This only looks at animals treated in utero. These data were most recently updated on Sun Nov 16 13:21:31 2014.
output_file_maternal <- "../data/Raw Weights - Maternal.csv"
data.maternal <- read.csv(output_file_maternal, row.names="X")
#removed sickly, weight losing mouse 206
data.maternal <- subset(data.maternal, animal.MouseID !=206)
data.maternal$animal.MouseID <- as.factor(data.maternal$animal.MouseID)
#find which experiments we did fat masss
fat.mass.experiments <- droplevels(subset(data.maternal, assay.assay=="Total Fat Mass"))$experiment.date
data <- subset(data.maternal, experiment.date %in% fat.mass.experiments)
data$animal.Cage <- as.factor(data$animal.Cage)
data$age.group <- cut(data$age, breaks=2)
data$Treatment <- relevel(data$Treatment, ref='Saline')
library(reshape2)
composition.data <- dcast(data,
animal.MouseID+Treatment+animal.Cage+age~assay.assay, value.var="values",
fun.aggregate = function(x) mean(x)/1000)
#remove whitespace
colnames(composition.data) <- gsub(" ", ".", colnames(composition.data))
composition.data$Fat.Pct <- composition.data$Total.Fat.Mass/composition.data$Body.Weight*100
composition.data$Lean.Pct <- composition.data$Lean.Mass/composition.data$Body.Weight*100
library(dplyr)
##
## Attaching package: 'dplyr'
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
se <- function(x) sd(x, na.rm=T)/sqrt(length(x))
fat.mass.summary <- subset(composition.data, age>145) %>%
group_by(Treatment) %>%
distinct(animal.MouseID) %>%
summarise(mean = mean(Total.Fat.Mass),
se = se(Total.Fat.Mass ),
sd = sd(Total.Fat.Mass ),
rel.sd = sd(Total.Fat.Mass)/mean(Total.Fat.Mass)*100,
n = length(Total.Fat.Mass),
shapiro = shapiro.test(Total.Fat.Mass)$p.value)
fat.pct.summary <- subset(composition.data, age>145) %>%
group_by(Treatment) %>%
distinct(animal.MouseID) %>%
summarise(mean = mean(Fat.Pct),
se = se(Fat.Pct),
sd = sd(Fat.Pct),
rel.sd = sd(Fat.Pct)/mean(Fat.Pct)*100,
n = length(Fat.Pct),
shapiro = shapiro.test(Fat.Pct)$p.value)
lean.mass.summary <- subset(composition.data, age>145) %>%
group_by(Treatment) %>%
distinct(animal.MouseID) %>%
summarise(mean = mean(Lean.Mass),
se = se(Lean.Mass),
sd = sd(Lean.Mass),
rel.sd = sd(Lean.Mass)/mean(Lean.Mass)*100,
n = length(Lean.Mass),
shapiro = shapiro.test(Lean.Mass)$p.value)
lean.pct.summary <- subset(composition.data, age>145) %>%
group_by(Treatment) %>%
distinct(animal.MouseID) %>%
summarise(mean = mean(Lean.Pct),
se = se(Lean.Pct),
sd = sd(Lean.Pct),
rel.sd = sd(Lean.Pct)/mean(Lean.Pct)*100,
n = length(Lean.Pct),
shapiro = shapiro.test(Lean.Pct)$p.value)
library(car)
library(xtable)
print(xtable(as.data.frame(fat.pct.summary), label="tab:summary-fat-pct", caption="Data for Percent Fat", digits=3), type='html')
Treatment | mean | se | sd | rel.sd | n | shapiro | |
---|---|---|---|---|---|---|---|
1 | Saline | 40.977 | 0.332 | 1.241 | 3.029 | 14 | 0.615 |
2 | MCP230 | 40.948 | 0.676 | 2.137 | 5.218 | 10 | 0.787 |
print(xtable(as.data.frame(fat.mass.summary), label="tab:summary-fat-mass", caption="Data for Total Fat", digits=3), type='html')
Treatment | mean | se | sd | rel.sd | n | shapiro | |
---|---|---|---|---|---|---|---|
1 | Saline | 17.955 | 0.396 | 1.480 | 8.245 | 14 | 0.854 |
2 | MCP230 | 19.860 | 0.654 | 2.068 | 10.412 | 10 | 0.611 |
print(xtable(as.data.frame(lean.mass.summary), label="tab:summary-lean-mass", caption="Data for Total Lean Mass", digits=3), type='html')
Treatment | mean | se | sd | rel.sd | n | shapiro | |
---|---|---|---|---|---|---|---|
1 | Saline | 24.435 | 0.433 | 1.621 | 6.636 | 14 | 0.376 |
2 | MCP230 | 27.065 | 0.358 | 1.131 | 4.179 | 10 | 0.363 |
ymax = max(fat.mass.summary$mean + fat.mass.summary$se)
plot <- with(fat.mass.summary, barplot(mean,
las=1,
ylab ="Total Fat Mass (g)",
names.arg=Treatment,
ylim = c(0,ymax)))
superpose.eb <- function (x, y, ebl, ebu = ebl, length = 0.08, ...)
arrows(x, y + ebu, x, y - ebl, angle = 90, code = 3,
length = length, ...)
superpose.eb(plot, fat.mass.summary$mean, fat.mass.summary$se)
unique.composition.data <- distinct(subset(composition.data, age>145), animal.MouseID)
The data were normally distributed (p>0.6111494) and had equal variance via a Levene’s test (p=0.1816593). Therefore via a Student’s t-test, the p-value was 0.0150579. There was a 10.609858% increase in fat mass.
ymax = max(lean.mass.summary$mean + lean.mass.summary$se)
plot <- with(lean.mass.summary, barplot(mean,
las=1,
ylab ="Total Lean Mass (g)",
names.arg=Treatment,
ylim = c(0,ymax)))
superpose.eb(plot, lean.mass.summary$mean, lean.mass.summary$se)
The data were normally distributed (p>0.3627422) and had equal variance via a Levene’s test (p=0.3664842). Therefore via a Student’s t-test, the p-value was 2.227829810^{-4}. There was a 10.7632494% increase in lean mass.
ymax = max(fat.pct.summary$mean + fat.pct.summary$se)
plot <- with(fat.pct.summary, barplot(mean,
las=1,
ylab ="Percent Body Fat",
names.arg=Treatment,
ylim = c(0,ymax)))
superpose.eb(plot, fat.pct.summary$mean, fat.pct.summary$se)
The data were normally distributed (p>0.6152608) and had unequal variance via a Levene’s test (p=0.0241466). Therefore via a Welch’s t-test, the p-value was 0.9699659.
sessionInfo()
## R version 3.1.1 (2014-07-10)
## Platform: x86_64-apple-darwin13.1.0 (64-bit)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] xtable_1.7-4 car_2.0-21 dplyr_0.3.0.2 reshape2_1.4
##
## loaded via a namespace (and not attached):
## [1] assertthat_0.1 DBI_0.3.1 digest_0.6.4 evaluate_0.5.5
## [5] formatR_1.0 htmltools_0.2.6 knitr_1.8 lazyeval_0.1.9
## [9] magrittr_1.0.1 MASS_7.3-35 nnet_7.3-8 parallel_3.1.1
## [13] plyr_1.8.1 Rcpp_0.11.3 rmarkdown_0.3.10 stringr_0.6.2
## [17] tools_3.1.1 yaml_2.1.13