ggtheme <- function(legend.position = "bottom") {
theme_classic(base_size = 14) %+replace%
theme(aspect.ratio = 1,
legend.position = legend.position,
legend.text = element_text(size = 14),
axis.title = element_text(size = 15),
legend.title = element_text(size = 15),
plot.title = element_text(size = 16),
strip.text = element_text(size = 14))
}LSA-Mielnicka-vignette
R Markdown
Function made by Francesco for pretty graphs
summary function
data_summary <- function(x) {
m <- mean(x)
ymin <- m-sd(x)
ymax <- m+sd(x)
return(c(y=m,ymin=ymin,ymax=ymax))
}data load gfp/gfp line
gfp_breeding_table = readxl::read_xlsx(path = "Mielnicka_source_data_16042024_updated_names.xlsx", sheet = 7)
head(gfp_breeding_table)# A tibble: 6 × 18
maleID genotype cage pregnancy birth_date no_pups_born males...7
<chr> <chr> <chr> <dbl> <dttm> <dbl> <chr>
1 MBO-000971 gfp/gfp bmbo… 1 2019-10-27 00:00:00 7 4
2 MBO-000971 gfp/gfp bmbo… 2 2019-11-17 00:00:00 9 4
3 MBO-000971 gfp/gfp bmbo… 3 2020-01-20 00:00:00 11 4
4 MBO-000971 gfp/gfp bmbo… 4 2020-02-16 00:00:00 9 4
5 MBO-000971 gfp/gfp bmbo… 5 2020-04-02 00:00:00 8 NA
6 MBO-000972 gfp/gfp bmbo… 1 2019-11-04 00:00:00 9 2
# ℹ 11 more variables: females...8 <chr>, weight_at_birth <chr>,
# no_of_pups_weaned <dbl>, males...11 <chr>, females...12 <chr>,
# weight_at_weaning <chr>, end_of_breeding <dttm>, DOB <dttm>,
# breeding_start_date <dttm>, time_of_breeding <dbl>, fathers_age <dbl>birth weights for progeny sired by gfp/gfp males vs WT
# graph
gfp_breeding_table %>%
separate_rows(weight_at_birth, sep = "-") %>%
type_convert(col_types = cols(weight_at_birth = col_double())) %>%
ggplot(aes(x = genotype, y = weight_at_birth, fill = genotype)) +
geom_violin(trim = F) +
geom_boxplot(width = 0.2) +
stat_summary(fun = mean, geom = "point", shape = 5, size = 2) +
labs(y="Weight of pups at birth (g)", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("gfp/gfp", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()
# testing
gfp_breeding_table %>%
separate_rows(weight_at_birth, sep = "-") %>%
type_convert(col_types = cols(weight_at_birth = col_double())) %>%
t_test(weight_at_birth ~ genotype, alternative = "two.sided") %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 weight_at_birth gfp/gfp WT 188 199 4.59 380. 0.00000611 **** gfp_breeding_table %>%
separate_rows(weight_at_birth, sep = "-") %>%
type_convert(col_types = cols(weight_at_birth = col_double())) %>%
dplyr::rename(genotype = genotype) %>%
group_by(genotype) %>%
summarise(mean = mean(weight_at_birth), median = median(weight_at_birth), sd = sd(weight_at_birth))# A tibble: 2 × 4
genotype mean median sd
<chr> <dbl> <dbl> <dbl>
1 WT 1.31 1.3 0.176
2 gfp/gfp 1.40 1.4 0.187weaning weights for progeny sired by gfp/gfp males vs WT
#graph
gfp_breeding_table %>%
separate_rows(weight_at_weaning, sep = "-") %>%
type_convert(col_types = cols(weight_at_weaning = col_double())) %>%
drop_na(weight_at_weaning) %>%
ggplot(aes(x = genotype, y = weight_at_weaning, fill = genotype)) +
geom_violin(trim = F) +
geom_boxplot(width = 0.2) +
stat_summary(fun = mean, shape = 5, size = 0.5) +
labs(y="Weight of pups at weening (g)", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("gfp/gfp", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
#testing
gfp_breeding_table %>%
separate_rows(weight_at_weaning, sep = "-") %>%
type_convert(col_types = cols(weight_at_weaning = col_double())) %>%
drop_na(weight_at_weaning) %>%
t_test(weight_at_weaning ~ genotype, alternative = "greater") %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 weight_at_weaning gfp/gfp WT 169 193 1.53 353. 0.064 ns gfp_breeding_table %>%
separate_rows(weight_at_birth, sep = "-") %>%
type_convert(col_types = cols(weight_at_birth = col_double())) %>%
drop_na(weight_at_birth) %>%
group_by(genotype) %>%
dplyr::summarise(mean = mean(weight_at_birth), median = median(weight_at_birth), sd = sd(weight_at_birth))# A tibble: 2 × 4
genotype mean median sd
<chr> <dbl> <dbl> <dbl>
1 WT 1.31 1.3 0.176
2 gfp/gfp 1.40 1.4 0.187data load for phd/phd line
phd_breeding_table = readxl::read_xlsx(path = "Mielnicka_source_data_16042024_updated_names.xlsx", sheet = 8)
head(phd_breeding_table)# A tibble: 6 × 18
cage genotype pregnancy father_ID birth_date n_pups_born males...7
<chr> <chr> <dbl> <chr> <dttm> <dbl> <chr>
1 bmbo-0… phd/phd 1 MBO-0014… 2020-06-15 00:00:00 7 5
2 bmbo-0… phd/phd 2 MBO-0014… 2020-09-09 00:00:00 8 5
3 bmbo-0… phd/phd 3 MBO-0014… 2020-10-05 00:00:00 8 NA
4 bmbo-0… phd/phd 1 MBO-0014… 2020-06-12 00:00:00 11 NA
5 bmbo-0… phd/phd 1 MBO-0014… 2020-06-26 00:00:00 8 4
6 bmbo-0… phd/phd 2 MBO-0014… 2020-08-08 00:00:00 10 5
# ℹ 11 more variables: females...8 <chr>, weight_at_birth <chr>,
# n_of_pups_weaned <dbl>, males...11 <chr>, females...12 <chr>,
# weight_at_weaning <chr>, end_of_breeding <dttm>, DOB <dttm>,
# breeding_start_date <dttm>, time_of_breeding <dbl>, fathers_age <dbl>birth weights for progeny sired by phd/phd males vs WT
phd_breeding_table %>%
separate_rows(weight_at_birth, sep = "-") %>%
type_convert(col_types = cols(weight_at_birth = col_double())) %>%
drop_na(weight_at_birth) %>%
ggplot(aes(x = genotype, y = weight_at_birth, fill = genotype)) +
geom_violin(trim = F) +
geom_boxplot(width=0.1) +
stat_summary(fun=mean, geom="point", shape=23, size=2) +
labs(y="Weight of pups at birth (g)", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "phd/phd")) +
scale_fill_discrete(name = "genotype", labels = c("phd/phd", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()
#testing
phd_breeding_table %>%
separate_rows(weight_at_birth, sep = "-") %>%
type_convert(col_types = cols(weight_at_birth = col_double())) %>%
drop_na(weight_at_birth) %>%
t_test(weight_at_birth ~ genotype) %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 weight_at_birth phd/phd WT 200 359 2.35 435. 0.0192 * phd_breeding_table %>%
separate_rows(weight_at_birth, sep = "-") %>%
type_convert(col_types = cols(weight_at_birth = col_double())) %>%
drop_na(weight_at_birth) %>%
group_by(genotype) %>%
dplyr::summarise(mean = mean(weight_at_birth), median = median(weight_at_birth), sd = sd(weight_at_birth))# A tibble: 2 × 4
genotype mean median sd
<chr> <dbl> <dbl> <dbl>
1 WT 1.34 1.3 0.205
2 phd/phd 1.38 1.38 0.192ween weights for progeny sired by phd/phd males vs WT
#graph
phd_breeding_table %>%
separate_rows(weight_at_weaning, sep = "-") %>%
type_convert(col_types = cols(weight_at_weaning = col_double())) %>%
drop_na(weight_at_weaning) %>%
ggplot(aes(x = genotype, y = weight_at_weaning, fill = genotype)) +
geom_violin(trim = F) +
geom_boxplot(width = 0.1) +
stat_summary(fun.y=mean, geom="point", shape=23, size=2) +
labs(y="Weight of pups at weaning (g)", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "phd/phd")) +
scale_fill_discrete(name = "genotype", labels = c("phd/phd", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
ℹ Please use the `fun` argument instead.
#testing
phd_breeding_table %>%
separate_rows(weight_at_weaning, sep = "-") %>%
type_convert(col_types = cols(weight_at_weaning = col_double())) %>%
drop_na(weight_at_weaning) %>%
t_test(weight_at_weaning ~ genotype, alternative = "greater") %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 weight_at_weaning phd/phd WT 135 274 0.691 251. 0.245 ns phd_breeding_table %>%
separate_rows(weight_at_weaning, sep = "-") %>%
type_convert(col_types = cols(weight_at_weaning = col_double())) %>%
drop_na(weight_at_weaning) %>%
group_by(genotype) %>%
summarise(mean = mean(weight_at_weaning), median = median(weight_at_weaning), sd = sd(weight_at_weaning))# A tibble: 2 × 4
genotype mean median sd
<chr> <dbl> <dbl> <dbl>
1 WT 8.62 8.4 1.66
2 phd/phd 8.74 8.67 1.78litter sizes gfp/gfp
gfp_breeding_table %>%
drop_na(no_pups_born) %>%
ggplot(aes(x = genotype, y = no_pups_born, fill = genotype)) +
geom_boxplot() +
geom_dotplot(size = 3, binaxis='y', stackdir='center') +
stat_summary(fun.y=mean, geom="point", shape=23, size=2) +
labs(y="litter size distribution", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("gfp/gfp", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Warning in geom_dotplot(size = 3, binaxis = "y", stackdir = "center"): Ignoring
unknown parameters: `size`Bin width defaults to 1/30 of the range of the data. Pick better value with
`binwidth`.
gfp_breeding_table %>%
drop_na(no_pups_born) %>%
t_test(no_pups_born ~ genotype) %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 no_pups_born gfp/gfp WT 20 24 1.74 41.7 0.0894 ns gfp_breeding_table %>%
group_by(genotype) %>%
summarise(mean = mean(no_pups_born), median = median(no_pups_born), sd = sd(no_pups_born))# A tibble: 2 × 4
genotype mean median sd
<chr> <dbl> <dbl> <dbl>
1 WT 8.29 8.5 2.40
2 gfp/gfp 9.4 9.5 1.82litter sizes phd/phd
phd_breeding_table %>%
drop_na(n_pups_born) %>%
ggplot(aes(x = genotype, y = n_pups_born, fill = genotype)) +
geom_boxplot() +
geom_dotplot(size = 3, binaxis='y', stackdir='center') +
stat_summary(fun.y=mean, geom="point", shape=23, size=2) +
labs(y="litter size distribution", x = "") +
scale_x_discrete(limits = c("WT", "phd/phd")) +
scale_fill_discrete(name = "genotype", labels = c("phd/phd", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Warning in geom_dotplot(size = 3, binaxis = "y", stackdir = "center"): Ignoring
unknown parameters: `size`Bin width defaults to 1/30 of the range of the data. Pick better value with
`binwidth`.
phd_breeding_table %>%
drop_na(n_pups_born) %>%
t_test(n_pups_born ~ genotype) %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 n_pups_born phd/phd WT 30 51 -0.621 57.0 0.537 ns phd_breeding_table %>%
group_by(genotype) %>%
summarise(mean = mean(n_pups_born), median = median(n_pups_born), sd = sd(n_pups_born))# A tibble: 2 × 4
genotype mean median sd
<chr> <dbl> <dbl> <dbl>
1 WT 7.04 7 2.47
2 phd/phd 6.67 7 2.68female breeding
fem_breeding_table = readxl::read_xlsx(path = "Mielnicka_source_data_16042024_updated_names.xlsx", sheet = 9)
head(fem_breeding_table)# A tibble: 6 × 18
femaleID genotype cage pregnancy birth_date no_pups_born males...7
<chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
1 MBO-000977 gfp/gfp BMBO… 1 2019-10-27 00:00:00 6 5
2 MBO-000977 gfp/gfp BMBO… 2 2019-11-20 00:00:00 11 5
3 MBO-000976 gfp/gfp bmbo… 1 2019-10-27 00:00:00 9 5
4 MBO-000976 gfp/gfp bmbo… 2 2019-11-20 00:00:00 10 6
5 MBO-000975 gfp/gfp BMBO… 1 2019-10-30 00:00:00 8 2
6 MBO-000975 gfp/gfp BMBO… 2 2019-11-23 00:00:00 10 5
# ℹ 11 more variables: females...8 <dbl>, weight_at_birth <chr>,
# no_of_pups_weaned <dbl>, males...11 <chr>, females...12 <chr>,
# weight_at_weaning <chr>, end_of_breeding <dttm>, DOB <dttm>,
# breeding_start_date <dttm>, fem_breeding <dbl>, mothers_age <dbl>birth weights females gfp/gfp
fem_breeding_table %>%
separate_rows(weight_at_birth, sep = "-") %>%
type_convert(col_types = cols(weight_at_birth = col_double())) %>%
drop_na(weight_at_birth) %>%
ggplot(aes(x = genotype, y = weight_at_birth, fill = genotype)) +
geom_violin(trim = F) +
geom_boxplot(width=0.1) +
stat_summary(fun=mean, geom="point", shape=23, size=2) +
labs(y="Weight of pups at birth (g)", x = "") +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("gfp/gfp", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()
# testing
fem_breeding_table %>%
separate_rows(weight_at_birth, sep = "-") %>%
type_convert(col_types = cols(weight_at_birth = col_double())) %>%
drop_na(weight_at_birth) %>%
t_test(weight_at_birth ~ genotype) %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 weight_at_birth gfp/gfp WT 54 71 0.912 91.6 0.364 ns fem_breeding_table %>%
separate_rows(weight_at_birth, sep = "-") %>%
type_convert(col_types = cols(weight_at_birth = col_double())) %>%
drop_na(weight_at_birth) %>%
group_by(genotype) %>%
dplyr::summarise(mean = mean(weight_at_birth), median = median(weight_at_birth), sd = sd(weight_at_birth))# A tibble: 2 × 4
genotype mean median sd
<chr> <dbl> <dbl> <dbl>
1 WT 1.31 1.3 0.187
2 gfp/gfp 1.35 1.3 0.262gfp/gfp fem littersizes
fem_breeding_table %>%
drop_na(no_pups_born) %>%
ggplot(aes(x = genotype, y = no_pups_born, fill = genotype)) +
geom_boxplot() +
geom_dotplot(size = 3, binaxis='y', stackdir='center') +
stat_summary(fun.y=mean, geom="point", shape=23, size=2) +
labs(y="litter size distribution", x = "") +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("gfp/gfp", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Warning in geom_dotplot(size = 3, binaxis = "y", stackdir = "center"): Ignoring
unknown parameters: `size`Bin width defaults to 1/30 of the range of the data. Pick better value with
`binwidth`.
fem_breeding_table %>%
drop_na(no_pups_born) %>%
t_test(no_pups_born ~ genotype) %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 no_pups_born gfp/gfp WT 6 10 1.96 11.8 0.0746 ns fem_breeding_table %>%
group_by(genotype) %>%
summarise(mean = mean(no_pups_born), median = median(no_pups_born), sd = sd(no_pups_born))# A tibble: 2 × 4
genotype mean median sd
<chr> <dbl> <dbl> <dbl>
1 WT 7.1 7 2.02
2 gfp/gfp 9 9.5 1.79testis weight gfp/gfp data load
gfp_testis_table = readxl::read_xlsx(path = "Mielnicka_source_data_16042024_updated_names.xlsx", sheet = 1)
gfp_testis_table = gfp_testis_table %>%
pivot_longer(cols = testis_1_weight_g:testis_2_weight_g, names_to = "testis_num", values_to = "testis_weight")
head(gfp_testis_table)# A tibble: 6 × 8
replicate DOB genotype age sort_date ID
<dbl> <dttm> <chr> <dbl> <dttm> <dbl>
1 1 2021-01-08 00:00:00 WT 23 2021-06-17 00:00:00 1558
2 1 2021-01-08 00:00:00 WT 23 2021-06-17 00:00:00 1558
3 2 2021-01-08 00:00:00 gfp/gfp 23 2021-06-18 00:00:00 1562
4 2 2021-01-08 00:00:00 gfp/gfp 23 2021-06-18 00:00:00 1562
5 3 2021-01-08 00:00:00 gfp/gfp 23 2021-06-22 00:00:00 1561
6 3 2021-01-08 00:00:00 gfp/gfp 23 2021-06-22 00:00:00 1561
# ℹ 2 more variables: testis_num <chr>, testis_weight <dbl>tesits weight gfp/gfp test
gfp_testis_table %>%
ggplot(aes(x = genotype, y = testis_weight*1000, fill = genotype)) +
geom_boxplot() +
geom_dotplot(size = 3, binaxis='y', stackdir='center') +
stat_summary(fun.y=mean, geom="point", shape=23, size=2) +
labs(y="testis weight (mg)", x = "") +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("gfp/gfp", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Warning in geom_dotplot(size = 3, binaxis = "y", stackdir = "center"): Ignoring
unknown parameters: `size`Bin width defaults to 1/30 of the range of the data. Pick better value with
`binwidth`.
gfp_testis_table %>%
drop_na(testis_weight) %>%
t_test(testis_weight ~ genotype) %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 testis_weight gfp/gfp WT 12 12 -0.138 18.4 0.891 ns gfp_testis_table %>%
group_by(genotype) %>%
summarise(mean = mean(testis_weight), median = median(testis_weight), sd = sd(testis_weight))# A tibble: 2 × 4
genotype mean median sd
<chr> <dbl> <dbl> <dbl>
1 WT 0.109 0.11 0.0121
2 gfp/gfp 0.108 0.106 0.0195sperm parameters gfp/gfp data load
sperm_parameters_gfp = gfp_testis_table = readxl::read_xlsx(path = "Mielnicka_source_data_16042024_updated_names.xlsx", sheet = 2)
head(sperm_parameters_gfp)# A tibble: 6 × 7
Mutation ID DOB Total Number million…¹ `% Total Motility`
<chr> <chr> <dttm> <dbl> <dbl>
1 WT MBO-00… 2020-08-27 00:00:00 37 51
2 WT MBO-00… 2021-07-26 00:00:00 38 32
3 WT MBO-00… 2021-07-26 00:00:00 32 39
4 WT MBO-00… 2021-08-09 00:00:00 29 32
5 gfp/gfp MBO-00… 2020-06-17 00:00:00 38 32
6 gfp/gfp MBO-00… 2020-06-17 00:00:00 30 47
# ℹ abbreviated name: ¹`Total Number million /mL`
# ℹ 2 more variables: `% Progressive Motility` <dbl>,
# `% Vibrating Motility` <dbl>sperm_parameters_gfp = sperm_parameters_gfp %>%
pivot_longer(cols = `Total Number million /mL`:`% Vibrating Motility`, values_to = "measurments", names_to = "parameter")
head(sperm_parameters_gfp)# A tibble: 6 × 5
Mutation ID DOB parameter measurments
<chr> <chr> <dttm> <chr> <dbl>
1 WT MBO-006795 2020-08-27 00:00:00 Total Number million /mL 37
2 WT MBO-006795 2020-08-27 00:00:00 % Total Motility 51
3 WT MBO-006795 2020-08-27 00:00:00 % Progressive Motility 32
4 WT MBO-006795 2020-08-27 00:00:00 % Vibrating Motility 19
5 WT MBO-008419 2021-07-26 00:00:00 Total Number million /mL 38
6 WT MBO-008419 2021-07-26 00:00:00 % Total Motility 32ivf_gfp = readxl::read_xlsx(path = "Mielnicka_source_data_16042024_updated_names.xlsx", sheet = 3)
head(ivf_gfp)# A tibble: 6 × 6
Mutation ID DOB `N. Oocytes` `N. 2-Cells` `% Fertilization`
<chr> <chr> <dttm> <dbl> <dbl> <dbl>
1 gfp/gfp MBO-… 2021-03-11 00:00:00 81 72 88.9
2 gfp/gfp MBO-… 2021-03-11 00:00:00 80 67 83.8
3 gfp/gfp MBO-… 2021-03-11 00:00:00 79 68 86.1
4 gfp/gfp MBO-… 2021-03-11 00:00:00 56 49 87.5
5 gfp/gfp MBO-… 2021-03-11 00:00:00 59 53 89.8
6 gfp/gfp MBO-… 2021-03-11 00:00:00 100 88 88 ivf_gfp = ivf_gfp %>%
pivot_longer(cols = `N. Oocytes`:`% Fertilization`, values_to = "measurments", names_to = "parameter")
head(ivf_gfp)# A tibble: 6 × 5
Mutation ID DOB parameter measurments
<chr> <chr> <dttm> <chr> <dbl>
1 gfp/gfp MBO-007550 2021-03-11 00:00:00 N. Oocytes 81
2 gfp/gfp MBO-007550 2021-03-11 00:00:00 N. 2-Cells 72
3 gfp/gfp MBO-007550 2021-03-11 00:00:00 % Fertilization 88.9
4 gfp/gfp MBO-007550 2021-03-11 00:00:00 N. Oocytes 80
5 gfp/gfp MBO-007550 2021-03-11 00:00:00 N. 2-Cells 67
6 gfp/gfp MBO-007550 2021-03-11 00:00:00 % Fertilization 83.8sperm parameters & IVF gfp/gfp
sperm count
sperm_parameters_gfp %>%
dplyr::filter(parameter == "Total Number million /mL") %>%
ggplot(aes(x = Mutation, y = measurments, fill = Mutation)) +
geom_boxplot() +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("WT", "gfp/gfp")) +
labs(y="total number million/ml", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("gfp/gfp", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
sperm_parameters_gfp %>%
dplyr::filter(parameter == "Total Number million /mL") %>%
t_test(measurments ~ Mutation) %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 measurments gfp/gfp WT 10 4 0.146 7.21 0.888 ns sperm_parameters_gfp %>%
dplyr::filter(parameter == "Total Number million /mL") %>%
group_by(Mutation) %>%
summarise(mean = mean(measurments), median = median(measurments), sd = sd(measurments)) # A tibble: 2 × 4
Mutation mean median sd
<chr> <dbl> <dbl> <dbl>
1 WT 34 34.5 4.24
2 gfp/gfp 34.4 33 5.44sperm motility in % for gfp/gfp
sperm_parameters_gfp %>%
dplyr::filter(!parameter == "Total Number million /mL") %>%
ggplot(aes(x = Mutation, y = measurments, fill = Mutation)) +
geom_boxplot() +
facet_wrap(~factor(parameter, levels = c("% Progressive Motility", "% Vibrating Motility", "% Total Motility"))) +
geom_boxplot() +
stat_summary(fun = mean, shape = 5, size = 0.4) +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("WT", "gfp/gfp")) +
labs(y="sperm paramters", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("gfp/gfp", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
sperm_parameters_gfp %>%
dplyr::filter(!parameter == "Total Number million /mL") %>%
group_by(parameter) %>%
t_test(measurments ~ Mutation) %>%
add_significance()# A tibble: 3 × 10
parameter .y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 % Progressive … meas… gfp/g… WT 10 4 -0.710 5.20 0.508 ns
2 % Total Motili… meas… gfp/g… WT 10 4 0.157 4.88 0.882 ns
3 % Vibrating Mo… meas… gfp/g… WT 10 4 1.56 8.82 0.153 ns sperm_parameters_gfp %>%
dplyr::filter(!parameter == "Total Number million /mL") %>%
group_by(parameter, Mutation) %>%
summarise(mean = mean(measurments), median = median(measurments), sd = sd(measurments))`summarise()` has grouped output by 'parameter'. You can override using the
`.groups` argument.# A tibble: 6 × 5
# Groups: parameter [3]
parameter Mutation mean median sd
<chr> <chr> <dbl> <dbl> <dbl>
1 % Progressive Motility WT 24 23 6.32
2 % Progressive Motility gfp/gfp 21.4 21 5.85
3 % Total Motility WT 38.5 35.5 8.96
4 % Total Motility gfp/gfp 39.3 39 7.67
5 % Vibrating Motility WT 14.5 13.5 3.11
6 % Vibrating Motility gfp/gfp 17.9 17 4.82IVF results gfp/gfp
ivf_gfp %>%
dplyr::filter(parameter == "% Fertilization") %>%
ggplot(aes(x = Mutation, y = measurments, fill = Mutation)) +
geom_boxplot() +
stat_summary(fun = mean, shape = 5, size = 0.5) +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("WT", "gfp/gfp")) +
labs(y="percentage of oocytes", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("gfp/gfp", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
ivf_gfp %>%
dplyr::filter(parameter == "% Fertilization") %>%
t_test(measurments ~ Mutation) %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 measurments gfp/gfp WT 21 12 1.70 20.0 0.104 ns # number of oocytes and fertilised eggs reaching 2cell stage (2N)
ivf_gfp %>%
dplyr::filter(!parameter == "% Fertilization") %>%
ggplot(aes(x = Mutation, y = measurments, fill = Mutation)) +
geom_boxplot() +
stat_summary(fun = mean, shape = 5, size = 0.5) +
facet_wrap(~factor(parameter, levels =c("N. Oocytes", "N. 2-Cells"))) +
scale_x_discrete(limits = c("WT", "gfp/gfp"), labels = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("WT", "gfp/gfp")) +
labs(y="percentage of oocytes", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "gfp/gfp")) +
scale_fill_discrete(name = "genotype", labels = c("gfp/gfp", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
testis weight phd/phd
phd_testis_table = readxl::read_xlsx(path = "Mielnicka_source_data_16042024_updated_names.xlsx", sheet = 4)
phd_testis_table = phd_testis_table %>%
pivot_longer(cols = testis_1_weight:testis_2_weight, names_to = "testis_num", values_to = "testis_weight")
head(phd_testis_table) # A tibble: 6 × 5
male_ID total_body_weight genotype testis_num testis_weight
<chr> <dbl> <chr> <chr> <dbl>
1 MBO-001492 36.5 phd/phd testis_1_weight 0.114
2 MBO-001492 36.5 phd/phd testis_2_weight 0.119
3 MBO-001498 37.7 phd/phd testis_1_weight 0.105
4 MBO-001498 37.7 phd/phd testis_2_weight 0.114
5 MBO-001499 33.1 phd/phd testis_1_weight 0.12
6 MBO-001499 33.1 phd/phd testis_2_weight 0.11 phd_testis_table %>%
ggplot(aes(x = genotype, y = testis_weight*1000, fill = genotype)) +
geom_boxplot() +
geom_dotplot(size = 3, binaxis='y', stackdir='center') +
stat_summary(fun.y=mean, geom="point", shape=23, size=2) +
labs(y="testis weight (mg)", x = "") +
scale_x_discrete(limits = c("WT", "phd/phd")) +
scale_fill_discrete(name = "genotype", labels = c("phd/phd", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Warning in geom_dotplot(size = 3, binaxis = "y", stackdir = "center"): Ignoring
unknown parameters: `size`Bin width defaults to 1/30 of the range of the data. Pick better value with
`binwidth`.
phd_testis_table %>%
drop_na(testis_weight) %>%
t_test(testis_weight ~ genotype) %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 testis_weight phd/phd WT 18 20 -1.65 34.4 0.108 ns phd_testis_table %>%
group_by(genotype) %>%
summarise(mean = mean(testis_weight), median = median(testis_weight), sd = sd(testis_weight))# A tibble: 2 × 4
genotype mean median sd
<chr> <dbl> <dbl> <dbl>
1 WT 0.116 0.116 0.0133
2 phd/phd 0.110 0.11 0.00958sperm parameters phd/phd data load
sperm_parameters_phd = readxl::read_xlsx(path = "Mielnicka_source_data_16042024_updated_names.xlsx", sheet = 5)
head(sperm_parameters_phd)# A tibble: 6 × 7
Mutation ID DOB Total Number million…¹ `% Total Motility`
<chr> <chr> <dttm> <dbl> <dbl>
1 WT MBO-00… 2021-04-06 00:00:00 35 23
2 WT MBO-00… 2021-04-06 00:00:00 44 23
3 WT MBO-00… 2021-04-06 00:00:00 32 29
4 WT MBO-00… 2021-07-19 00:00:00 40 21
5 WT MBO-00… 2021-07-19 00:00:00 35 29
6 WT MBO-00… 2021-08-09 00:00:00 39 25
# ℹ abbreviated name: ¹`Total Number million /mL`
# ℹ 2 more variables: `% Progressive Motility` <dbl>,
# `% Vibrating Motility` <dbl>sperm_parameters_phd = sperm_parameters_phd %>%
pivot_longer(cols = `Total Number million /mL`:`% Vibrating Motility`, values_to = "measurments", names_to = "parameter")
head(sperm_parameters_phd)# A tibble: 6 × 5
Mutation ID DOB parameter measurments
<chr> <chr> <dttm> <chr> <dbl>
1 WT MBO-007723 2021-04-06 00:00:00 Total Number million /mL 35
2 WT MBO-007723 2021-04-06 00:00:00 % Total Motility 23
3 WT MBO-007723 2021-04-06 00:00:00 % Progressive Motility 14
4 WT MBO-007723 2021-04-06 00:00:00 % Vibrating Motility 9
5 WT MBO-007737 2021-04-06 00:00:00 Total Number million /mL 44
6 WT MBO-007737 2021-04-06 00:00:00 % Total Motility 23ivf_phd = readxl::read_xlsx(path = "Mielnicka_source_data_16042024_updated_names.xlsx", sheet = 6)
head(ivf_phd)# A tibble: 6 × 7
Mutation ID DOB `Lab ID` `N. Oocytes` `N. 2-Cells`
<chr> <chr> <dttm> <dbl> <dbl> <dbl>
1 WT MBO-007723 2021-04-06 00:00:00 4117 79 74
2 WT MBO-007723 2021-04-06 00:00:00 4117 60 57
3 WT MBO-007723 2021-04-06 00:00:00 4117 90 84
4 WT MBO-007737 2021-04-06 00:00:00 4106 90 78
5 WT MBO-007737 2021-04-06 00:00:00 4106 93 87
6 WT MBO-007737 2021-04-06 00:00:00 4106 65 60
# ℹ 1 more variable: `% Fertilization` <dbl>ivf_phd = ivf_phd %>%
pivot_longer(cols = `N. Oocytes`:`% Fertilization`, values_to = "measurments", names_to = "parameter")
head(ivf_phd)# A tibble: 6 × 6
Mutation ID DOB `Lab ID` parameter measurments
<chr> <chr> <dttm> <dbl> <chr> <dbl>
1 WT MBO-007723 2021-04-06 00:00:00 4117 N. Oocytes 79
2 WT MBO-007723 2021-04-06 00:00:00 4117 N. 2-Cells 74
3 WT MBO-007723 2021-04-06 00:00:00 4117 % Fertilization 93.7
4 WT MBO-007723 2021-04-06 00:00:00 4117 N. Oocytes 60
5 WT MBO-007723 2021-04-06 00:00:00 4117 N. 2-Cells 57
6 WT MBO-007723 2021-04-06 00:00:00 4117 % Fertilization 95 sperm count phd/phd
sperm_parameters_phd %>%
dplyr::filter(parameter == "Total Number million /mL") %>%
ggplot(aes(x = Mutation, y = measurments, fill = Mutation)) +
geom_boxplot() +
scale_x_discrete(limits = c("WT", "phd/phd")) +
scale_fill_discrete(name = "genotype", labels = c("WT", "phd/phd")) +
labs(y="total number million/ml", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "phd/phd")) +
scale_fill_discrete(name = "genotype", labels = c("phd/phd", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
sperm_parameters_phd %>%
dplyr::filter(parameter == "Total Number million /mL") %>%
t_test(measurments ~ Mutation) %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 measurments phd/phd WT 6 6 0.405 9.42 0.694 ns sperm_parameters_phd %>%
dplyr::filter(parameter == "Total Number million /mL") %>%
group_by(Mutation) %>%
summarise(mean = mean(measurments), median = median(measurments), sd = sd(measurments)) # A tibble: 2 × 4
Mutation mean median sd
<chr> <dbl> <dbl> <dbl>
1 WT 37.5 37 4.32
2 phd/phd 38.7 42 5.57sperm motility in % phd/phd
sperm_parameters_phd %>%
dplyr::filter(!parameter == "Total Number million /mL") %>%
ggplot(aes(x = Mutation, y = measurments, fill = Mutation)) +
geom_boxplot() +
facet_wrap(~factor(parameter, levels = c("% Progressive Motility", "% Vibrating Motility", "% Total Motility"))) +
geom_boxplot() +
stat_summary(fun = mean, shape = 5, size = 0.4) +
scale_x_discrete(limits = c("WT", "phd/phd")) +
scale_fill_discrete(name = "genotype", labels = c("WT", "phd/phd")) +
labs(y="sperm paramters", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "phd/phd")) +
scale_fill_discrete(name = "genotype", labels = c("phd/phd", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
sperm_parameters_phd %>%
dplyr::filter(!parameter == "Total Number million /mL") %>%
group_by(parameter) %>%
t_test(measurments ~ Mutation) %>%
add_significance()# A tibble: 3 × 10
parameter .y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 % Progressive … meas… phd/p… WT 6 6 0.558 9.09 0.59 ns
2 % Total Motili… meas… phd/p… WT 6 6 0.939 9.70 0.37 ns
3 % Vibrating Mo… meas… phd/p… WT 6 6 1.4 9.98 0.192 ns sperm_parameters_phd %>%
dplyr::filter(!parameter == "Total Number million /mL") %>%
group_by(parameter, Mutation) %>%
summarise(mean = mean(measurments), median = median(measurments), sd = sd(measurments))`summarise()` has grouped output by 'parameter'. You can override using the
`.groups` argument.# A tibble: 6 × 5
# Groups: parameter [3]
parameter Mutation mean median sd
<chr> <chr> <dbl> <dbl> <dbl>
1 % Progressive Motility WT 15.2 15 2.14
2 % Progressive Motility phd/phd 16 15.5 2.97
3 % Total Motility WT 25 24 3.35
4 % Total Motility phd/phd 27 26.5 4
5 % Vibrating Motility WT 9.83 9.5 1.47
6 % Vibrating Motility phd/phd 11 11 1.41IVF results phd/phd
ivf_phd %>%
dplyr::filter(parameter == "% Fertilization") %>%
ggplot(aes(x = Mutation, y = measurments, fill = Mutation)) +
geom_boxplot() +
stat_summary(fun = mean, shape = 5, size = 0.5) +
scale_x_discrete(limits = c("WT", "phd/phd")) +
scale_fill_discrete(name = "genotype", labels = c("WT", "phd/phd")) +
labs(y="percentage of oocytes", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "phd/phd")) +
scale_fill_discrete(name = "genotype", labels = c("phd/phd", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
ivf_phd %>%
dplyr::filter(parameter == "% Fertilization") %>%
t_test(measurments ~ Mutation) %>%
add_significance()# A tibble: 1 × 9
.y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 measurments phd/phd WT 18 18 1.19 32.0 0.243 ns # number of oocytes and fertilised eggs reaching 2cell stage (2N)
ivf_phd %>%
dplyr::filter(!parameter == "% Fertilization") %>%
ggplot(aes(x = Mutation, y = measurments, fill = Mutation)) +
geom_boxplot() +
stat_summary(fun = mean, shape = 5, size = 0.5) +
facet_wrap(~factor(parameter, levels =c("N. Oocytes", "N. 2-Cells"))) +
scale_x_discrete(limits = c("WT", "phd/phd"), labels = c("WT", "phd/phd")) +
scale_fill_discrete(name = "genotype", labels = c("WT", "phd/phd")) +
labs(y="percentage of oocytes", x = "") +
guides(fill=guide_legend(title="genotype")) +
scale_x_discrete(limits = c("WT", "phd/phd")) +
scale_fill_discrete(name = "genotype", labels = c("phd/phd", "WT")) +
guides(fill = guide_legend(reverse=TRUE)) +
ggtheme()Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_segment()`).
ivf_phd %>%
dplyr::filter(!parameter == "% Fertilization") %>%
group_by(parameter) %>%
t_test(measurments ~ Mutation) %>%
add_significance()# A tibble: 2 × 10
parameter .y. group1 group2 n1 n2 statistic df p p.signif
<chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 N. 2-Cells measurmen… phd/p… WT 18 18 0.674 33.8 0.505 ns
2 N. Oocytes measurmen… phd/p… WT 18 18 0.0926 33.8 0.927 ns