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Genomic-Selection-for-Drought-Tolerance-Using-Genome-Wide-SNPs-in-Casava/
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|---|---|---|---|---|
| html | 502889a | WevertonGomesCosta | 2025-01-15 | update phenotype.html |
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Libraries
Load the necessary libraries:
library(kableExtra)
library(tidyverse)
#if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("ComplexHeatmap")
require(ComplexHeatmap)
library(data.table)
library(readxl)
library(metan)
library(DataExplorer)
library(ggthemes)
library(GGally)
theme_set(theme_bw())Data import and manipulation
Import the phenotypic dataset, excluding traits without information and redundant traits:
pheno <- read_excel("data/Phenotyping2.xlsx", na = "NA") %>%
select_if(~ !all(is.na(.))) %>% # Deleting traits without information
select(-c("Local", "Tratamento"))Convert character traits to factors, and numeric grades to integer factors:
Exploratory Data Analysis
Provide an introductory analysis of the dataset:
introduce(pheno) %>%
t() %>%
kbl(escape = F, align = 'c') %>%
kable_classic("hover", full_width = F, position = "center", fixed_thead = T)| rows | 2336 |
| columns | 29 |
| discrete_columns | 6 |
| continuous_columns | 23 |
| all_missing_columns | 0 |
| total_missing_values | 16875 |
| complete_rows | 440 |
| total_observations | 67744 |
| memory_usage | 534240 |
We don’t have any columns that have all of the missing observations, but we do have a lot of missing values in every dataset. Some manipulations should be performed to improve the quality of the data.
Environment Analysis
Heatmap visualization of clone presence per year:
pheno_year_count <- pheno %>%
count(Ano, Clone)
genmat <- model.matrix(~ -1 + Clone, data = pheno_year_count)
envmat <- model.matrix(~ -1 + Ano, data = pheno_year_count)
genenvmat <- t(envmat) %*% genmat
genenvmat_ch <- ifelse(genenvmat == 1, "Present", "Absent")
Heatmap(genenvmat_ch,
col = c("white", "tomato"),
show_column_names = F,
heatmap_legend_param = list(title = ""),
column_title = "Genotypes",
row_title = "Environments")
| Version | Author | Date |
|---|---|---|
| 77e21a6 | Weverton Gomes | 2025-01-13 |
Filter out the year 2016 due to insufficient observations:
Reinspect the updated heatmap:
pheno_year_count <- pheno %>%
count(Ano, Clone)
genmat <- model.matrix(~ -1 + Clone, data = pheno_year_count)
envmat <- model.matrix(~ -1 + Ano, data = pheno_year_count)
genenvmat <- t(envmat) %*% genmat
genenvmat_ch <- ifelse(genenvmat == 1, "Present", "Absent")
Heatmap(genenvmat_ch,
col = c("white", "tomato"),
show_column_names = F,
heatmap_legend_param = list(title = ""),
column_title = "Genotypes",
row_title = "Environments")
| Version | Author | Date |
|---|---|---|
| 77e21a6 | Weverton Gomes | 2025-01-13 |
Visualize the number of environments and genotypes:
genotype_year_table <- genenvmat %*% t(genenvmat) %>%
kbl(escape = F, align = 'c') %>%
kable_classic("hover", full_width = F, position = "center", fixed_thead = T)
rm(pheno_year_count, genmat, envmat, genenvmat, genenvmat_ch)
genotype_year_table| Ano2017 | Ano2018 | Ano2019 | Ano2020 | |
|---|---|---|---|---|
| Ano2017 | 165 | 42 | 22 | 14 |
| Ano2018 | 42 | 138 | 39 | 16 |
| Ano2019 | 22 | 39 | 133 | 29 |
| Ano2020 | 14 | 16 | 29 | 138 |
Observe that some clones were evaluated in only one year. The number of clones evaluated across different years is summarized below:
pheno %>%
count(Ano, Clone) %>%
count(Clone) %>%
count(n) %>%
kbl(escape = F, align = 'c', col.names = c("N of Environment", "N of Genotypes")) %>%
kable_classic("hover", full_width = F, position = "center", fixed_thead = T)Storing counts in `nn`, as `n` already present in input
ℹ Use `name = "new_name"` to pick a new name.| N of Environment | N of Genotypes |
|---|---|
| 1 | 350 |
| 2 | 72 |
| 3 | 20 |
| 4 | 5 |
Only 5 clones were evaluated in all years, which might affect the model’s accuracy. Therefore, adopting mixed models via REML in the analysis is suitable for obtaining BLUPs.
Analysis of traits
Provide descriptive statistics for each trait:
summary_table <- summary(pheno) %>%
t() %>%
kbl(escape = F, align = 'c') %>%
kable_classic("hover", full_width = F, position = "center", fixed_thead = T)
summary_table| Clone | BGM-0164: 16 | BGM-0170: 16 | BGM-0396: 16 | BGM-1243: 16 | BGM-1267: 16 | BGM-0134: 12 | (Other) :2204 |
| Ano | 2017:660 | 2018:552 | 2019:532 | 2020:552 | NA | NA | NA |
| Bloco | 1:574 | 2:574 | 3:574 | 4:574 | NA | NA | NA |
| row | 11 : 121 | 3 : 120 | 10 : 120 | 2 : 119 | 6 : 118 | 4 : 117 | (Other):1581 |
| col | 6 : 131 | 4 : 130 | 8 : 130 | 2 : 128 | 5 : 128 | 9 : 128 | (Other):1521 |
| N_Roots | Min. : 0.125 | 1st Qu.: 2.333 | Median : 4.000 | Mean : 4.293 | 3rd Qu.: 6.000 | Max. :15.667 | NA’s :322 |
| FRY | Min. : 0.116 | 1st Qu.: 1.857 | Median : 3.750 | Mean : 4.946 | 3rd Qu.: 6.857 | Max. :22.200 | NA’s :335 |
| ShY | Min. : 0.694 | 1st Qu.: 6.944 | Median :11.167 | Mean :14.228 | 3rd Qu.:18.714 | Max. :61.167 | NA’s :211 |
| DMC | Min. :11.98 | 1st Qu.:25.00 | Median :28.80 | Mean :29.06 | 3rd Qu.:32.78 | Max. :48.34 | NA’s :968 |
| StY | Min. :0.0154 | 1st Qu.:0.5494 | Median :1.2068 | Mean :1.5164 | 3rd Qu.:2.1092 | Max. :8.8669 | NA’s :978 |
| Plant.Height | Min. :0.3600 | 1st Qu.:0.9633 | Median :1.1600 | Mean :1.1919 | 3rd Qu.:1.3908 | Max. :3.0333 | NA’s :220 |
| HI | Min. : 1.574 | 1st Qu.:15.726 | Median :23.345 | Mean :24.556 | 3rd Qu.:31.884 | Max. :71.967 | NA’s :341 |
| StC | Min. : 7.326 | 1st Qu.:20.350 | Median :24.147 | Mean :24.419 | 3rd Qu.:28.160 | Max. :43.686 | NA’s :968 |
| PltArc | Min. :1.000 | 1st Qu.:1.000 | Median :2.000 | Mean :1.996 | 3rd Qu.:3.000 | Max. :5.000 | NA’s :678 |
| Leaf.Ret | Min. :0.3333 | 1st Qu.:1.0000 | Median :1.3333 | Mean :1.7796 | 3rd Qu.:2.0000 | Max. :5.0000 | NA’s :216 |
| Root.Le | Min. : 7.00 | 1st Qu.:19.00 | Median :23.00 | Mean :23.21 | 3rd Qu.:27.00 | Max. :47.33 | NA’s :339 |
| Root.Di | Min. : 6.12 | 1st Qu.:23.36 | Median :28.17 | Mean :28.88 | 3rd Qu.:33.50 | Max. :63.30 | NA’s :339 |
| Stem.D | Min. :1.013 | 1st Qu.:1.859 | Median :2.101 | Mean :2.112 | 3rd Qu.:2.362 | Max. :4.373 | NA’s :224 |
| Mite | Min. :1.000 | 1st Qu.:3.000 | Median :3.667 | Mean :3.475 | 3rd Qu.:4.000 | Max. :5.000 | NA’s :222 |
| Incidence_Mites | Min. :1 | 1st Qu.:1 | Median :1 | Mean :1 | 3rd Qu.:1 | Max. :1 | NA’s :674 |
| Nstem.Plant | Min. :1.000 | 1st Qu.:1.333 | Median :2.000 | Mean :2.131 | 3rd Qu.:2.667 | Max. :6.667 | NA’s :850 |
| Stand6MAP | Min. :1.000 | 1st Qu.:4.000 | Median :6.000 | Mean :5.202 | 3rd Qu.:7.000 | Max. :8.000 | NA’s :849 |
| Branching | Min. :0.0000 | 1st Qu.:0.0000 | Median :0.3333 | Mean :0.5911 | 3rd Qu.:1.0000 | Max. :4.0000 | NA’s :673 |
| Staygreen | Min. :1.000 | 1st Qu.:1.000 | Median :1.000 | Mean :1.292 | 3rd Qu.:2.000 | Max. :3.000 | NA’s :669 |
| Vigor | Mode:logical | TRUE:1075 | NA’s:1221 | NA | NA | NA | NA |
| Flowering | Min. :0.0000 | 1st Qu.:0.0000 | Median :0.0000 | Mean :0.0074 | 3rd Qu.:0.0000 | Max. :1.0000 | NA’s :673 |
| Canopy_Lenght | Min. : 10.00 | 1st Qu.: 54.33 | Median : 72.00 | Mean : 73.35 | 3rd Qu.: 91.67 | Max. :240.17 | NA’s :1458 |
| Canopy_Width | Min. : 7.50 | 1st Qu.: 56.33 | Median : 75.58 | Mean : 76.68 | 3rd Qu.: 96.00 | Max. :213.33 | NA’s :1458 |
| Leaf_Lenght | Min. : 3.750 | 1st Qu.: 8.417 | Median :11.250 | Mean :11.676 | 3rd Qu.:14.200 | Max. :32.667 | NA’s :1459 |
Exclude traits with high missing value ratios:
pheno <- pheno %>%
select(-c(Incidence_Mites, Vigor, Flowering, Leaf_Lenght, Canopy_Width, Canopy_Lenght))Ensure that the traits have acceptable missing value ratios:
| Version | Author | Date |
|---|---|---|
| 77e21a6 | Weverton Gomes | 2025-01-13 |
Evaluate the distribution of traits by year with histograms for quantitative traits:
| Version | Author | Date |
|---|---|---|
| 77e21a6 | Weverton Gomes | 2025-01-13 |
Remove traits that lack normal distribution:
Analisys of Clone
Inspect missing values by clone and year:
pheno_missing_summary <- pheno %>%
select(-Bloco, -row, -col) %>%
group_by(Clone, Ano) %>%
summarise_all(.funs = list(~ sum(is.na(.)))) %>%
ungroup() %>%
select_numeric_cols() %>%
mutate(mean = rowMeans(.),
Clone.Ano = factor(unique(interaction(pheno$Clone, pheno$Ano)))) %>%
filter(mean > 2) %>%
droplevels()
missing_genotypes <- nlevels(pheno_missing_summary$Clone.Ano) %>%
kbl(escape = F, align = 'c', col.names = c("N of genotypes")) %>%
kable_classic("hover", full_width = F, position = "center", fixed_thead = T)
missing_genotypes| N of genotypes |
|---|
| 54 |
Evaluate clone and year descriptive statistics:
clone_year_stats <- ge_details(pheno, Ano, Clone, resp = everything()) %>%
t() %>%
kbl(escape = F, align = 'c') %>%
kable_classic("hover", full_width = F, position = "center", fixed_thead = T)
clone_year_stats| Parameters | Mean | SE | SD | CV | Min | Max | MinENV | MaxENV | MinGEN | MaxGEN |
| N_Roots | 4.29 | 0.06 | 2.51 | 58.56 | 0.12 (BGM-1031 in 2017) | 15.67 (2012-107-002 in 2019) | 2018 (1.62) | 2019 (5.76) | BGM-0411 (0.33) | 2012-107-002 (11.33) |
| FRY | 4.95 | 0.09 | 4.04 | 81.79 | 0.12 (BGM-0886 in 2017) | 22.2 (BGM-1267 in 2018) | 2017 (2.75) | 2020 (6.52) | BGM-1488 (0.34) | IAC-14 (14.07) |
| ShY | 14.23 | 0.22 | 10.16 | 71.45 | 0.69 (BGM-0996 in 2017) | 61.17 (BGM-2124 in 2020) | 2017 (8.47) | 2020 (25.87) | BGM-0048 (1.52) | BGM-2124 (54.33) |
| DMC | 29.06 | 0.17 | 6.1 | 21 | 11.98 (BGM-0626 in 2020) | 48.34 (BGM-1015 in 2020) | 2020 (26.21) | 2018 (35.38) | BGM-0626 (14.98) | BGM-1015 (45.02) |
| StY | 1.52 | 0.04 | 1.27 | 84.01 | 0.02 (BGM-0340 in 2019) | 8.87 (BGM-0396 in 2018) | 2019 (1.32) | 2018 (1.84) | BGM-0089 (0.06) | BGM-1023 (4.76) |
| Plant.Height | 1.19 | 0.01 | 0.33 | 27.43 | 0.36 (BGM-0426 in 2020) | 3.03 (BR-11-24-156 in 2020) | 2017 (1) | 2019 (1.48) | Jatobá (0.58) | BGM-1200 (1.91) |
| HI | 24.56 | 0.27 | 11.89 | 48.42 | 1.57 (BGM-1159 in 2017) | 71.97 (BGM-1315 in 2018) | 2020 (18.61) | 2018 (32.36) | BGM-0961 (2.5) | Mata_Fome_Branca (52.78) |
| StC | 24.42 | 0.17 | 6.11 | 25.05 | 7.33 (BGM-0626 in 2020) | 43.69 (BGM-1015 in 2020) | 2020 (21.56) | 2018 (30.78) | BGM-0626 (10.33) | BGM-1015 (40.37) |
| Root.Le | 23.21 | 0.13 | 5.8 | 24.99 | 7 (BGM-1574 in 2017) | 47.33 (BGM-0396 in 2018) | 2017 (19.72) | 2019 (27.14) | Jatobá (8.5) | BGM-1956 (35.5) |
| Root.Di | 28.88 | 0.18 | 7.9 | 27.35 | 6.12 (BGM-2142 in 2019) | 63.3 (BRS Mulatinha in 2018) | 2017 (24.49) | 2018 (34.74) | BGM-0089 (12.14) | BGM-1956 (48.91) |
| Stem.D | 2.11 | 0.01 | 0.38 | 17.9 | 1.01 (BGM-0592 in 2018) | 4.37 (BRS Tapioqueira in 2020) | 2018 (2.02) | 2017 (2.16) | BGM-0048 (1.25) | BGM-1523 (2.93) |
| Nstem.Plant | 2.13 | 0.03 | 0.95 | 44.53 | 1 (BGM-0036 in 2018) | 6.67 (BGM-0714 in 2019) | 2018 (1.44) | 2019 (2.71) | BGM-0066 (1) | BGM-0451 (4.44) |
Again, some traits were not computed for the year 2017, so we have to eliminate that year when performing the analysis for these traits.
Evaluate the clone-only descriptive statistics for the traits.
cv_stats <- desc_stat(pheno, by = Ano, na.rm = TRUE, stats = "cv") %>%
na.omit() %>%
arrange(desc(cv)) %>%
pivot_wider(names_from = "Ano", values_from = "cv") %>%
kbl(escape = F, align = 'c') %>%
kable_classic("hover", full_width = F, position = "center", fixed_thead = T)
cv_stats| variable | 2018 | 2020 | 2019 | 2017 |
|---|---|---|---|---|
| StY | 91.8742 | 81.2220 | 72.3243 | NA |
| FRY | 86.0073 | 68.4455 | 70.0326 | 66.8379 |
| ShY | 69.0625 | 42.7475 | 47.4901 | 45.4973 |
| N_Roots | 63.2913 | 47.0839 | 47.2357 | 48.8708 |
| HI | 41.5442 | 43.3204 | 41.8572 | 48.2942 |
| Nstem.Plant | 33.3849 | 37.1507 | 37.3740 | NA |
| Plant.Height | 28.0409 | 24.0927 | 19.1125 | 21.0398 |
| StC | 15.6937 | 27.3600 | 16.5407 | NA |
| Root.Le | 27.1625 | 18.9730 | 21.1089 | 21.5823 |
| Root.Di | 23.3943 | 20.5858 | 24.3155 | 21.0039 |
| DMC | 13.6086 | 22.5059 | 13.8212 | NA |
| Stem.D | 22.3941 | 17.5042 | 15.7037 | 16.7774 |
Some traits were not computed for the year 2017, so we have to eliminate that year when performing the analysis for these traits and some traits presented hight cv, as StY and FRY.
General Inspection
Identifying outliers in all non-categorical variables:
inspect(pheno %>%
select_if(~ !is.factor(.)), verbose = FALSE) %>%
arrange(desc(Outlier)) %>%
kbl(escape = F, align = 'c') %>%
kable_classic(
"hover",
full_width = F,
position = "center",
fixed_thead = T
)| Variable | Class | Missing | Levels | Valid_n | Min | Median | Max | Outlier | Text |
|---|---|---|---|---|---|---|---|---|---|
| ShY | numeric | Yes |
|
2085 | 0.69 | 11.17 | 61.17 | 89 | NA |
| FRY | numeric | Yes |
|
1961 | 0.12 | 3.75 | 22.20 | 71 | NA |
| StY | numeric | Yes |
|
1318 | 0.02 | 1.21 | 8.87 | 49 | NA |
| Root.Di | numeric | Yes |
|
1957 | 6.12 | 28.17 | 63.30 | 32 | NA |
| Nstem.Plant | numeric | Yes |
|
1446 | 1.00 | 2.00 | 6.67 | 30 | NA |
| Plant.Height | numeric | Yes |
|
2076 | 0.36 | 1.16 | 3.03 | 23 | NA |
| Stem.D | numeric | Yes |
|
2072 | 1.01 | 2.10 | 4.37 | 22 | NA |
| Root.Le | numeric | Yes |
|
1957 | 7.00 | 23.00 | 47.33 | 19 | NA |
| N_Roots | numeric | Yes |
|
1974 | 0.12 | 4.00 | 15.67 | 16 | NA |
| HI | numeric | Yes |
|
1955 | 1.57 | 23.35 | 71.97 | 15 | NA |
| DMC | numeric | Yes |
|
1328 | 11.98 | 28.80 | 48.34 | 6 | NA |
| StC | numeric | Yes |
|
1328 | 7.33 | 24.15 | 43.69 | 6 | NA |
Confirming what was previously described, most traits with high coefficients of variation (CV) have many outliers.
Inspect overall data correlations and save clean data:
# Descriptive statistics (mean, minimum, maximum, CV)
pheno_mean_sd <- desc_stat(pheno, stats = c("mean, min, max, cv"), na.rm = TRUE)
pheno_mean_sd %>%
kbl(escape = F, align = 'c') %>%
kable_classic("hover", full_width = F, position = "center", fixed_thead = T)| variable | mean | min | max | cv |
|---|---|---|---|---|
| DMC | 29.0583 | 11.9760 | 48.3360 | 20.9974 |
| FRY | 4.9462 | 0.1160 | 22.2000 | 81.7937 |
| HI | 24.5559 | 1.5744 | 71.9673 | 48.4154 |
| N_Roots | 4.2929 | 0.1250 | 15.6670 | 58.5550 |
| Nstem.Plant | 2.1309 | 1.0000 | 6.6667 | 44.5279 |
| Plant.Height | 1.1919 | 0.3600 | 3.0333 | 27.4309 |
| Root.Di | 28.8785 | 6.1200 | 63.3033 | 27.3488 |
| Root.Le | 23.2147 | 7.0000 | 47.3333 | 24.9935 |
| ShY | 14.2276 | 0.6940 | 61.1670 | 71.4549 |
| StC | 24.4195 | 7.3260 | 43.6860 | 25.0456 |
| StY | 1.5164 | 0.0154 | 8.8669 | 84.0137 |
| Stem.D | 2.1125 | 1.0133 | 4.3733 | 17.8989 |
Climate data
# Load the climate data
climate_data <- read.csv("data/dados__temp_umi.csv", sep = ";", na = "null", dec = ",")
# Convert the date column to date format
climate_data$Data.Medicao <- dmy(climate_data$Data.Medicao)
# Extract the year and semester from the date
climate_data <- climate_data %>%
mutate(Ano = year(Data.Medicao), Semestre = ifelse(month(Data.Medicao) <= 6, "1-6", "7-12"))
# Calculate the means per semester and year for each variable
semester_means <- climate_data %>%
group_by(Ano, Semestre) %>%
summarise_if(is.numeric, ~mean(., na.rm = TRUE))
# Display the results
semester_means %>%
kbl(escape = F, align = 'c') %>%
kable_classic("hover", full_width = F, position = "center", fixed_thead = T)| Ano | Semestre | EVAPORACAO.DO.PICHE..DIARIA.mm. | INSOLACAO.TOTAL..DIARIO.h. | PRECIPITACAO.TOTAL..DIARIO.mm. | TEMPERATURA.MAXIMA..DIARIA..C. | TEMPERATURA.MEDIA.COMPENSADA..DIARIA..C. | TEMPERATURA.MINIMA..DIARIA..C. | UMIDADE.RELATIVA.DO.AR..MEDIA.DIARIA… | UMIDADE.RELATIVA.DO.AR..MINIMA.DIARIA… | VENTO..VELOCIDADE.MEDIA.DIARIA.m.s. |
|---|---|---|---|---|---|---|---|---|---|---|
| 2016 | 1-6 | 10.607182 | 8.453297 | 1.7829670 | 32.94780 | 27.85220 | 23.30440 | 65.63571 | 55.33516 | 2.703846 |
| 2016 | 7-12 | 13.772826 | 9.549456 | 0.1625000 | 33.72609 | 28.05217 | 22.64511 | 58.46141 | 47.90217 | 3.155978 |
| 2017 | 1-6 | 12.409945 | 8.513260 | 0.5801105 | 33.51271 | 28.48122 | 23.77845 | 60.43833 | 50.20994 | 2.971271 |
| 2017 | 7-12 | 13.032065 | 8.686957 | 0.2304348 | 32.30109 | 26.99239 | 22.08533 | 55.83770 | 44.88587 | 3.279891 |
| 2018 | 1-6 | 9.982873 | 7.917680 | 1.4602210 | 32.39779 | 27.39116 | 23.11713 | 62.37821 | 50.77348 | 2.782320 |
| 2018 | 7-12 | 12.984239 | 9.203261 | 0.3222826 | 33.13152 | 27.72283 | 22.60054 | 54.03859 | 43.55978 | 3.023913 |
| 2019 | 1-6 | 10.839326 | 8.501695 | 1.1387640 | 33.57175 | 28.31243 | 23.88596 | 61.06723 | 48.66298 | 2.758989 |
| 2019 | 7-12 | 13.895652 | 9.229891 | 0.1972826 | 33.72391 | 27.91413 | 22.59565 | 53.56667 | 43.01087 | 3.244565 |
| 2020 | 1-6 | 8.186517 | 6.869101 | 1.9252809 | 32.20337 | 27.24157 | 23.48090 | 70.01461 | 59.37234 | 2.555056 |
| 2020 | 7-12 | 12.382065 | 8.848913 | 0.9619565 | 32.79946 | 27.07017 | 21.95217 | 59.94088 | 47.12500 | 3.020109 |
Then, now we go to execute the mixed models analisys script: mixed_models.Rmd
R version 4.3.3 (2024-02-29 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=Portuguese_Brazil.utf8 LC_CTYPE=Portuguese_Brazil.utf8
[3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C
[5] LC_TIME=Portuguese_Brazil.utf8
time zone: America/Sao_Paulo
tzcode source: internal
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] GGally_2.2.1 ggthemes_5.1.0 DataExplorer_0.8.3
[4] metan_1.19.0 readxl_1.4.3 data.table_1.16.4
[7] ComplexHeatmap_2.16.0 lubridate_1.9.4 forcats_1.0.0
[10] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
[13] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[16] ggplot2_3.5.1 tidyverse_2.0.0 kableExtra_1.4.0
loaded via a namespace (and not attached):
[1] Rdpack_2.6.2 gridExtra_2.3 writexl_1.5.1
[4] rlang_1.1.4 magrittr_2.0.3 clue_0.3-66
[7] GetoptLong_1.0.5 git2r_0.35.0 matrixStats_1.5.0
[10] compiler_4.3.3 png_0.1-8 systemfonts_1.1.0
[13] vctrs_0.6.5 pkgconfig_2.0.3 shape_1.4.6.1
[16] crayon_1.5.3 fastmap_1.2.0 labeling_0.4.3
[19] promises_1.3.2 rmarkdown_2.29 tzdb_0.4.0
[22] nloptr_2.1.1 xfun_0.50 cachem_1.1.0
[25] jsonlite_1.8.9 later_1.4.1 tweenr_2.0.3
[28] parallel_4.3.3 cluster_2.1.8 R6_2.5.1
[31] bslib_0.8.0 stringi_1.8.4 RColorBrewer_1.1-3
[34] boot_1.3-31 numDeriv_2016.8-1.1 jquerylib_0.1.4
[37] cellranger_1.1.0 Rcpp_1.0.14 iterators_1.0.14
[40] knitr_1.49 IRanges_2.34.1 igraph_2.1.3
[43] httpuv_1.6.15 Matrix_1.6-1 splines_4.3.3
[46] timechange_0.3.0 tidyselect_1.2.1 rstudioapi_0.17.1
[49] yaml_2.3.10 doParallel_1.0.17 codetools_0.2-20
[52] lmerTest_3.1-3 lattice_0.22-6 plyr_1.8.9
[55] withr_3.0.2 evaluate_1.0.3 ggstats_0.8.0
[58] polyclip_1.10-7 xml2_1.3.6 circlize_0.4.16
[61] pillar_1.10.1 whisker_0.4.1 foreach_1.5.2
[64] stats4_4.3.3 reformulas_0.4.0 generics_0.1.3
[67] rprojroot_2.0.4 mathjaxr_1.6-0 S4Vectors_0.38.1
[70] hms_1.1.3 munsell_0.5.1 scales_1.3.0
[73] minqa_1.2.8 glue_1.8.0 tools_4.3.3
[76] lme4_1.1-36 fs_1.6.5 rbibutils_2.3
[79] colorspace_2.1-1 networkD3_0.4 nlme_3.1-166
[82] patchwork_1.3.0 ggforce_0.4.2 cli_3.6.3
[85] workflowr_1.7.1 viridisLite_0.4.2 svglite_2.1.3
[88] gtable_0.3.6 sass_0.4.9 digest_0.6.37
[91] BiocGenerics_0.46.0 ggrepel_0.9.6 htmlwidgets_1.6.4
[94] rjson_0.2.23 farver_2.1.2 htmltools_0.5.8.1
[97] lifecycle_1.0.4 GlobalOptions_0.1.2 MASS_7.3-60.0.1