS12_82_M2_12_P Mut 9.67 9.67
## 4 iPS12_82_M2_12_P Mut 9.81 9.81
## 5 iPS12_82_M2_12_P Mut 10.32 10.32
## 6 iPS12_82_M2_12_P Mut 9.89 9.891.3.7 SRY_iPS12_M2_24h00-Wt
Input = ("
names values block
WT 9.50 iPS12_45_M2_24_P
WT 9.76 iPS12_45_M2_24_P
WT 9.50 iPS12_45_M2_24_P
WT 9.34 iPS12_45_M2_24_P
WT 9.57 iPS12_45_M2_24_P
WT 9.09 iPS12_45_M2_24_P
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data19 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data19)
Data19b<- Data19|> select(block, names, values, values_wo_outliers)
Data19b## block names values values_wo_outliers
## 1 iPS12_45_M2_24_P WT 9.50 9.50
## 2 iPS12_45_M2_24_P WT 9.76 9.76
## 3 iPS12_45_M2_24_P WT 9.50 9.50
## 4 iPS12_45_M2_24_P WT 9.34 9.34
## 5 iPS12_45_M2_24_P WT 9.57 9.57
## 6 iPS12_45_M2_24_P WT 9.09 NA1.3.8 SRY_iPS12_M2_24h00-Mut
Input = ("
names values block
Mut 9.08 iPS12_82_M2_24_P
Mut 9.13 iPS12_82_M2_24_P
Mut 9.31 iPS12_82_M2_24_P
Mut 8.85 iPS12_82_M2_24_P
Mut 9.12 iPS12_82_M2_24_P
Mut 8.83 iPS12_82_M2_24_P
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data20 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data20)
Data20b<- Data20|> select(block, names, values, values_wo_outliers)
Data20b## block names values values_wo_outliers
## 1 iPS12_82_M2_24_P Mut 9.08 9.08
## 2 iPS12_82_M2_24_P Mut 9.13 9.13
## 3 iPS12_82_M2_24_P Mut 9.31 9.31
## 4 iPS12_82_M2_24_P Mut 8.85 8.85
## 5 iPS12_82_M2_24_P Mut 9.12 9.12
## 6 iPS12_82_M2_24_P Mut 8.83 8.831.3.9 SRY_iPS12-M2_48h00-Wt
Input = ("
names values block
WT 10.60 iPS12_45_M2_48_P
WT 10.87 iPS12_45_M2_48_P
WT 10.64 iPS12_45_M2_48_P
WT 10.91 iPS12_45_M2_48_P
WT 10.96 iPS12_45_M2_48_P
WT NA iPS12_45_M2_48_P
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data21 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data21)
Data21b<- Data21|> select(block, names, values, values_wo_outliers)
Data21b## block names values values_wo_outliers
## 1 iPS12_45_M2_48_P WT 10.60 10.60
## 2 iPS12_45_M2_48_P WT 10.87 10.87
## 3 iPS12_45_M2_48_P WT 10.64 10.64
## 4 iPS12_45_M2_48_P WT 10.91 10.91
## 5 iPS12_45_M2_48_P WT 10.96 10.96
## 6 iPS12_45_M2_48_P WT NA NA1.3.10 SRY_iPS12-M2_48h00-Mut
Input = ("
names values block
Mut 10.73 iPS12_82_M2_48_P
Mut 11.23 iPS12_82_M2_48_P
Mut 11.14 iPS12_82_M2_48_P
Mut 10.80 iPS12_82_M2_48_P
Mut 10.86 iPS12_82_M2_48_P
Mut 10.85 iPS12_82_M2_48_P
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data22 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data22)
Data22b<- Data22|> select(block, names, values, values_wo_outliers)
Data22b## block names values values_wo_outliers
## 1 iPS12_82_M2_48_P Mut 10.73 10.73
## 2 iPS12_82_M2_48_P Mut 11.23 11.23
## 3 iPS12_82_M2_48_P Mut 11.14 11.14
## 4 iPS12_82_M2_48_P Mut 10.80 10.80
## 5 iPS12_82_M2_48_P Mut 10.86 10.86
## 6 iPS12_82_M2_48_P Mut 10.85 10.851.4 SRY_iPS19
1.4.1 SRY_iPS19-iPS-Wt
Input = ("
names values block
WT 10.02 iPS19_45_iPS
WT 9.89 iPS19_45_iPS
WT 10.11 iPS19_45_iPS
WT 10.11 iPS19_45_iPS
WT 9.82 iPS19_45_iPS
WT 9.92 iPS19_45_iPS
WT 8.79 iPS19_82_iPS
WT 9.22 iPS19_82_iPS
WT 9.28 iPS19_82_iPS
WT 9.50 iPS19_82_iPS
WT 9.52 iPS19_82_iPS
WT 9.07 iPS19_82_iPS
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data23 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data23)
Data23b<- Data23|> select(block, names, values, values_wo_outliers)
Data23b## block names values values_wo_outliers
## 1 iPS19_45_iPS WT 10.02 10.02
## 2 iPS19_45_iPS WT 9.89 9.89
## 3 iPS19_45_iPS WT 10.11 10.11
## 4 iPS19_45_iPS WT 10.11 10.11
## 5 iPS19_45_iPS WT 9.82 9.82
## 6 iPS19_45_iPS WT 9.92 9.92
## 7 iPS19_82_iPS WT 8.79 8.79
## 8 iPS19_82_iPS WT 9.22 9.22
## 9 iPS19_82_iPS WT 9.28 9.28
## 10 iPS19_82_iPS WT 9.50 9.50
## 11 iPS19_82_iPS WT 9.52 9.52
## 12 iPS19_82_iPS WT 9.07 9.071.4.2 SRY_iPS19-iPS-Mut
Input = ("
names values block
Mut 9.83 iPS19_82_iPS
Mut 10.20 iPS19_82_iPS
Mut 10.18 iPS19_82_iPS
Mut 9.93 iPS19_82_iPS
Mut 10.20 iPS19_82_iPS
Mut 10.05 iPS19_82_iPS
Mut 9.28 iPS19_82_iPS
Mut 9.57 iPS19_82_iPS
Mut 9.75 iPS19_82_iPS
Mut 9.29 iPS19_82_iPS
Mut 9.46 iPS19_82_iPS
Mut 9.57 iPS19_82_iPS
"
)
Data = read.table(textConnection(Input),header=TRUE)
Data$names = factor(Data$names,ordered=FALSE, levels=unique(Data$names))
Data$block = factor(Data$block,ordered=FALSE, levels=unique(Data$block))
# SRY_boxplot 1
boxplot(values ~ names,
data = Data,
ylab ="values",
xlab ="names")
Data24 <- Data |>
mutate(
IQR = IQR(values, na.rm = TRUE),
Outlier_upper = quantile(values, probs = c(.75), na.rm = TRUE) + 1.5 * IQR,
Outlier_lower = quantile(values, probs = c(.25), na.rm = TRUE) - 1.5 * IQR,
values_wo_outliers = if_else(values <= Outlier_lower | values >= Outlier_upper, NA, values))
boxplot(values_wo_outliers ~ names, Data24)
Data24b<- Data24|> select(block, names, values, values_wo_outliers)
Data24b## block names values values_wo_outliers
## 1 iPS19_82_iPS Mut 9.83 9.83
## 2 iPS19_82_iPS Mut 10.20 10.20
## 3 iPS19_82_iPS Mut 10.18 10.18
## 4 iPS19_82_iPS Mut 9.93 9.93
## 5 iPS19_82_iPS Mut 10.20 10.20
## 6 iPS19_82_iPS Mut 10.05 10.05
## 7 iPS19_82_iPS Mut 9.28 9.28
## 8 iPS19_82_iPS Mut 9.57 9.57
## 9 iPS19_82_iPS Mut 9.75 9.75
## 10 iPS19_82_iPS Mut 9.29 9.29
## 11 iPS19_82_iPS Mut 9.46 9.46
## 12 iPS19_82_iPS Mut 9.57 9.57