In DrSeacow/DBSStats2Labs:
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
This function requires loading of the "stringr," "Tidyr," and "dplyr" packages.
This function assumes that, when imaging your brain tissue, a group-specific tag is put at the beginning of the file name for each of the groups. The first of these will ultimately become the same thing input under the "first_group" term.
It is recommended that groups are code-named for anonymity of condition to avoid experimenter bias, along all steps of the immunohistochemistry process, if possible.
For area percentage analyses, not split by plane (averages across all planes and uses unpaired-samples t-test):
immunot_percent <- function(type, measure, region, first_group, second_group, n_per_group, data){
library(stringr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(patchwork)
immunot_data <- as.data.frame(read.csv("percent_ps6_BLA.csv", header = TRUE, sep = ",", dec = ".")) %>%
select(c("Label", "Mean", "X.Area"))
immunot_data <- separate(immunot_data, "Label", c("Group", NA, "Subject", NA, "Plane", "Side", NA, NA, NA, NA, NA, NA), "_")
gp1_grand_mean <- mean(immunot_data[immunot_data$Group == 'Ctrl', 'X.Area'])
gp2_grand_mean <- mean(immunot_data[immunot_data$Group == 'Switch', 'X.Area'])
gp1_grand_sterr <- sd(immunot_data[immunot_data$Group == 'Ctrl', 'X.Area']) / sqrt(6)
gp2_grand_sterr <- sd(immunot_data[immunot_data$Group == 'Switch', 'X.Area']) / sqrt(6)
cross_plane_sterrs <- c(gp1_grand_sterr, gp2_grand_sterr)
cross_plane_compare <- as.numeric(c(gp1_grand_mean, gp2_grand_mean))
graph_df_without_plane <- tibble(Group = c("Ctrl", "Switch"),
Percent_PS6 = cross_plane_compare)
ggplot(graph_df_without_plane, aes(x = Group, y = Percent_PS6)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Percent_PS6 - cross_plane_sterrs), ymax = (Percent_PS6 + cross_plane_sterrs)))
gp1_grand_values <- c(immunot_data[immunot_data$Group == 'Ctrl', 'X.Area'])
gp2_grand_values <- c(immunot_data[immunot_data$Group == 'Switch', 'X.Area'])
overall_t <- t.test(gp1_grand_values, gp2_grand_values, var.equal = TRUE)
overall_t
ant_t <- t.test(immunot_data)
}
For area percentage analyses, split by plane (this treats "plane" as a factor in two-way factorial ANOVA):
immunot_percent <- function(type, measure, region, first_group, n_per_group, data){
library(stringr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(patchwork)
immunot_data <- as.data.frame(read.csv("percent_ps6_BLA.csv", header = TRUE, sep = ",", dec = ".")) %>%
select(c("Label", "Mean", "X.Area"))
immunot_data <- separate(immunot_data, "Label", c("Group", NA, "Subject", NA, "Plane", "Side", NA, NA, NA, NA, NA, NA), "_")
means_by_plane <- tibble(aggregate(immunot_data$X.Area, FUN = mean, by = list(Plane = immunot_data$Plane, Group = immunot_data$Group)))
sds_by_plane <- tibble(aggregate(immunot_data$X.Area, FUN = sd, by = list(Plane = immunot_data$Plane, Group = immunot_data$Group)))
gp1_mean_ant <- means_by_plane[1,3]
gp1_mean_med <- means_by_plane[2,3]
gp1_mean_pos <- means_by_plane[3,3]
gp2_mean_ant <- means_by_plane[4,3]
gp2_mean_med <- means_by_plane[5,3]
gp2_mean_pos <- means_by_plane[6,3]
gp1_sterr_ant <- sds_by_plane[1,3] / sqrt(6)
gp1_sterr_med <- sds_by_plane[2,3] / sqrt(6)
gp1_sterr_pos <- sds_by_plane[3,3] / sqrt(6)
gp2_sterr_ant <- sds_by_plane[4,3] / sqrt(6)
gp2_sterr_med <- sds_by_plane[5,3] / sqrt(6)
gp2_sterr_pos <- sds_by_plane[6,3] / sqrt(6)
ant_compare <- as.numeric(c(gp1_mean_ant, gp2_mean_ant))
med_compare <- as.numeric(c(gp1_mean_med, gp2_mean_med))
pos_compare <- as.numeric(c(gp1_mean_pos, gp2_mean_pos))
ant_sterrs <- as.numeric(c(gp1_sterr_ant, gp2_sterr_ant))
med_sterrs <- as.numeric(c(gp1_sterr_med, gp2_sterr_med))
pos_sterrs <- as.numeric(c(gp1_sterr_pos, gp2_sterr_pos))
graph_df_ant <- tibble(Ant = rep(c("Ctrl", "Switch")),
Percent_PS6 = ant_compare)
graph_df_med <- tibble(Med = rep(c("Ctrl", "Switch")),
Percent_PS6 = med_compare)
graph_df_pos <- tibble(Pos = rep(c("Ctrl", "Switch")),
Percent_PS6 = pos_compare)
a <- ggplot(graph_df_ant, aes(x = Ant, y = Percent_PS6)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Percent_PS6 - ant_sterrs), ymax = (Percent_PS6 + ant_sterrs)))
m <- ggplot(graph_df_med, aes(x = Med, y = Percent_PS6)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Percent_PS6 - med_sterrs), ymax = (Percent_PS6 + med_sterrs)))
p <- ggplot(graph_df_pos, aes(x = Pos, y = Percent_PS6)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Percent_PS6 - pos_sterrs), ymax = (Percent_PS6 + pos_sterrs)))
a+m+p
plane_wise_anova <- summary(aov(X.Area ~ Plane*Group, data = immunot_data))
plane_wise_anova
}
For cell count analyses, combined across planes
immunot_percent <- function(type, measure, region, first_group, n_per_group, data){
library(stringr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(patchwork)
immunot_data <- as.data.frame(read.csv("outline_VTA.csv", header = TRUE, sep = ",", dec = ".")) %>%
select(c("Label", "Mean", "X.Area"))
immunot_data <- separate(immunot_data, "Label", c("Group", NA, "Subject", NA, "Plane", "Side", NA), "_")
values_by_group <- tibble(aggregate(immunot_data$Plane, FUN = length, by = list(Plane = immunot_data$Plane, Group = immunot_data$Group, immunot_data$Subject)))
values_by_group_avg_by_plane <- tibble(aggregate(values_by_group$x, FUN = mean, by = list(Group = values_by_group$Group, Subject = values_by_group$Group.3)))
gp1_values <- as.vector(values_by_group_avg_by_plane$x[values_by_group_avg_by_plane$Group == "Ctrl"])
gp2_values <- as.vector(values_by_group_avg_by_plane$x[values_by_group_avg_by_plane$Group == "Switch"])
gp1_grand_mean <- sum(values_by_group_avg_by_plane[values_by_group_avg_by_plane$Group == 'Ctrl', 'x']) / 6
gp2_grand_mean <- sum(values_by_group_avg_by_plane[values_by_group_avg_by_plane$Group == 'Switch', 'x']) / 6
gp1_sterr <- sd(gp1_values) / sqrt(6)
gp2_sterr <- sd(gp2_values) / sqrt(6)
cross_plane_sds <- c(gp1_sterr, gp2_sterr)
cross_plane_compare <- as.numeric(c(gp1_grand_mean, gp2_grand_mean))
graph_df_without_plane <- tibble(Group = c("Ctrl", "Switch"),
Cells_Counted = cross_plane_compare)
ggplot(graph_df_without_plane, aes(x = Group, y = Cells_Counted)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Cells_Counted - cross_plane_sds), ymax = (Cells_Counted + cross_plane_sds)))
overall_t <- t.test(gp1_values, gp2_values, var.equal = TRUE)
overall_t
}
For cell count analyses, split across planes
immunot_percent <- function(type, measure, region, first_group, data){
library(stringr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(patchwork)
immunot_data <- as.data.frame(read.csv("outline_VTA.csv", header = TRUE, sep = ",", dec = ".")) %>%
select(c("Label", "Mean", "X.Area"))
immunot_data <- separate(immunot_data, "Label", c("Group", NA, "Subject", NA, "Plane", "Side", NA, NA, NA, NA, NA, NA), "_")
counts_by_plane <- tibble(aggregate(immunot_data$Plane, FUN = length, by = list(Plane = immunot_data$Plane, Group = immunot_data$Group, immunot_data$Subject)))
gp1_ant <- counts_by_plane[counts_by_plane$Group == "Ctrl" & counts_by_plane$Plane == "Ant", ]
gp1_ant_values <- as.vector(t(gp1_ant %>% select(x)))
gp2_ant <- counts_by_plane[counts_by_plane$Group == "Switch" & counts_by_plane$Plane == "Ant", ]
gp2_ant_values <- as.vector(t(gp2_ant %>% select(x)))
gp1_med <- counts_by_plane[counts_by_plane$Group == "Ctrl" & counts_by_plane$Plane == "Med", ]
gp1_med_values <- as.vector(t(gp1_med %>% select(x)))
gp2_med <- counts_by_plane[counts_by_plane$Group == "Switch" & counts_by_plane$Plane == "Med", ]
gp2_med_values <- as.vector(t(gp2_med %>% select(x)))
gp1_pos <- counts_by_plane[counts_by_plane$Group == "Ctrl" & counts_by_plane$Plane == "Pos", ]
gp1_pos_values <- as.vector(t(gp1_pos %>% select(x)))
gp2_pos <- counts_by_plane[counts_by_plane$Group == "Switch" & counts_by_plane$Plane == "Pos", ]
gp2_pos_values <- as.vector(t(gp2_pos %>% select(x)))
plane_count_aov <- tibble(Group = rep(rep(c("gp1", "gp2"), each = 6), 3),
Plane = rep(c("ant", "med", "pos"), each = 12),
Counts = c(gp1_ant_values, gp2_ant_values, gp1_med_values, gp2_med_values, gp1_pos_values, gp2_pos_values)
)
means_by_plane <- aggregate(counts_by_plane$x, FUN = mean, by = list(Plane = counts_by_plane$Plane, Group = counts_by_plane$Group))
sds_by_plane <- tibble(aggregate(counts_by_plane$x, FUN = sd, by = list(Plane = counts_by_plane$Plane, Group = counts_by_plane$Group)))
gp1_mean_ant <- means_by_plane[1,3]
gp1_mean_med <- means_by_plane[2,3]
gp1_mean_pos <- means_by_plane[3,3]
gp2_mean_ant <- means_by_plane[4,3]
gp2_mean_med <- means_by_plane[5,3]
gp2_mean_pos <- means_by_plane[6,3]
gp1_sterr_ant <- sds_by_plane[1,3] / sqrt(length(gp1_ant_values))
gp1_sterr_med <- sds_by_plane[2,3] / sqrt(length(gp1_med_values))
gp1_sterr_pos <- sds_by_plane[3,3] / sqrt(length(gp1_pos_values))
gp2_sterr_ant <- sds_by_plane[4,3] / sqrt(length(gp2_ant_values))
gp2_sterr_med <- sds_by_plane[5,3] / sqrt(length(gp2_med_values))
gp2_sterr_pos <- sds_by_plane[6,3] / sqrt(length(gp2_pos_values))
ant_compare <- as.numeric(c(gp1_mean_ant, gp2_mean_ant))
med_compare <- as.numeric(c(gp1_mean_med, gp2_mean_med))
pos_compare <- as.numeric(c(gp1_mean_pos, gp2_mean_pos))
ant_sterrs <- as.numeric(c(gp1_sterr_ant, gp2_sterr_ant))
med_sterrs <- as.numeric(c(gp1_sterr_med, gp2_sterr_med))
pos_sterrs <- as.numeric(c(gp1_sterr_pos, gp2_sterr_pos))
graph_df_ant <- tibble(Ant = rep(c("Ctrl", "Switch")),
Cells_Counted = ant_compare)
graph_df_med <- tibble(Med = rep(c("Ctrl", "Switch")),
Cells_Counted = med_compare)
graph_df_pos <- tibble(Pos = rep(c("Ctrl", "Switch")),
Cells_Counted = pos_compare)
a <- ggplot(graph_df_ant, aes(x = Ant, y = Cells_Counted)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Cells_Counted - ant_sterrs), ymax = (Cells_Counted + ant_sterrs)))
m <- ggplot(graph_df_med, aes(x = Med, y = Cells_Counted)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Cells_Counted - med_sterrs), ymax = (Cells_Counted + med_sterrs)))
p <- ggplot(graph_df_pos, aes(x = Pos, y = Cells_Counted)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Cells_Counted - pos_sterrs), ymax = (Cells_Counted + pos_sterrs)))
a+m+p
plane_wise_anova <- summary(aov(Counts ~ Plane*Group, data = plane_count_aov))
plane_wise_anova
}
For intensity analyses, combined across planes
immunot_percent <- function(type, measure, region, first_group, n_per_group, data){
immunot_data <- as.data.frame(read.csv("outline_VTA.csv", header = TRUE, sep = ",", dec = ".")) %>%
select(c("Label", "Mean", "X.Area"))
immunot_data <- separate(immunot_data, "Label", c("Group", NA, "Subject", NA, "Plane", "Side", NA), "_")
gp1_grand_mean <- mean(immunot_data[immunot_data$Group == 'Ctrl', 'Mean'])
gp2_grand_mean <- mean(immunot_data[immunot_data$Group == 'Switch', 'Mean'])
gp1_grand_sterr <- sd(immunot_data[immunot_data$Group == 'Ctrl', 'Mean']) / sqrt(length(immunot_data[immunot_data$Group == 'Ctrl', 'Mean']))
gp2_grand_sterr <- sd(immunot_data[immunot_data$Group == 'Switch', 'Mean']) / sqrt(length(immunot_data[immunot_data$Group == 'Switch', 'Mean']))
cross_plane_sterr <- c(gp1_grand_sterr, gp2_grand_sterr)
cross_plane_compare <- as.numeric(c(gp1_grand_mean, gp2_grand_mean))
graph_df_without_plane <- tibble(Group = c("Ctrl", "Switch"),
Intensity = cross_plane_compare)
ggplot(graph_df_without_plane, aes(x = Group, y = Intensity)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Intensity - cross_plane_sterr), ymax = (Intensity + cross_plane_sterr)))
values_by_group <- tibble(aggregate(immunot_data$Mean, FUN = mean, by = list(Group = immunot_data$Group, Subject = immunot_data$Subject)))
values_by_group_avg_by_plane <- tibble(aggregate(values_by_group$x, FUN = mean, by = list(Group = values_by_group$Group, Subject = values_by_group$Group.3)))
gp1_values <- as.vector(values_by_group$x[values_by_group$Group == "Ctrl"])
gp2_values <- as.vector(values_by_group$x[values_by_group$Group == "Switch"])
overall_t <- t.test(gp1_values, gp2_values, var.equal = TRUE)
overall_t
}
For intensity analyses, split across planes
immunot_percent <- function(type, measure, region, first_group, data){
immunot_data <- as.data.frame(read.csv("percent_ps6_BLA.csv", header = TRUE, sep = ",", dec = ".")) %>%
select(c("Label", "Mean", "X.Area"))
immunot_data <- separate(immunot_data, "Label", c("Group", NA, "Subject", NA, "Plane", "Side", NA, NA, NA, NA, NA, NA), "_")
means_by_plane <- tibble(aggregate(immunot_data$Mean, FUN = mean, by = list(Plane = immunot_data$Plane, Group = immunot_data$Group)))
sds_by_plane <- tibble(aggregate(immunot_data$Mean, FUN = sd, by = list(Plane = immunot_data$Plane, Group = immunot_data$Group)))
gp1_mean_ant <- means_by_plane[1,3]
gp1_mean_med <- means_by_plane[2,3]
gp1_mean_pos <- means_by_plane[3,3]
gp2_mean_ant <- means_by_plane[4,3]
gp2_mean_med <- means_by_plane[5,3]
gp2_mean_pos <- means_by_plane[6,3]
gp1_sterr_ant <- sds_by_plane[1,3] / sqrt(6)
gp1_sterr_med <- sds_by_plane[2,3] / sqrt(6)
gp1_sterr_pos <- sds_by_plane[3,3] / sqrt(6)
gp2_sterr_ant <- sds_by_plane[4,3] / sqrt(6)
gp2_sterr_med <- sds_by_plane[5,3] / sqrt(6)
gp2_sterr_pos <- sds_by_plane[6,3] / sqrt(6)
ant_compare <- as.numeric(c(gp1_mean_ant, gp2_mean_ant))
med_compare <- as.numeric(c(gp1_mean_med, gp2_mean_med))
pos_compare <- as.numeric(c(gp1_mean_pos, gp2_mean_pos))
ant_sterrs <- as.numeric(c(gp1_sterr_ant, gp2_sterr_ant))
med_sterrs <- as.numeric(c(gp1_sterr_med, gp2_sterr_med))
pos_sterrs <- as.numeric(c(gp1_sterr_pos, gp2_sterr_pos))
graph_df_ant <- tibble(Ant = rep(c("Ctrl", "Switch")),
Intensity = ant_compare)
graph_df_med <- tibble(Med = rep(c("Ctrl", "Switch")),
Intensity = med_compare)
graph_df_pos <- tibble(Pos = rep(c("Ctrl", "Switch")),
Intensity = pos_compare)
a <- ggplot(graph_df_ant, aes(x = Ant, y = Intensity)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Intensity - ant_sterrs), ymax = (Intensity + ant_sterrs)))
m <- ggplot(graph_df_med, aes(x = Med, y = Intensity)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Intensity - med_sterrs), ymax = (Intensity + med_sterrs)))
p <- ggplot(graph_df_pos, aes(x = Pos, y = Intensity)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Intensity - pos_sterrs), ymax = (Intensity + pos_sterrs)))
a+m+p
plane_wise_anova <- summary(aov(Mean ~ Plane*Group, data = immunot_data))
plane_wise_anova
}
library(DBSStats2Labs)
DrSeacow/DBSStats2Labs documentation built on June 1, 2022, 7:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
This function requires loading of the "stringr," "Tidyr," and "dplyr" packages.
This function assumes that, when imaging your brain tissue, a group-specific tag is put at the beginning of the file name for each of the groups. The first of these will ultimately become the same thing input under the "first_group" term.
It is recommended that groups are code-named for anonymity of condition to avoid experimenter bias, along all steps of the immunohistochemistry process, if possible.
For area percentage analyses, not split by plane (averages across all planes and uses unpaired-samples t-test):
immunot_percent <- function(type, measure, region, first_group, second_group, n_per_group, data){
library(stringr) library(dplyr) library(tidyr) library(ggplot2) library(patchwork)
immunot_data <- as.data.frame(read.csv("percent_ps6_BLA.csv", header = TRUE, sep = ",", dec = ".")) %>% select(c("Label", "Mean", "X.Area")) immunot_data <- separate(immunot_data, "Label", c("Group", NA, "Subject", NA, "Plane", "Side", NA, NA, NA, NA, NA, NA), "_") gp1_grand_mean <- mean(immunot_data[immunot_data$Group == 'Ctrl', 'X.Area']) gp2_grand_mean <- mean(immunot_data[immunot_data$Group == 'Switch', 'X.Area']) gp1_grand_sterr <- sd(immunot_data[immunot_data$Group == 'Ctrl', 'X.Area']) / sqrt(6) gp2_grand_sterr <- sd(immunot_data[immunot_data$Group == 'Switch', 'X.Area']) / sqrt(6) cross_plane_sterrs <- c(gp1_grand_sterr, gp2_grand_sterr) cross_plane_compare <- as.numeric(c(gp1_grand_mean, gp2_grand_mean)) graph_df_without_plane <- tibble(Group = c("Ctrl", "Switch"), Percent_PS6 = cross_plane_compare) ggplot(graph_df_without_plane, aes(x = Group, y = Percent_PS6)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Percent_PS6 - cross_plane_sterrs), ymax = (Percent_PS6 + cross_plane_sterrs)))
gp1_grand_values <- c(immunot_data[immunot_data$Group == 'Ctrl', 'X.Area']) gp2_grand_values <- c(immunot_data[immunot_data$Group == 'Switch', 'X.Area'])
overall_t <- t.test(gp1_grand_values, gp2_grand_values, var.equal = TRUE)
overall_t
ant_t <- t.test(immunot_data)
}
For area percentage analyses, split by plane (this treats "plane" as a factor in two-way factorial ANOVA):
immunot_percent <- function(type, measure, region, first_group, n_per_group, data){
library(stringr) library(dplyr) library(tidyr) library(ggplot2) library(patchwork)
immunot_data <- as.data.frame(read.csv("percent_ps6_BLA.csv", header = TRUE, sep = ",", dec = ".")) %>% select(c("Label", "Mean", "X.Area")) immunot_data <- separate(immunot_data, "Label", c("Group", NA, "Subject", NA, "Plane", "Side", NA, NA, NA, NA, NA, NA), "_")
means_by_plane <- tibble(aggregate(immunot_data$X.Area, FUN = mean, by = list(Plane = immunot_data$Plane, Group = immunot_data$Group)))
sds_by_plane <- tibble(aggregate(immunot_data$X.Area, FUN = sd, by = list(Plane = immunot_data$Plane, Group = immunot_data$Group)))
gp1_mean_ant <- means_by_plane[1,3] gp1_mean_med <- means_by_plane[2,3] gp1_mean_pos <- means_by_plane[3,3] gp2_mean_ant <- means_by_plane[4,3] gp2_mean_med <- means_by_plane[5,3] gp2_mean_pos <- means_by_plane[6,3] gp1_sterr_ant <- sds_by_plane[1,3] / sqrt(6) gp1_sterr_med <- sds_by_plane[2,3] / sqrt(6) gp1_sterr_pos <- sds_by_plane[3,3] / sqrt(6) gp2_sterr_ant <- sds_by_plane[4,3] / sqrt(6) gp2_sterr_med <- sds_by_plane[5,3] / sqrt(6) gp2_sterr_pos <- sds_by_plane[6,3] / sqrt(6)
ant_compare <- as.numeric(c(gp1_mean_ant, gp2_mean_ant))
med_compare <- as.numeric(c(gp1_mean_med, gp2_mean_med))
pos_compare <- as.numeric(c(gp1_mean_pos, gp2_mean_pos))
ant_sterrs <- as.numeric(c(gp1_sterr_ant, gp2_sterr_ant))
med_sterrs <- as.numeric(c(gp1_sterr_med, gp2_sterr_med))
pos_sterrs <- as.numeric(c(gp1_sterr_pos, gp2_sterr_pos))
graph_df_ant <- tibble(Ant = rep(c("Ctrl", "Switch")), Percent_PS6 = ant_compare)
graph_df_med <- tibble(Med = rep(c("Ctrl", "Switch")), Percent_PS6 = med_compare)
graph_df_pos <- tibble(Pos = rep(c("Ctrl", "Switch")), Percent_PS6 = pos_compare)
a <- ggplot(graph_df_ant, aes(x = Ant, y = Percent_PS6)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Percent_PS6 - ant_sterrs), ymax = (Percent_PS6 + ant_sterrs))) m <- ggplot(graph_df_med, aes(x = Med, y = Percent_PS6)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Percent_PS6 - med_sterrs), ymax = (Percent_PS6 + med_sterrs))) p <- ggplot(graph_df_pos, aes(x = Pos, y = Percent_PS6)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Percent_PS6 - pos_sterrs), ymax = (Percent_PS6 + pos_sterrs))) a+m+p
plane_wise_anova <- summary(aov(X.Area ~ Plane*Group, data = immunot_data)) plane_wise_anova
}
For cell count analyses, combined across planes
immunot_percent <- function(type, measure, region, first_group, n_per_group, data){
library(stringr) library(dplyr) library(tidyr) library(ggplot2) library(patchwork)
immunot_data <- as.data.frame(read.csv("outline_VTA.csv", header = TRUE, sep = ",", dec = ".")) %>% select(c("Label", "Mean", "X.Area")) immunot_data <- separate(immunot_data, "Label", c("Group", NA, "Subject", NA, "Plane", "Side", NA), "_") values_by_group <- tibble(aggregate(immunot_data$Plane, FUN = length, by = list(Plane = immunot_data$Plane, Group = immunot_data$Group, immunot_data$Subject))) values_by_group_avg_by_plane <- tibble(aggregate(values_by_group$x, FUN = mean, by = list(Group = values_by_group$Group, Subject = values_by_group$Group.3)))
gp1_values <- as.vector(values_by_group_avg_by_plane$x[values_by_group_avg_by_plane$Group == "Ctrl"]) gp2_values <- as.vector(values_by_group_avg_by_plane$x[values_by_group_avg_by_plane$Group == "Switch"])
gp1_grand_mean <- sum(values_by_group_avg_by_plane[values_by_group_avg_by_plane$Group == 'Ctrl', 'x']) / 6 gp2_grand_mean <- sum(values_by_group_avg_by_plane[values_by_group_avg_by_plane$Group == 'Switch', 'x']) / 6 gp1_sterr <- sd(gp1_values) / sqrt(6) gp2_sterr <- sd(gp2_values) / sqrt(6) cross_plane_sds <- c(gp1_sterr, gp2_sterr) cross_plane_compare <- as.numeric(c(gp1_grand_mean, gp2_grand_mean)) graph_df_without_plane <- tibble(Group = c("Ctrl", "Switch"), Cells_Counted = cross_plane_compare) ggplot(graph_df_without_plane, aes(x = Group, y = Cells_Counted)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Cells_Counted - cross_plane_sds), ymax = (Cells_Counted + cross_plane_sds)))
overall_t <- t.test(gp1_values, gp2_values, var.equal = TRUE) overall_t
}
For cell count analyses, split across planes
immunot_percent <- function(type, measure, region, first_group, data){
library(stringr) library(dplyr) library(tidyr) library(ggplot2) library(patchwork)
immunot_data <- as.data.frame(read.csv("outline_VTA.csv", header = TRUE, sep = ",", dec = ".")) %>% select(c("Label", "Mean", "X.Area")) immunot_data <- separate(immunot_data, "Label", c("Group", NA, "Subject", NA, "Plane", "Side", NA, NA, NA, NA, NA, NA), "_")
counts_by_plane <- tibble(aggregate(immunot_data$Plane, FUN = length, by = list(Plane = immunot_data$Plane, Group = immunot_data$Group, immunot_data$Subject)))
gp1_ant <- counts_by_plane[counts_by_plane$Group == "Ctrl" & counts_by_plane$Plane == "Ant", ] gp1_ant_values <- as.vector(t(gp1_ant %>% select(x))) gp2_ant <- counts_by_plane[counts_by_plane$Group == "Switch" & counts_by_plane$Plane == "Ant", ] gp2_ant_values <- as.vector(t(gp2_ant %>% select(x)))
gp1_med <- counts_by_plane[counts_by_plane$Group == "Ctrl" & counts_by_plane$Plane == "Med", ] gp1_med_values <- as.vector(t(gp1_med %>% select(x))) gp2_med <- counts_by_plane[counts_by_plane$Group == "Switch" & counts_by_plane$Plane == "Med", ] gp2_med_values <- as.vector(t(gp2_med %>% select(x)))
gp1_pos <- counts_by_plane[counts_by_plane$Group == "Ctrl" & counts_by_plane$Plane == "Pos", ] gp1_pos_values <- as.vector(t(gp1_pos %>% select(x))) gp2_pos <- counts_by_plane[counts_by_plane$Group == "Switch" & counts_by_plane$Plane == "Pos", ] gp2_pos_values <- as.vector(t(gp2_pos %>% select(x)))
plane_count_aov <- tibble(Group = rep(rep(c("gp1", "gp2"), each = 6), 3), Plane = rep(c("ant", "med", "pos"), each = 12), Counts = c(gp1_ant_values, gp2_ant_values, gp1_med_values, gp2_med_values, gp1_pos_values, gp2_pos_values) )
means_by_plane <- aggregate(counts_by_plane$x, FUN = mean, by = list(Plane = counts_by_plane$Plane, Group = counts_by_plane$Group))
sds_by_plane <- tibble(aggregate(counts_by_plane$x, FUN = sd, by = list(Plane = counts_by_plane$Plane, Group = counts_by_plane$Group)))
gp1_mean_ant <- means_by_plane[1,3] gp1_mean_med <- means_by_plane[2,3] gp1_mean_pos <- means_by_plane[3,3] gp2_mean_ant <- means_by_plane[4,3] gp2_mean_med <- means_by_plane[5,3] gp2_mean_pos <- means_by_plane[6,3] gp1_sterr_ant <- sds_by_plane[1,3] / sqrt(length(gp1_ant_values)) gp1_sterr_med <- sds_by_plane[2,3] / sqrt(length(gp1_med_values)) gp1_sterr_pos <- sds_by_plane[3,3] / sqrt(length(gp1_pos_values)) gp2_sterr_ant <- sds_by_plane[4,3] / sqrt(length(gp2_ant_values)) gp2_sterr_med <- sds_by_plane[5,3] / sqrt(length(gp2_med_values)) gp2_sterr_pos <- sds_by_plane[6,3] / sqrt(length(gp2_pos_values))
ant_compare <- as.numeric(c(gp1_mean_ant, gp2_mean_ant))
med_compare <- as.numeric(c(gp1_mean_med, gp2_mean_med))
pos_compare <- as.numeric(c(gp1_mean_pos, gp2_mean_pos))
ant_sterrs <- as.numeric(c(gp1_sterr_ant, gp2_sterr_ant))
med_sterrs <- as.numeric(c(gp1_sterr_med, gp2_sterr_med))
pos_sterrs <- as.numeric(c(gp1_sterr_pos, gp2_sterr_pos))
graph_df_ant <- tibble(Ant = rep(c("Ctrl", "Switch")), Cells_Counted = ant_compare)
graph_df_med <- tibble(Med = rep(c("Ctrl", "Switch")), Cells_Counted = med_compare)
graph_df_pos <- tibble(Pos = rep(c("Ctrl", "Switch")), Cells_Counted = pos_compare)
a <- ggplot(graph_df_ant, aes(x = Ant, y = Cells_Counted)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Cells_Counted - ant_sterrs), ymax = (Cells_Counted + ant_sterrs))) m <- ggplot(graph_df_med, aes(x = Med, y = Cells_Counted)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Cells_Counted - med_sterrs), ymax = (Cells_Counted + med_sterrs))) p <- ggplot(graph_df_pos, aes(x = Pos, y = Cells_Counted)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Cells_Counted - pos_sterrs), ymax = (Cells_Counted + pos_sterrs))) a+m+p
plane_wise_anova <- summary(aov(Counts ~ Plane*Group, data = plane_count_aov)) plane_wise_anova
}
For intensity analyses, combined across planes
immunot_percent <- function(type, measure, region, first_group, n_per_group, data){
immunot_data <- as.data.frame(read.csv("outline_VTA.csv", header = TRUE, sep = ",", dec = ".")) %>% select(c("Label", "Mean", "X.Area")) immunot_data <- separate(immunot_data, "Label", c("Group", NA, "Subject", NA, "Plane", "Side", NA), "_") gp1_grand_mean <- mean(immunot_data[immunot_data$Group == 'Ctrl', 'Mean']) gp2_grand_mean <- mean(immunot_data[immunot_data$Group == 'Switch', 'Mean']) gp1_grand_sterr <- sd(immunot_data[immunot_data$Group == 'Ctrl', 'Mean']) / sqrt(length(immunot_data[immunot_data$Group == 'Ctrl', 'Mean'])) gp2_grand_sterr <- sd(immunot_data[immunot_data$Group == 'Switch', 'Mean']) / sqrt(length(immunot_data[immunot_data$Group == 'Switch', 'Mean'])) cross_plane_sterr <- c(gp1_grand_sterr, gp2_grand_sterr) cross_plane_compare <- as.numeric(c(gp1_grand_mean, gp2_grand_mean)) graph_df_without_plane <- tibble(Group = c("Ctrl", "Switch"), Intensity = cross_plane_compare) ggplot(graph_df_without_plane, aes(x = Group, y = Intensity)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Intensity - cross_plane_sterr), ymax = (Intensity + cross_plane_sterr)))
values_by_group <- tibble(aggregate(immunot_data$Mean, FUN = mean, by = list(Group = immunot_data$Group, Subject = immunot_data$Subject)))
values_by_group_avg_by_plane <- tibble(aggregate(values_by_group$x, FUN = mean, by = list(Group = values_by_group$Group, Subject = values_by_group$Group.3)))
gp1_values <- as.vector(values_by_group$x[values_by_group$Group == "Ctrl"]) gp2_values <- as.vector(values_by_group$x[values_by_group$Group == "Switch"])
overall_t <- t.test(gp1_values, gp2_values, var.equal = TRUE) overall_t
}
For intensity analyses, split across planes
immunot_percent <- function(type, measure, region, first_group, data){
immunot_data <- as.data.frame(read.csv("percent_ps6_BLA.csv", header = TRUE, sep = ",", dec = ".")) %>% select(c("Label", "Mean", "X.Area")) immunot_data <- separate(immunot_data, "Label", c("Group", NA, "Subject", NA, "Plane", "Side", NA, NA, NA, NA, NA, NA), "_")
means_by_plane <- tibble(aggregate(immunot_data$Mean, FUN = mean, by = list(Plane = immunot_data$Plane, Group = immunot_data$Group)))
sds_by_plane <- tibble(aggregate(immunot_data$Mean, FUN = sd, by = list(Plane = immunot_data$Plane, Group = immunot_data$Group)))
gp1_mean_ant <- means_by_plane[1,3] gp1_mean_med <- means_by_plane[2,3] gp1_mean_pos <- means_by_plane[3,3] gp2_mean_ant <- means_by_plane[4,3] gp2_mean_med <- means_by_plane[5,3] gp2_mean_pos <- means_by_plane[6,3] gp1_sterr_ant <- sds_by_plane[1,3] / sqrt(6) gp1_sterr_med <- sds_by_plane[2,3] / sqrt(6) gp1_sterr_pos <- sds_by_plane[3,3] / sqrt(6) gp2_sterr_ant <- sds_by_plane[4,3] / sqrt(6) gp2_sterr_med <- sds_by_plane[5,3] / sqrt(6) gp2_sterr_pos <- sds_by_plane[6,3] / sqrt(6)
ant_compare <- as.numeric(c(gp1_mean_ant, gp2_mean_ant))
med_compare <- as.numeric(c(gp1_mean_med, gp2_mean_med))
pos_compare <- as.numeric(c(gp1_mean_pos, gp2_mean_pos))
ant_sterrs <- as.numeric(c(gp1_sterr_ant, gp2_sterr_ant))
med_sterrs <- as.numeric(c(gp1_sterr_med, gp2_sterr_med))
pos_sterrs <- as.numeric(c(gp1_sterr_pos, gp2_sterr_pos))
graph_df_ant <- tibble(Ant = rep(c("Ctrl", "Switch")), Intensity = ant_compare)
graph_df_med <- tibble(Med = rep(c("Ctrl", "Switch")), Intensity = med_compare)
graph_df_pos <- tibble(Pos = rep(c("Ctrl", "Switch")), Intensity = pos_compare)
a <- ggplot(graph_df_ant, aes(x = Ant, y = Intensity)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Intensity - ant_sterrs), ymax = (Intensity + ant_sterrs))) m <- ggplot(graph_df_med, aes(x = Med, y = Intensity)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Intensity - med_sterrs), ymax = (Intensity + med_sterrs))) p <- ggplot(graph_df_pos, aes(x = Pos, y = Intensity)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = (Intensity - pos_sterrs), ymax = (Intensity + pos_sterrs))) a+m+p
plane_wise_anova <- summary(aov(Mean ~ Plane*Group, data = immunot_data)) plane_wise_anova
}
library(DBSStats2Labs)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.