Analysis_KatiaAvina
Loranda Calderon
2024-09-19
#install.packages("conflicted")
#install.packages("dplyr")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 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ 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 errorslibrary(gtsummary)
library(gt)
library(readxl)
library(conflicted)
library(dplyr)setwd(dir = "/Users/lorandacalderonzamora/Downloads/")
data <- read_excel("Reorganized_BASE_miRNAs.xlsx")
class(data)## [1] "tbl_df" "tbl" "data.frame"names(data)## [1] "Patient_ID" "miRNA_Group_4"
## [3] "miRNA_Group_2" "DAI"
## [5] "Leptin_ng_ml" "Adiponectin"
## [7] "Adiponectin_Leptin_Index" "Sex"
## [9] "IR" "Age"
## [11] "Hypertension" "Dyslipidemia"
## [13] "Liver_Disease" "Obesity"
## [15] "Ghrelin" "AIC_BI"
## [17] "AEC_BI" "ACT_BI"
## [19] "MLG_BI" "AEC_ACT_BI"
## [21] "AEC_ACT_Torso_BI" "Cell_Mass_BI"
## [23] "Bone_Minerals_BI" "AGV_BI"
## [25] "Basal_Metabolic_Rate_BI" "AF50khz_BD_BI"
## [27] "DII" "Total_Cholesterol"
## [29] "Triglycerides" "LDL_C"
## [31] "HDL_C" "HDL2"
## [33] "HDL3" "HDL2B"
## [35] "HDL2A" "HDL3A"
## [37] "HDL3B" "HDL3C"
## [39] "Apo_B" "Apo_A"
## [41] "Glucose" "Insulin_2"
## [43] "HOMA_IR" "C_Peptide"
## [45] "GIP" "GLP_1"
## [47] "Glucagon" "ALT"
## [49] "AST" "ALP"
## [51] "GGT" "Creatinine"
## [53] "Uric_Acid" "CRP"
## [55] "IL_1B" "IL_6"
## [57] "IL_10" "TNF_a"
## [59] "MCP_1" "MCP_1_ng_ml"
## [61] "PAI_1" "PAI_1_ng_ml"
## [63] "Resistin" "Resistin_ng_ml"
## [65] "Vaspin" "Alcohol"
## [67] "Smoking" "Sedentarism"
## [69] "BMI" "WHR"
## [71] "Weight" "Height"
## [73] "Arm_Circumference_BI" "Fat_BI"
## [75] "Lean_Mass_BI" "Muscle_Mass_BI"
## [77] "Fat_Percentage_BI" "Lean_Mass_Percentage_BI"
## [79] "Muscle_Mass_Percentage_BI" "hsa-miR-370-3p22"
## [81] "hsa-miR-376c-3p20" "hsa-miR-769-5p86"
## [83] "hsa-miR-3913-5p87" "hsa-miR-338-5p01"
## [85] "hsa-miR-203a-3p64" "hsa-miR-16-5p69"
## [87] "hsa-miR-15a-5p68" "hsa-let-7a-5p62"
## [89] "hsa-miR-451047"data <- as.data.frame(data)
data <- data %>%
mutate(
Insulin_resistance = dplyr::recode(miRNA_Group_2, `0` = "Insulin sensitivity", `1` = "Insulin resistance"),
Sex = dplyr::recode(Sex, `0` = "Male", `1` = "Female"),
Hypertension = dplyr::recode(Hypertension, `0` = "No", `1` = "Yes"),
Dyslipidemia = dplyr::recode(Dyslipidemia, `0` = "No", `1` = "Yes"),
Liver_Disease = dplyr::recode(Liver_Disease, `0` = "No", `1` = "Yes"),
Obesity = dplyr::recode(Obesity, `0` = "No", `1` = "Yes"),
Smoking = dplyr::recode(Smoking, 'no' = "Non-smoker", 'yes' = "Smoker"),
Alcohol = dplyr::recode(Alcohol, 'no' = "Non-drinker", 'yes' = "Drinker"),
Sedentarism = dplyr::recode(Sedentarism, `0` = "Active", `1` = "Sedentary")
)str(data)## 'data.frame': 19 obs. of 90 variables:
## $ Patient_ID : chr "137MP" "139MP" "140MP" "142MP" ...
## $ miRNA_Group_4 : num 0 0 0 1 2 3 1 3 2 1 ...
## $ miRNA_Group_2 : num 0 0 0 1 0 1 1 1 0 1 ...
## $ DAI : num 0.88 2.51 0.52 0.8 0.82 2.47 1.52 1.31 1.77 1.12 ...
## $ Leptin_ng_ml : num 1.959 1.025 0.793 4.262 1.143 ...
## $ Adiponectin : num 10.07 5.48 22.84 8.55 18.58 ...
## $ Adiponectin_Leptin_Index : num 5.14 5.35 28.79 2.01 16.26 ...
## $ Sex : chr "Male" "Male" "Male" "Male" ...
## $ IR : num 0 0 0 1 0 1 1 1 0 1 ...
## $ Age : num 61 75 74 59 65 70 66 73 60 55 ...
## $ Hypertension : chr "No" "No" "Yes" "No" ...
## $ Dyslipidemia : chr "No" "Yes" "No" "No" ...
## $ Liver_Disease : chr "No" "No" "No" "No" ...
## $ Obesity : chr "No" "No" "No" "No" ...
## $ Ghrelin : num 185 227 233 274 224 ...
## $ AIC_BI : chr "18.7" "18.600000000000001" "18.399999999999999" "14.2" ...
## $ AEC_BI : chr "12" "11.8" "12.4" "8.6999999999999993" ...
## $ ACT_BI : chr "30.7" "30.4" "30.8" "22.9" ...
## $ MLG_BI : chr "41.7" "41.5" "42" "31.3" ...
## $ AEC_ACT_BI : chr "0.39" "0.38900000000000001" "0.40200000000000002" "0.38" ...
## $ AEC_ACT_Torso_BI : chr "0.39" "0.39" "0.40200000000000002" "0.38100000000000001" ...
## $ Cell_Mass_BI : chr "26.8" "26.6" "26.4" "20.3" ...
## $ Bone_Minerals_BI : chr "2.4" "2.58" "2.63" "1.91" ...
## $ AGV_BI : chr "139.69999999999999" "121.1" "129.80000000000001" "110.4" ...
## $ Basal_Metabolic_Rate_BI : chr "1271" "1267" "1276" "1047" ...
## $ AF50khz_BD_BI : chr "5.0999999999999996" "4.8" "3.9" "4.7" ...
## $ DII : chr "-4.0599999999999996" "-1.87" "-3.06" "NA" ...
## $ Total_Cholesterol : num 217 201 197 198 166 ...
## $ Triglycerides : num 92.5 166.6 96 90 98.8 ...
## $ LDL_C : num 149.7 138 101.7 129.7 89.2 ...
## $ HDL_C : num 52.6 36 80.2 53.4 60.6 38 39.1 33.6 34.5 32.2 ...
## $ HDL2 : chr "21.36" "17.77" "NA" "NA" ...
## $ HDL3 : chr "78.64" "82.23" "NA" "NA" ...
## $ HDL2B : chr "5.54" "5.88" "NA" "NA" ...
## $ HDL2A : chr "15.82" "11.9" "NA" "NA" ...
## $ HDL3A : chr "21.9" "22.55" "NA" "NA" ...
## $ HDL3B : chr "23.62" "27.35" "NA" "NA" ...
## $ HDL3C : chr "33.11" "32.32" "NA" "NA" ...
## $ Apo_B : num 120.9 126.8 89.1 110.3 81.3 ...
## $ Apo_A : num 155 136 190 159 172 ...
## $ Glucose : num 82 99.5 89.9 103.4 92.6 ...
## $ Insulin_2 : num 7.45 7.22 3.93 8.28 6.59 ...
## $ HOMA_IR : num 1.508 1.775 0.872 2.113 1.507 ...
## $ C_Peptide : num 2013 2049 1748 2456 2045 ...
## $ GIP : num 47.3 91.4 119.2 1433.4 123.9 ...
## $ GLP_1 : num 442 410 376 312 348 ...
## $ Glucagon : num 2901 3635 2962 2954 2715 ...
## $ ALT : num 22.7 23.8 14.8 27.1 11.2 30.6 55.7 10.5 29 20.1 ...
## $ AST : num 21 27 22.1 24.8 16 20.3 38.4 18.5 20.9 17.4 ...
## $ ALP : num 116.6 98.1 84 131.8 69.1 ...
## $ GGT : num 47.1 12.2 15.3 25.3 21.4 27.2 66.3 20.2 20.5 26.1 ...
## $ Creatinine : num 0.6 0.7 0.8 0.6 0.9 0.6 0.9 0.5 0.6 0.7 ...
## $ Uric_Acid : num 4.1 6 4 3.4 5.4 5 7.4 7 6 4.9 ...
## $ CRP : num 0.77 1.55 2.04 2.11 0.56 6.06 0.32 6.36 0.96 7.25 ...
## $ IL_1B : num 1.61 1.77 1.43 1.49 1.04 1.07 0.59 1.91 1.74 1.66 ...
## $ IL_6 : num 3.46 5.61 2.41 3.95 2.57 ...
## $ IL_10 : num 3.42 4.92 4.3 3.81 3.47 2.03 2.46 4.6 7.04 4.4 ...
## $ TNF_a : num 157122 170708 243543 159033 70673 ...
## $ MCP_1 : num 14 11.9 13.1 14.2 17.1 ...
## $ MCP_1_ng_ml : num 0.014 0.0119 0.0131 0.0142 0.0171 ...
## $ PAI_1 : num 5036 7558 4570 4674 5689 ...
## $ PAI_1_ng_ml : num 5.04 7.56 4.57 4.67 5.69 ...
## $ Resistin : num 4036 3162 8519 2553 5772 ...
## $ Resistin_ng_ml : num 4.04 3.16 8.52 2.55 5.77 ...
## $ Vaspin : num 6830 9594 6454 27712 6085 ...
## $ Alcohol : chr "si" "si" "si" "si" ...
## $ Smoking : chr "Non-smoker" "Non-smoker" "Non-smoker" "Non-smoker" ...
## $ Sedentarism : chr "Active" "Sedentary" "Sedentary" "Active" ...
## $ BMI : num 24.8 23.4 23.6 24.5 25.8 ...
## $ WHR : num 87.5 91 73 81.8 90 ...
## $ Weight : num 67.2 63.5 64.9 51.4 65.3 79.2 66.5 62 67.5 55 ...
## $ Height : num 165 165 166 145 159 ...
## $ Arm_Circumference_BI : chr "30.9" "30.4" "29" "29.3" ...
## $ Fat_BI : chr "25.5" "22" "22.9" "20.100000000000001" ...
## $ Lean_Mass_BI : chr "39.299999999999997" "38.9" "39.4" "29.4" ...
## $ Muscle_Mass_BI : chr "22.4" "22.2" "22.1" "16.5" ...
## $ Fat_Percentage_BI : chr "37.9" "34.6" "35.4" "39" ...
## $ Lean_Mass_Percentage_BI : chr "58.482142857142797" "61.259842519685002" "60.708782742681002" "57.198443579766497" ...
## $ Muscle_Mass_Percentage_BI: chr "33.3333333333333" "34.960629921259802" "34.052388289676401" "32.101167315175097" ...
## $ hsa-miR-370-3p22 : num 12.96 15.8 1.37 13.56 8.53 ...
## $ hsa-miR-376c-3p20 : num 10.49 4.31 6.83 9.69 48.35 ...
## $ hsa-miR-769-5p86 : num 1093 1107 760 2254 930 ...
## $ hsa-miR-3913-5p87 : num 14.82 23.7 23.68 34.88 5.69 ...
## $ hsa-miR-338-5p01 : num 6.79 3.59 2.73 7.75 11.38 ...
## $ hsa-miR-203a-3p64 : num 16.1 207.5 27.3 73.6 11.4 ...
## $ hsa-miR-16-5p69 : num 7572 6214 10803 5459 17800 ...
## $ hsa-miR-15a-5p68 : num 1162 995 1144 690 1996 ...
## $ hsa-let-7a-5p62 : num 45143 70585 34691 44336 49640 ...
## $ hsa-miR-451047 : num 2.47 3.59 1.37 1.94 11.38 ...
## $ Insulin_resistance : chr "Insulin sensitivity" "Insulin sensitivity" "Insulin sensitivity" "Insulin resistance" ...#Convertir variables para que sean leidas correctamente
data <- data %>%
mutate(
Glucose = as.numeric(as.character(Glucose)),
Insulin_2 = as.numeric(as.character(Insulin_2)),
HOMA_IR = as.numeric(as.character(HOMA_IR)),
Glucagon = as.numeric(as.character(Glucagon)),
GIP = as.numeric(as.character(GIP)),
GLP_1 = as.numeric(as.character(GLP_1)),
C_Peptide = as.numeric(as.character(C_Peptide)),
Total_Cholesterol = as.numeric(as.character(Total_Cholesterol)),
Triglycerides = as.numeric(as.character(Triglycerides)),
HDL_C = as.numeric(as.character(HDL_C)),
LDL_C = as.numeric(as.character(LDL_C)),
Apo_A = as.numeric(as.character(Apo_A)),
Apo_B = as.numeric(as.character(Apo_B)),
Creatinine = as.numeric(as.character(Creatinine)),
Uric_Acid = as.numeric(as.character(Uric_Acid)),
ALT = as.numeric(as.character(ALT)),
AST = as.numeric(as.character(AST)),
GGT = as.numeric(as.character(GGT)),
ALP = as.numeric(as.character(ALP)),
Age = as.numeric(Age),
BMI = as.numeric(BMI),
Resistin = as.numeric(Resistin_ng_ml),
`hsa-miR-370-3p22` = as.numeric(`hsa-miR-370-3p22`),
`hsa-miR-376c-3p20` = as.numeric(`hsa-miR-376c-3p20`),
`hsa-miR-769-5p86` = as.numeric(`hsa-miR-769-5p86`),
`hsa-miR-3913-5p87` = as.numeric(`hsa-miR-3913-5p87`),
`hsa-miR-338-5p01` = as.numeric(`hsa-miR-338-5p01`),
`hsa-miR-203a-3p64` = as.numeric(`hsa-miR-203a-3p64`),
`hsa-miR-16-5p69` = as.numeric(`hsa-miR-16-5p69`),
`hsa-miR-15a-5p68` = as.numeric(`hsa-miR-15a-5p68`),
`hsa-let-7a-5p62` = as.numeric(`hsa-let-7a-5p62`),
`hsa-miR-451047` = as.numeric(`hsa-miR-451047`)
)# Seleccionar solo las variables relevantes para el análisis
selected_data <- data %>%
select(
Insulin_resistance, Age, Sex, Smoking, Alcohol, Sedentarism, BMI,
Hypertension, Dyslipidemia, Obesity
)
# Crear la tabla de resumen
summary_table <- selected_data %>%
tbl_summary(
by = Insulin_resistance,
missing = "no",
type = list(
Age ~ "continuous",
Sex ~ "categorical",
Smoking ~ "categorical",
Alcohol ~ "categorical",
Sedentarism ~ "categorical",
Hypertension ~ "categorical",
Dyslipidemia ~ "categorical",
Obesity ~ "categorical",
BMI ~ "continuous"
),
statistic = list(
Age ~ "{mean} ({sd})",
Sex ~ "{n} / {N} ({p}%)",
Smoking ~ "{n} / {N} ({p}%)",
Alcohol ~ "{n} / {N} ({p}%)",
Sedentarism ~ "{n} / {N} ({p}%)",
Hypertension ~ "{n} / {N} ({p}%)",
Dyslipidemia ~ "{n} / {N} ({p}%)",
Obesity ~ "{n} / {N} ({p}%)",
BMI ~ "{mean} ({sd})"
)
) %>%
add_overall() %>%
add_p() %>%
modify_header(label = "**Variable**") %>%
modify_caption("**Table: Sociodemographic and lifestyle characteristics by insulin resistance status**") %>%
as_gt() %>%
gt::tab_style(
style = gt::cell_text(weight = "bold"),
locations = gt::cells_column_labels()
)## The following warnings were returned during `as_gt()`:
## ! For variable `Age` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## p-value with ties
## ! For variable `Age` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## confidence intervals with tiessummary_table| Variable | Overall N = 191 |
Insulin resistance N = 101 |
Insulin sensitivity N = 91 |
p-value2 |
|---|---|---|---|---|
| Age | 59 (10) | 58 (9) | 60 (10) | 0.6 |
| Sex | >0.9 | |||
| Â Â Â Â Female | 5 / 19 (26%) | 3 / 10 (30%) | 2 / 9 (22%) | |
| Â Â Â Â Male | 14 / 19 (74%) | 7 / 10 (70%) | 7 / 9 (78%) | |
| Smoking | 0.5 | |||
| Â Â Â Â Non-smoker | 17 / 19 (89%) | 8 / 10 (80%) | 9 / 9 (100%) | |
| Â Â Â Â si | 2 / 19 (11%) | 2 / 10 (20%) | 0 / 9 (0%) | |
| Alcohol | 0.6 | |||
| Â Â Â Â Non-drinker | 3 / 19 (16%) | 1 / 10 (10%) | 2 / 9 (22%) | |
| Â Â Â Â si | 16 / 19 (84%) | 9 / 10 (90%) | 7 / 9 (78%) | |
| Sedentarism | >0.9 | |||
| Â Â Â Â Active | 9 / 19 (47%) | 5 / 10 (50%) | 4 / 9 (44%) | |
| Â Â Â Â Sedentary | 10 / 19 (53%) | 5 / 10 (50%) | 5 / 9 (56%) | |
| BMI | 26.9 (3.9) | 28.6 (4.4) | 25.1 (2.4) | 0.079 |
| Hypertension | >0.9 | |||
| Â Â Â Â No | 16 / 19 (84%) | 8 / 10 (80%) | 8 / 9 (89%) | |
| Â Â Â Â Yes | 3 / 19 (16%) | 2 / 10 (20%) | 1 / 9 (11%) | |
| Dyslipidemia | 0.4 | |||
| Â Â Â Â No | 10 / 19 (53%) | 4 / 10 (40%) | 6 / 9 (67%) | |
| Â Â Â Â Yes | 9 / 19 (47%) | 6 / 10 (60%) | 3 / 9 (33%) | |
| Obesity | 0.087 | |||
| Â Â Â Â No | 15 / 19 (79%) | 6 / 10 (60%) | 9 / 9 (100%) | |
| Â Â Â Â Yes | 4 / 19 (21%) | 4 / 10 (40%) | 0 / 9 (0%) | |
| 1 Mean (SD); n / N (%) | ||||
| 2 Wilcoxon rank sum test; Fisher’s exact test; Wilcoxon rank sum exact test | ||||
selected_data <- data %>%
select(
Insulin_resistance, Glucose, Insulin_2, HOMA_IR, Glucagon, GIP, GLP_1, C_Peptide, Total_Cholesterol, Triglycerides, HDL_C, LDL_C, Apo_A, Apo_B, Creatinine, Uric_Acid, ALT, AST, GGT, ALP, Leptin_ng_ml, Adiponectin, Adiponectin_Leptin_Index, Ghrelin
)
summary_table <- selected_data %>%
tbl_summary(
by = Insulin_resistance,
missing = "no",
type = list(
Glucose ~ "continuous",
Insulin_2 ~ "continuous",
HOMA_IR ~ "continuous",
Glucagon ~ "continuous",
GIP ~ "continuous",
GLP_1 ~ "continuous",
C_Peptide ~ "continuous",
Total_Cholesterol ~ "continuous",
Triglycerides ~ "continuous",
HDL_C ~ "continuous",
LDL_C ~ "continuous",
Apo_A ~ "continuous",
Apo_B ~ "continuous",
Leptin_ng_ml ~ "continuous",
Adiponectin ~ "continuous",
Adiponectin_Leptin_Index ~ "continuous",
Ghrelin ~ "continuous",
Creatinine ~ "continuous",
Uric_Acid ~ "continuous",
ALT ~ "continuous",
AST ~ "continuous",
GGT ~ "continuous",
ALP ~ "continuous"
),
statistic = list(
Glucose ~ "{mean} ({sd})",
Insulin_2 ~ "{mean} ({sd})",
HOMA_IR ~ "{mean} ({sd})",
Glucagon ~ "{mean} ({sd})",
GIP ~ "{mean} ({sd})",
GLP_1 ~ "{mean} ({sd})",
C_Peptide ~ "{mean} ({sd})",
Total_Cholesterol ~ "{mean} ({sd})",
Triglycerides ~ "{mean} ({sd})",
HDL_C ~ "{mean} ({sd})",
LDL_C ~ "{mean} ({sd})",
Apo_A ~ "{mean} ({sd})",
Apo_B ~ "{mean} ({sd})",
Leptin_ng_ml ~ "{mean} ({sd})",
Adiponectin ~ "{mean} ({sd})",
Adiponectin_Leptin_Index ~ "{mean} ({sd})",
Ghrelin ~ "{mean} ({sd})",
Creatinine ~ "{mean} ({sd})",
Uric_Acid ~ "{mean} ({sd})",
ALT ~ "{mean} ({sd})",
AST ~ "{mean} ({sd})",
GGT ~ "{mean} ({sd})",
ALP ~ "{mean} ({sd})"
)
) %>%
add_overall() %>%
add_p() %>%
modify_header(label = "**Variable**") %>%
modify_caption("**Table: Clinical characteristics by insulin resistance status**") %>%
as_gt() %>%
gt::tab_style(
style = gt::cell_text(weight = "bold"),
locations = gt::cells_column_labels()
)## The following warnings were returned during `as_gt()`:
## ! For variable `ALP` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## p-value with ties
## ! For variable `ALP` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## confidence intervals with ties
## ! For variable `Creatinine` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## p-value with ties
## ! For variable `Creatinine` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## confidence intervals with ties
## ! For variable `GLP_1` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## p-value with ties
## ! For variable `GLP_1` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## confidence intervals with ties
## ! For variable `Uric_Acid` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## p-value with ties
## ! For variable `Uric_Acid` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## confidence intervals with tiessummary_table| Variable | Overall N = 191 |
Insulin resistance N = 101 |
Insulin sensitivity N = 91 |
p-value2 |
|---|---|---|---|---|
| Glucose | 100 (9) | 103 (9) | 95 (8) | 0.11 |
| Insulin_2 | 10.4 (6.3) | 13.9 (6.9) | 6.5 (1.5) | <0.001 |
| HOMA_IR | 2.59 (1.72) | 3.56 (1.91) | 1.52 (0.35) | <0.001 |
| Glucagon | 3,054 (415) | 3,128 (220) | 2,972 (565) | 0.4 |
| GIP | 218 (326) | 259 (416) | 173 (201) | 0.5 |
| GLP_1 | 374 (77) | 391 (87) | 356 (64) | 0.6 |
| C_Peptide | 2,108 (245) | 2,289 (163) | 1,907 (135) | <0.001 |
| Total_Cholesterol | 183 (42) | 189 (53) | 177 (27) | 0.7 |
| Triglycerides | 159 (80) | 188 (93) | 126 (49) | 0.11 |
| HDL_C | 44 (13) | 40 (9) | 48 (16) | 0.2 |
| LDL_C | 114 (35) | 119 (42) | 108 (26) | 0.6 |
| Apo_A | 146 (26) | 149 (26) | 143 (27) | 0.7 |
| Apo_B | 112 (29) | 119 (35) | 104 (19) | 0.3 |
| Creatinine | 0.77 (0.20) | 0.76 (0.17) | 0.79 (0.24) | >0.9 |
| Uric_Acid | 5.51 (1.41) | 5.64 (1.31) | 5.36 (1.59) | 0.8 |
| ALT | 28 (13) | 31 (15) | 23 (10) | 0.3 |
| AST | 24 (8) | 25 (10) | 23 (4) | >0.9 |
| GGT | 35 (17) | 39 (20) | 31 (14) | 0.5 |
| ALP | 95 (17) | 97 (19) | 93 (15) | 0.7 |
| Leptin_ng_ml | 2.51 (1.66) | 3.66 (1.44) | 1.23 (0.61) | <0.001 |
| Adiponectin | 9.7 (4.5) | 8.0 (2.1) | 11.5 (5.7) | 0.2 |
| Adiponectin_Leptin_Index | 7 (9) | 2 (1) | 13 (10) | <0.001 |
| Ghrelin | 277 (169) | 258 (88) | 297 (234) | >0.9 |
| 1 Mean (SD) | ||||
| 2 Wilcoxon rank sum exact test; Wilcoxon rank sum test | ||||
selected_data <- data %>%
select(
Insulin_resistance, CRP, IL_1B, IL_6, IL_10, TNF_a, PAI_1, MCP_1, Resistin, Vaspin
)
summary_table <- selected_data %>%
tbl_summary(
by = Insulin_resistance,
missing = "no",
type = list(
CRP ~ "continuous",
IL_1B ~ "continuous",
IL_6 ~ "continuous",
IL_10 ~ "continuous",
TNF_a ~ "continuous",
PAI_1 ~ "continuous",
MCP_1 ~ "continuous",
Resistin ~ "continuous",
Vaspin ~ "continuous"
),
statistic = list(
CRP ~ "{mean} ({sd})",
IL_1B ~ "{mean} ({sd})",
IL_6 ~ "{mean} ({sd})",
IL_10 ~ "{mean} ({sd})",
TNF_a ~ "{mean} ({sd})",
PAI_1 ~ "{mean} ({sd})",
MCP_1 ~ "{mean} ({sd})",
Resistin ~ "{mean} ({sd})",
Vaspin ~ "{mean} ({sd})"
)
) %>%
add_overall() %>%
add_p() %>%
modify_header(label = "**Variable**") %>%
modify_caption("**Table: Inflammatory markers and immune function characteristics by insulin resistance status**") %>%
as_gt() %>%
gt::tab_style(
style = gt::cell_text(weight = "bold"),
locations = gt::cells_column_labels()
)## The following warnings were returned during `as_gt()`:
## ! For variable `IL_10` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## p-value with ties
## ! For variable `IL_10` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## confidence intervals with ties
## ! For variable `IL_1B` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## p-value with ties
## ! For variable `IL_1B` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## confidence intervals with ties
## ! For variable `IL_6` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## p-value with ties
## ! For variable `IL_6` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## confidence intervals with tiessummary_table| Variable | Overall N = 191 |
Insulin resistance N = 101 |
Insulin sensitivity N = 91 |
p-value2 |
|---|---|---|---|---|
| CRP | 2.90 (2.64) | 3.54 (2.73) | 2.19 (2.49) | 0.2 |
| IL_1B | 1.31 (0.38) | 1.28 (0.42) | 1.33 (0.35) | >0.9 |
| IL_6 | 4.50 (3.09) | 5.08 (3.70) | 3.85 (2.28) | 0.3 |
| IL_10 | 3.61 (1.22) | 3.27 (0.92) | 3.98 (1.45) | 0.3 |
| TNF_a | 126,513 (74,386) | 125,203 (84,729) | 127,968 (66,098) | 0.7 |
| PAI_1 | 7,385 (2,423) | 8,639 (2,590) | 5,991 (1,228) | 0.028 |
| MCP_1 | 15.3 (4.7) | 17.9 (4.3) | 12.4 (3.5) | 0.013 |
| Resistin | 6.3 (4.2) | 6.5 (4.3) | 6.0 (4.4) | 0.8 |
| Vaspin | 8,308 (5,761) | 8,712 (6,770) | 7,860 (4,761) | 0.8 |
| 1 Mean (SD) | ||||
| 2 Wilcoxon rank sum exact test; Wilcoxon rank sum test | ||||
#install.packages(c("ggpubr", "cowplot"))library(ggpubr)
library(cowplot)
library(ggplot2)data <- data %>%
mutate(
Glucose = as.numeric(as.character(Glucose )),
Insulin_2 = as.numeric(as.character(Insulin_2)),
HOMA_IR = as.numeric(as.character(HOMA_IR)),
Glucagon = as.numeric(as.character(Glucagon)),
GIP = as.numeric(as.character(GIP)),
GLP_1 = as.numeric(as.character(GLP_1)),
C_Peptide = as.numeric(as.character(C_Peptide)),
Total_Cholesterol = as.numeric(as.character(Total_Cholesterol)),
Triglycerides = as.numeric(as.character(Triglycerides)),
HDL_C = as.numeric(as.character(HDL_C)),
LDL_C = as.numeric(as.character(LDL_C)),
Apo_A = as.numeric(as.character(Apo_A)),
Apo_B = as.numeric(as.character(Apo_B)),
Creatinine = as.numeric(as.character(Creatinine)),
Uric_Acid = as.numeric(as.character(Uric_Acid)),
ALT = as.numeric(as.character(ALT)),
AST = as.numeric(as.character(AST)),
GGT = as.numeric(as.character(GGT)),
ALP = as.numeric(as.character(ALP)),
Age = as.numeric(Age),
BMI = as.numeric(BMI)
)
custom_colors <- c("Insulin sensitivity" = "#1f77b4", "Insulin resistance" = "#ff7f0e")
theme_custom <- theme_pubr() +
theme(axis.title.x = element_blank(),
axis.text = element_text(size = 10),
legend.position = "none")
# Función para crear gráficos de boxplot para cada variable
create_boxplot <- function(data, variable, var_label, y_label, y_lim) {
ggplot(data, aes(x = Insulin_resistance, y = !!sym(variable), fill = Insulin_resistance)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = 0.2, alpha = 0.5, color = "black") +
stat_compare_means(aes(label = ..p.signif..), method = "t.test", label.x = 1.5, label.y = y_lim - 0.1*y_lim) +
labs(title = var_label, y = y_label, fill = " ") +
scale_x_discrete(labels = c("Insulin sensitivity", "Insulin resistance")) +
scale_fill_manual(values = custom_colors) +
ylim(0, y_lim) + # Ajustar la escala del eje y según la variable
theme_custom
}
# Crear gráficos para cada variable
p1 <- create_boxplot(data, "Glucose", "", "Glucose (mg/dL)", 200)
p2 <- create_boxplot(data, "Insulin_2", "", "Insulin (μU/mL)", 50)
p3 <- create_boxplot(data, "HOMA_IR", "", "HOMA-IR", 15)
p4 <- create_boxplot(data, "Glucagon", "", "Glucagon (pg/mL)", 4000)
p5 <- create_boxplot(data, "GIP", "", "GIP (pg/mL)", 2000)
p6 <- create_boxplot(data, "GLP_1", "", "GLP-1 (pg/mL)", 600)
p7 <- create_boxplot(data, "C_Peptide", "", "C-Peptide (pmol/L)", 3000)
p8 <- create_boxplot(data, "Total_Cholesterol", "", "Total Cholesterol (mg/dL)", 300)
p9 <- create_boxplot(data, "Triglycerides", "", "Triglycerides (mg/dL)", 500)
p10 <- create_boxplot(data, "HDL_C", "", "HDL Cholesterol (mg/dL)", 100)
p11 <- create_boxplot(data, "LDL_C", "", "LDL Cholesterol (mg/dL)", 200)
p12 <- create_boxplot(data, "Apo_A", "", "Apo A (mg/dL)", 200)
p13 <- create_boxplot(data, "Apo_B", "", "Apo B (mg/dL)", 200)
p14 <- create_boxplot(data, "Leptin_ng_ml", "", "Leptin (ng/mL)", 100)
p15 <- create_boxplot(data, "Adiponectin", "", "Adiponectin (μg/mL)", 40)
p16 <- create_boxplot(data, "Adiponectin_Leptin_Index", "", "Adiponectin/Leptin Index", 50)
p17 <- create_boxplot(data, "Ghrelin", "G", "Ghrelin (pg/mL)", 1000)
p18 <- create_boxplot(data, "Creatinine", "", "Creatinine (mg/dL)", 2)
p19 <- create_boxplot(data, "Uric_Acid", "", "Uric Acid (mg/dL)", 10)
p20 <- create_boxplot(data, "ALT", "", "ALT (U/L)", 100)
p21 <- create_boxplot(data, "AST", "", "AST (U/L)", 100)
p22 <- create_boxplot(data, "GGT", "", "GGT (U/L)", 100)
p23 <- create_boxplot(data, "ALP", "", "ALP (U/L)", 150)
p24 <- create_boxplot(data, "CRP", "", "CRP (mg/L)", 10)
p25 <- create_boxplot(data, "IL_1B", "", "IL-1B (pg/mL)", 10)
p26 <- create_boxplot(data, "IL_6", "", "IL-6 (pg/mL)", 10)
p27 <- create_boxplot(data, "IL_10", "", "IL-10 (pg/mL)", 10)
p28 <- create_boxplot(data, "TNF_a", "", "TNF-α (pg/mL)", 500000)
p29 <- create_boxplot(data, "PAI_1", "", "PAI-1 (ng/mL)", 20000)
p30 <- create_boxplot(data, "MCP_1", "", "MCP-1 (pg/mL)", 100)
p31 <- create_boxplot(data, "Resistin", "", "Resistin (ng/mL)", 100)
p32 <- create_boxplot(data, "Vaspin", "", "Vaspin (ng/mL)", 30000)
print(p1)
print(p2)
print(p3)
print(p4)
print(p5)
print(p6)## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
print(p7)
print(p8)
print(p9)
print(p10)
print(p11)
print(p12)
print(p13)
print(p14)
print(p15)
print(p16)
print(p17)
print(p18)
print(p19)
print(p20)
print(p21)
print(p22)
print(p23)
print(p24)
print(p25)
print(p26)## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
print(p27)
print(p28)
print(p29)
print(p30)
print(p31)
print(p32)
custom_colors <- c("Insulin sensitivity" = "#1f77b4", "Insulin resistance" = "#ff7f0e")
theme_custom <- theme_pubr() +
theme(axis.title.x = element_blank(),
axis.text = element_text(size = 10),
legend.position = "none")
# Función para crear gráficos de boxplot para cada variable
create_boxplot <- function(data, variable, var_label, y_label, y_lim) {
ggplot(data, aes(x = Insulin_resistance, y = !!sym(variable), fill = Insulin_resistance)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = 0.2, alpha = 0.5, color = "black") +
stat_compare_means(aes(label = ..p.signif..), method = "t.test", label.x = 1.5, label.y = y_lim - 0.1 * y_lim) +
labs(title = var_label, y = y_label, fill = " ") +
scale_x_discrete(labels = c("Insulin sensitivity", "Insulin resistance")) +
scale_fill_manual(values = custom_colors) +
ylim(0, y_lim) + # Ajustar la escala del eje y según la variable
theme_custom
}
p1 <- create_boxplot(data, "hsa-miR-370-3p22", "", "hsa-miR-370-3p22 (normalized counts)", 500)
p2 <- create_boxplot(data, "hsa-miR-376c-3p20", "", "hsa-miR-376c-3p20 (normalized counts)", 500)
p3 <- create_boxplot(data, "hsa-miR-769-5p86", "", "hsa-miR-769-5p86 (normalized counts)", 15000)
p4 <- create_boxplot(data, "hsa-miR-3913-5p87", "", "hsa-miR-3913-5p87 (normalized counts)", 500)
p5 <- create_boxplot(data, "hsa-miR-338-5p01", "", "hsa-miR-338-5p01 (normalized counts)", 500)
p6 <- create_boxplot(data, "hsa-miR-203a-3p64", "", "hsa-miR-203a-3p64 (normalized counts)", 500)
p7 <- create_boxplot(data, "hsa-miR-16-5p69", "", "hsa-miR-16-5p69 (normalized counts)", 150000)
p8 <- create_boxplot(data, "hsa-miR-15a-5p68", "", "hsa-miR-15a-5p68 (normalized counts)", 15000)
p9 <- create_boxplot(data, "hsa-let-7a-5p62", "", "hsa-let-7a-5p62 (normalized counts)", 600000)
p10 <- create_boxplot(data, "hsa-miR-451047", "", "hsa-miR-451047 (normalized counts)", 100)
print(p1)## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
print(p2)## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
print(p3)## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
print(p4)## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
print(p5)## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
print(p6)## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
print(p7)
print(p8)
print(p9)
print(p10)## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
data <- data %>%
mutate(
Arm_Circumference_BI = as.numeric(as.character(Arm_Circumference_BI)),
Fat_BI = as.numeric(as.character(Fat_BI)),
Lean_Mass_BI = as.numeric(as.character(Lean_Mass_BI)),
Muscle_Mass_BI = as.numeric(as.character(Muscle_Mass_BI)),
Fat_Percentage_BI = as.numeric(as.character(Fat_Percentage_BI)),
Lean_Mass_Percentage_BI = as.numeric(as.character(Lean_Mass_Percentage_BI)),
Muscle_Mass_Percentage_BI = as.numeric(as.character(Muscle_Mass_Percentage_BI))
)## Warning: There were 7 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `Arm_Circumference_BI =
## as.numeric(as.character(Arm_Circumference_BI))`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 6 remaining warnings.selected_data <- data %>%
select(
Insulin_resistance, BMI, Weight, Height, WHR, Arm_Circumference_BI, Fat_BI, Lean_Mass_BI, Muscle_Mass_BI, Fat_Percentage_BI, Lean_Mass_Percentage_BI, Muscle_Mass_Percentage_BI
)
summary_table <- selected_data %>%
tbl_summary(
by = Insulin_resistance,
missing = "no",
type = list(
BMI ~ "continuous",
Weight ~ "continuous",
Height ~ "continuous",
WHR ~ "continuous",
Arm_Circumference_BI ~ "continuous",
Fat_BI ~ "continuous",
Lean_Mass_BI ~ "continuous",
Muscle_Mass_BI ~ "continuous",
Fat_Percentage_BI ~ "continuous",
Lean_Mass_Percentage_BI ~ "continuous",
Muscle_Mass_Percentage_BI ~ "continuous"
),
statistic = list(
BMI ~ "{mean} ({sd})",
Weight ~ "{mean} ({sd})",
Height ~ "{mean} ({sd})",
WHR ~ "{mean} ({sd})",
Arm_Circumference_BI ~ "{mean} ({sd})",
Fat_BI ~ "{mean} ({sd})",
Lean_Mass_BI ~ "{mean} ({sd})",
Muscle_Mass_BI ~ "{mean} ({sd})",
Fat_Percentage_BI ~ "{mean} ({sd})",
Lean_Mass_Percentage_BI ~ "{mean} ({sd})",
Muscle_Mass_Percentage_BI ~ "{mean} ({sd})"
)
) %>%
add_overall() %>%
add_p() %>%
modify_header(label = "**Variable**") %>%
modify_caption("**Table: Anthropometric measurements by insulin resistance status**") %>%
as_gt() %>%
gt::tab_style(
style = gt::cell_text(weight = "bold"),
locations = gt::cells_column_labels()
)## The following warnings were returned during `as_gt()`:
## ! For variable `Arm_Circumference_BI` (`Insulin_resistance`) and "estimate",
## "statistic", "p.value", "conf.low", and "conf.high" statistics: cannot
## compute exact p-value with ties
## ! For variable `Arm_Circumference_BI` (`Insulin_resistance`) and "estimate",
## "statistic", "p.value", "conf.low", and "conf.high" statistics: cannot
## compute exact confidence intervals with ties
## ! For variable `Muscle_Mass_BI` (`Insulin_resistance`) and "estimate",
## "statistic", "p.value", "conf.low", and "conf.high" statistics: cannot
## compute exact p-value with ties
## ! For variable `Muscle_Mass_BI` (`Insulin_resistance`) and "estimate",
## "statistic", "p.value", "conf.low", and "conf.high" statistics: cannot
## compute exact confidence intervals with ties
## ! For variable `WHR` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## p-value with ties
## ! For variable `WHR` (`Insulin_resistance`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: cannot compute exact
## confidence intervals with tiessummary_table| Variable | Overall N = 191 |
Insulin resistance N = 101 |
Insulin sensitivity N = 91 |
p-value2 |
|---|---|---|---|---|
| BMI | 26.9 (3.9) | 28.6 (4.4) | 25.1 (2.4) | 0.079 |
| Weight | 68 (13) | 69 (17) | 67 (5) | >0.9 |
| Height | 159 (11) | 155 (12) | 163 (9) | 0.13 |
| WHR | 91 (10) | 95 (11) | 87 (6) | 0.10 |
| Arm_Circumference_BI | 33.03 (3.25) | 34.81 (3.49) | 31.24 (1.75) | 0.015 |
| Fat_BI | 25.6 (7.1) | 29.0 (7.1) | 22.2 (5.6) | 0.063 |
| Lean_Mass_BI | 42 (11) | 41 (13) | 42 (8) | 0.7 |
| Muscle_Mass_BI | 24.1 (6.7) | 24.1 (8.3) | 24.1 (5.3) | 0.7 |
| Fat_Percentage_BI | 37 (9) | 40 (9) | 34 (9) | 0.2 |
| Lean_Mass_Percentage_BI | 60 (8) | 58 (8) | 62 (8) | 0.5 |
| Muscle_Mass_Percentage_BI | 34.8 (5.4) | 33.6 (5.4) | 36.0 (5.5) | 0.5 |
| 1 Mean (SD) | ||||
| 2 Wilcoxon rank sum exact test; Wilcoxon rank sum test | ||||
data <- data %>%
mutate(
Arm_Circumference_BI = as.numeric(as.character(Arm_Circumference_BI)),
Fat_BI = as.numeric(as.character(Fat_BI)),
Lean_Mass_BI = as.numeric(as.character(Lean_Mass_BI)),
Muscle_Mass_BI = as.numeric(as.character(Muscle_Mass_BI)),
Fat_Percentage_BI = as.numeric(as.character(Fat_Percentage_BI)),
Lean_Mass_Percentage_BI = as.numeric(as.character(Lean_Mass_Percentage_BI)),
Muscle_Mass_Percentage_BI = as.numeric(as.character(Muscle_Mass_Percentage_BI))
)
custom_colors <- c("Insulin sensitivity" = "#1f77b4", "Insulin resistance" = "#ff7f0e")
theme_custom <- theme_pubr() +
theme(axis.title.x = element_blank(),
axis.text = element_text(size = 10),
legend.position = "none")
create_boxplot <- function(data, variable, var_label, y_label, y_lim) {
ggplot(data, aes(x = Insulin_resistance, y = !!sym(variable), fill = Insulin_resistance)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = 0.2, alpha = 0.5, color = "black") +
stat_compare_means(aes(label = ..p.signif..), method = "t.test", label.x = 1.5, label.y = y_lim - 0.1*y_lim) +
labs(title = var_label, y = y_label, fill = " ") +
scale_x_discrete(labels = c("Insulin sensitivity", "Insulin resistance")) +
scale_fill_manual(values = custom_colors) +
ylim(0, y_lim) + # Ajustar la escala del eje y según la variable
theme_custom
}
p1 <- create_boxplot(data, "BMI", "", "BMI (kg/m²)", 50)
p2 <- create_boxplot(data, "Weight", "", "Weight (kg)", 150)
p3 <- create_boxplot(data, "Height", "", "Height (cm)", 200)
p4 <- create_boxplot(data, "WHR", "", "Waist-to-Hip Ratio", 1.5)
p5 <- create_boxplot(data, "Arm_Circumference_BI", "", "Arm Circumference (cm)", 50)
p6 <- create_boxplot(data, "Fat_BI", "", "Fat Mass (kg)", 100)
p7 <- create_boxplot(data, "Lean_Mass_BI", "", "Lean Mass (kg)", 100)
p8 <- create_boxplot(data, "Muscle_Mass_BI", "", "Muscle Mass (kg)", 100)
p9 <- create_boxplot(data, "Fat_Percentage_BI", "", "Fat Percentage (%)", 100)
p10 <- create_boxplot(data, "Lean_Mass_Percentage_BI", "", "Lean Mass Percentage (%)", 100)
p11 <- create_boxplot(data, "Muscle_Mass_Percentage_BI", "", "Muscle Mass Percentage (%)", 100)
print(p1)
print(p2)
print(p3)
print(p4)## Warning: Removed 19 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 19 rows containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Computation failed in `stat_compare_means()`.
## Caused by error:
## ! argument "x" is missing, with no default## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(d[d > tolerance]): no non-missing arguments to min; returning
## Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in stats::runif(length(x), -amount, amount): NAs produced## Warning: Removed 19 rows containing missing values or values outside the scale range
## (`geom_point()`).
print(p5)## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
print(p6)## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
print(p7)## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
print(p8)## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
print(p9)## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
print(p10)## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
print(p11)## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_compare_means()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
#install.packages("factoextra")library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa# PCA para reducir la dimensión de las variables
data <- data %>%
mutate_at(vars(
Glucose, Insulin_2, HOMA_IR, Glucagon, GIP, GLP_1, C_Peptide,
Total_Cholesterol, Triglycerides, HDL_C, LDL_C, Apo_A, Apo_B,
Creatinine, Uric_Acid, ALT, AST, GGT, ALP,
BMI, Weight, Height, WHR, Arm_Circumference_BI, Fat_BI,
Lean_Mass_BI, Muscle_Mass_BI, Fat_Percentage_BI,
Lean_Mass_Percentage_BI, Muscle_Mass_Percentage_BI,
CRP, IL_1B, IL_6, IL_10, TNF_a, PAI_1, MCP_1, Resistin, Vaspin),
as.numeric)
data_clean <- data %>%
drop_na()
clinical_vars <- data_clean %>%
select(Glucose, Insulin_2, HOMA_IR, Glucagon, GIP, GLP_1, C_Peptide,
Total_Cholesterol, Triglycerides, HDL_C, LDL_C, Apo_A, Apo_B,
Creatinine, Uric_Acid, ALT, AST, GGT, ALP)
anthropometric_vars <- data_clean %>%
select(BMI, Weight, Height, WHR, Arm_Circumference_BI, Fat_BI,
Lean_Mass_BI, Muscle_Mass_BI, Fat_Percentage_BI,
Lean_Mass_Percentage_BI, Muscle_Mass_Percentage_BI)
inflammatory_vars <- data_clean %>%
select(CRP, IL_1B, IL_6, IL_10, TNF_a, PAI_1, MCP_1, Resistin, Vaspin)
pca_clinical <- prcomp(clinical_vars, scale. = TRUE)
fviz_pca_var(pca_clinical, col.var = "contrib", repel = TRUE) +
labs(title = "PCA - Clinical Variables")
pca_anthropometric <- prcomp(anthropometric_vars, scale. = TRUE)
fviz_pca_var(pca_anthropometric, col.var = "contrib", repel = TRUE) +
labs(title = "PCA - Anthropometric Variables")
pca_inflammatory <- prcomp(inflammatory_vars, scale. = TRUE)
fviz_pca_var(pca_inflammatory, col.var = "contrib", repel = TRUE) +
labs(title = "PCA - Inflammatory Variables")
# Extraer las contribuciones de las variables en el primer componente
contrib_clinical <- get_pca_var(pca_clinical)$contrib
contrib_anthropometric <- get_pca_var(pca_anthropometric)$contrib
contrib_inflammatory <- get_pca_var(pca_inflammatory)$contrib
# Mostrar las contribuciones más significativas
print("Contribuciones significativas - Variables clÃnicas")## [1] "Contribuciones significativas - Variables clÃnicas"print(contrib_clinical)## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Glucose 0.77669712 14.5456407 3.892845e-04 0.46085941 2.390421e-01
## Insulin_2 12.08644555 1.8069554 9.792895e-01 0.84409050 5.121913e+00
## HOMA_IR 11.46860544 2.7480306 8.563561e-01 0.79565219 5.331350e+00
## Glucagon 0.21989407 2.7739022 1.561723e+01 5.28836048 6.260512e-01
## GIP 0.05366267 0.1120226 9.353045e+00 25.79667919 1.357399e-03
## GLP_1 0.68263678 6.5155558 5.169339e+00 11.23200899 1.217692e+01
## C_Peptide 6.51429709 1.7372950 8.930233e+00 0.19735808 4.832149e+00
## Total_Cholesterol 7.50058943 13.6504187 2.115962e+00 1.28599526 7.916913e-01
## Triglycerides 8.54319132 0.2837430 2.352128e-01 8.30126252 1.148088e+01
## HDL_C 0.95647339 20.7714161 3.849401e-02 0.48111974 1.154606e+01
## LDL_C 8.43026579 7.4588210 4.621821e+00 0.50683336 1.754428e+00
## Apo_A 0.41958483 16.0832097 3.246179e-01 0.07203933 2.277051e+01
## Apo_B 11.39408078 3.8933828 1.117428e+00 1.56086984 5.415562e+00
## Creatinine 1.39821653 0.5116297 2.153387e+01 0.54059569 1.381175e+01
## Uric_Acid 2.09374952 4.1108205 1.350545e+01 0.44180812 5.814114e-01
## ALT 9.96440849 1.5836763 1.864740e+00 6.65664145 4.470664e-02
## AST 7.69485766 0.2046502 4.167069e+00 3.88548310 2.061951e+00
## GGT 9.27444163 0.6845802 1.704437e+00 1.89161686 1.412074e+00
## ALP 0.52790192 0.5242495 7.865016e+00 29.76072588 2.004833e-04
## Dim.6 Dim.7 Dim.8 Dim.9
## Glucose 36.448656340 2.63291657 1.58106903 0.008327375
## Insulin_2 0.005220409 0.34356379 0.23988610 13.742327489
## HOMA_IR 0.349877077 0.81936039 0.16958888 15.127880221
## Glucagon 7.621676595 24.53979899 4.15750697 10.639823042
## GIP 13.947104957 9.18117475 0.00175932 0.451276239
## GLP_1 6.081542828 0.07727763 3.01834025 21.954140762
## C_Peptide 2.283900666 2.96642727 0.74717895 14.866593471
## Total_Cholesterol 0.094682391 0.04157343 0.49013593 1.768490387
## Triglycerides 5.912221081 1.44346357 4.15155663 2.139770669
## HDL_C 0.041654269 5.53986474 0.58130076 1.829189151
## LDL_C 0.325957076 1.31614339 3.86149033 7.962154947
## Apo_A 3.321763855 0.76910624 0.81285563 0.011212960
## Apo_B 0.059710386 0.14954781 3.27074709 0.557596725
## Creatinine 0.768279416 1.56933810 1.69623041 0.005068993
## Uric_Acid 0.371164570 5.07291123 46.00367454 5.971284135
## ALT 3.350598155 1.45930470 7.84555996 2.110510541
## AST 4.704662310 21.69374179 10.09077416 0.029704425
## GGT 11.823642415 13.53325314 1.83081729 0.060223918
## ALP 2.487685204 6.85123247 9.44952778 0.764424549
## Dim.10 Dim.11 Dim.12 Dim.13
## Glucose 2.133081e+01 0.297544684 0.1081837 11.421850145
## Insulin_2 8.393718e-01 2.080012854 2.6493258 1.028689190
## HOMA_IR 6.527779e-04 2.475657055 3.4839458 0.252389091
## Glucagon 1.614339e+00 3.030447181 15.5138113 1.421811054
## GIP 7.408678e-01 16.228928043 4.0978266 15.049278651
## GLP_1 7.834551e+00 2.603600027 3.7630702 15.917291472
## C_Peptide 2.405522e+01 1.273023717 0.1209428 14.421234875
## Total_Cholesterol 3.078153e+00 1.332770295 0.1452860 0.001059504
## Triglycerides 1.934076e+00 1.692670579 0.8094084 19.399561489
## HDL_C 3.668402e+00 10.811108841 9.3068810 9.461471190
## LDL_C 3.983115e+00 0.384632434 1.1945952 0.095574919
## Apo_A 2.235464e+00 29.246420922 0.2164751 3.337296752
## Apo_B 2.455449e+00 0.510668625 2.2858479 1.328138933
## Creatinine 6.007625e+00 0.079485240 15.3973713 3.904261533
## Uric_Acid 2.347609e+00 0.009405086 5.0842973 0.074086275
## ALT 4.120823e+00 6.222654952 5.8057446 0.014110340
## AST 1.857061e+00 0.630746875 13.3553434 0.393286481
## GGT 5.872241e+00 10.492286322 8.1231088 0.861723131
## ALP 6.024174e+00 10.597936267 8.5385351 1.616884978
## Dim.14 Dim.15 Dim.16 Dim.17
## Glucose 0.099764783 7.90682013 0.768170587 3.598897e-02
## Insulin_2 0.164580832 7.94009056 0.243815232 6.775170e+00
## HOMA_IR 0.362378870 4.91994968 2.418861045 5.659492e+00
## Glucagon 0.083129917 6.08711000 0.001517696 6.932416e-01
## GIP 0.508219927 0.76513209 2.946383645 7.121974e-01
## GLP_1 0.004260783 0.68283988 0.590529630 1.447991e+00
## C_Peptide 4.000298522 2.32041761 9.982330677 6.856087e-01
## Total_Cholesterol 0.049674516 0.18035029 0.041343436 9.535719e+00
## Triglycerides 0.102594402 8.22249730 17.790510404 3.032162e+00
## HDL_C 5.872793854 3.96689438 9.405842307 1.923898e-02
## LDL_C 0.690282773 1.73697266 9.342538935 1.085644e+01
## Apo_A 0.157731387 5.60258680 11.447909325 2.948095e+00
## Apo_B 0.073706333 0.01125151 2.327918250 5.515372e+01
## Creatinine 19.081335703 4.46189075 8.434301265 7.986496e-01
## Uric_Acid 7.049694127 6.09435238 1.170374694 8.664746e-04
## ALT 43.836775665 4.20324427 0.762897134 1.501273e-01
## AST 15.471775429 1.19926694 11.447103518 1.088123e+00
## GGT 0.358975590 31.80124521 0.031635754 1.977862e-01
## ALP 2.032026588 1.89708756 10.846016467 2.093788e-01
## Dim.18
## Glucose 1.058604e+00
## Insulin_2 3.410863e+01
## HOMA_IR 3.383529e+01
## Glucagon 5.520914e-02
## GIP 4.266632e-02
## GLP_1 1.966165e-01
## C_Peptide 5.058239e-02
## Total_Cholesterol 2.043512e+01
## Triglycerides 5.295806e-01
## HDL_C 8.841999e-01
## LDL_C 1.916811e+00
## Apo_A 1.768003e-01
## Apo_B 6.632696e+00
## Creatinine 8.501754e-05
## Uric_Acid 1.311730e-02
## ALT 2.719408e-03
## AST 1.924827e-02
## GGT 3.655130e-02
## ALP 5.473681e-03print("Contribuciones significativas - Variables antropométricas")## [1] "Contribuciones significativas - Variables antropométricas"print(contrib_anthropometric)## Dim.1 Dim.2 Dim.3 Dim.4
## BMI 1.70821217 17.54217873 17.167621 21.21104468
## Weight 4.48251378 15.05269400 15.130189 2.74660973
## Height 15.58586404 0.09809484 1.273223 42.57472042
## WHR 0.42616125 18.47026308 16.588642 0.03915646
## Arm_Circumference_BI 0.03005978 20.53372648 12.945179 4.58888290
## Fat_BI 5.25899485 13.89383211 6.070417 17.06632732
## Lean_Mass_BI 13.58742439 4.61673308 1.639327 0.04168809
## Muscle_Mass_BI 13.59123465 4.70765406 1.151761 0.25505532
## Fat_Percentage_BI 15.09357918 2.12864020 4.562062 4.83456549
## Lean_Mass_Percentage_BI 14.63398802 2.00240917 12.347319 2.91541811
## Muscle_Mass_Percentage_BI 15.60196789 0.95377425 11.124260 3.72653150
## Dim.5 Dim.6 Dim.7 Dim.8
## BMI 4.029972e-01 3.050259 31.6955130 5.515348783
## Weight 5.135106e-04 1.663216 11.7518344 3.549662261
## Height 2.892643e-01 25.317902 13.3227782 0.267260979
## WHR 6.209723e+01 1.383918 0.9719584 0.007088808
## Arm_Circumference_BI 9.957818e+00 30.784436 7.9936379 9.875349648
## Fat_BI 1.218039e+01 7.302572 0.3033940 22.712705191
## Lean_Mass_BI 2.138148e+00 13.703353 1.2210888 16.508805493
## Muscle_Mass_BI 1.186410e+00 6.893031 4.9634803 1.612837949
## Fat_Percentage_BI 4.503889e+00 2.920276 4.9079490 10.946252682
## Lean_Mass_Percentage_BI 5.244270e+00 5.658495 21.9197034 0.231813009
## Muscle_Mass_Percentage_BI 1.999075e+00 1.322542 0.9486626 28.772875197
## Dim.9 Dim.10 Dim.11
## BMI 1.416457461 0.273307968 1.706020e-02
## Weight 3.737736745 32.089422801 9.795608e+00
## Height 0.830997882 0.405435408 3.445901e-02
## WHR 0.014305610 0.001138472 1.389283e-04
## Arm_Circumference_BI 3.281265820 0.009472543 1.718035e-04
## Fat_BI 4.185644106 8.152536091 2.873192e+00
## Lean_Mass_BI 0.003208223 1.426553874 4.511367e+01
## Muscle_Mass_BI 16.519473817 33.432721884 1.568634e+01
## Fat_Percentage_BI 29.570450278 15.330509218 5.201827e+00
## Lean_Mass_Percentage_BI 16.319801911 1.493672336 1.723311e+01
## Muscle_Mass_Percentage_BI 24.120658146 7.385229404 4.044424e+00print("Contribuciones significativas - Variables inflamatorias")## [1] "Contribuciones significativas - Variables inflamatorias"print(contrib_inflammatory)## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6
## CRP 2.65167546 25.13535550 0.48529426 0.002278190 32.522351 18.8835121
## IL_1B 27.54889479 0.01544771 0.08381446 0.985713923 3.471677 1.9132063
## IL_6 16.48252490 15.31897015 0.11359764 1.371064035 5.486749 1.1351918
## IL_10 17.04476240 10.61073249 0.06224741 0.036215805 20.198296 6.6021704
## TNF_a 15.79533174 1.09974599 16.47705269 17.373881843 10.011527 0.4973376
## PAI_1 13.95367223 3.44323637 12.26275926 11.100678148 8.877874 36.3790355
## MCP_1 0.09289895 14.06715437 51.43313901 0.003169943 2.692519 20.2098685
## Resistin 1.60200721 22.76337815 17.21108056 1.828561517 3.934144 11.1576891
## Vaspin 4.82823232 7.54597927 1.87101473 67.298436595 12.804864 3.2219887
## Dim.7 Dim.8 Dim.9
## CRP 2.90846536 3.19468191 14.2163863
## IL_1B 0.06599334 59.21698262 6.6982700
## IL_6 10.75212563 2.02905973 47.3107168
## IL_10 0.01914710 31.73767142 13.6887572
## TNF_a 31.20601398 0.08596509 7.4531441
## PAI_1 12.83812639 0.67808273 0.4665351
## MCP_1 3.37461285 0.23078673 7.8958511
## Resistin 38.05271340 1.56789284 1.8825335
## Vaspin 0.78280194 1.25887693 0.3878060library(ggplot2)
library(corrplot)## corrplot 0.94 loadedlibrary(reshape2)selected_vars <- data_clean %>%
select(Insulin_2, HOMA_IR, Total_Cholesterol, Triglycerides, Apo_B, # ClÃnicas
Height, Fat_Percentage_BI, Muscle_Mass_Percentage_BI, Lean_Mass_BI, BMI, # Antropométricas
IL_1B, IL_6, CRP, TNF_a, PAI_1) # Inflamatorias
# Calcular la matriz de correlación de Pearson
cor_matrix <- cor(selected_vars, method = "pearson", use = "complete.obs")
print(cor_matrix)## Insulin_2 HOMA_IR Total_Cholesterol
## Insulin_2 1.00000000 0.99320748 0.43965506
## HOMA_IR 0.99320748 1.00000000 0.39280851
## Total_Cholesterol 0.43965506 0.39280851 1.00000000
## Triglycerides 0.53859263 0.52370442 0.61775183
## Apo_B 0.59845828 0.56268900 0.91343052
## Height 0.20313086 0.20153159 0.28707553
## Fat_Percentage_BI 0.01002150 0.02506200 -0.09317478
## Muscle_Mass_Percentage_BI 0.06039840 0.06570824 -0.02783750
## Lean_Mass_BI 0.56112852 0.57578062 0.28699401
## BMI 0.64993630 0.66693418 0.23361641
## IL_1B -0.33073190 -0.31319967 -0.13574561
## IL_6 -0.08026782 -0.04596279 -0.42019533
## CRP 0.21882517 0.30127499 -0.25715225
## TNF_a -0.31716166 -0.27830900 -0.29344973
## PAI_1 0.74909939 0.72467876 0.36593828
## Triglycerides Apo_B Height
## Insulin_2 0.53859263 0.59845828 0.20313086
## HOMA_IR 0.52370442 0.56268900 0.20153159
## Total_Cholesterol 0.61775183 0.91343052 0.28707553
## Triglycerides 1.00000000 0.76614387 0.23075883
## Apo_B 0.76614387 1.00000000 0.26568309
## Height 0.23075883 0.26568309 1.00000000
## Fat_Percentage_BI -0.13338699 -0.09231182 -0.84550389
## Muscle_Mass_Percentage_BI 0.12238050 0.01295374 0.84093383
## Lean_Mass_BI 0.35714507 0.39212097 0.86668261
## BMI 0.33626177 0.40643000 -0.27786606
## IL_1B -0.29294837 -0.20847191 -0.38964925
## IL_6 -0.19698430 -0.28064484 -0.39732188
## CRP 0.08533578 -0.07082323 0.00581612
## TNF_a -0.44921705 -0.42928887 -0.13148370
## PAI_1 0.65775658 0.53690375 0.16912183
## Fat_Percentage_BI Muscle_Mass_Percentage_BI
## Insulin_2 0.01002150 0.06039840
## HOMA_IR 0.02506200 0.06570824
## Total_Cholesterol -0.09317478 -0.02783750
## Triglycerides -0.13338699 0.12238050
## Apo_B -0.09231182 0.01295374
## Height -0.84550389 0.84093383
## Fat_Percentage_BI 1.00000000 -0.92084965
## Muscle_Mass_Percentage_BI -0.92084965 1.00000000
## Lean_Mass_BI -0.69165224 0.72784323
## BMI 0.50618228 -0.51924169
## IL_1B 0.41635510 -0.39872745
## IL_6 0.35055307 -0.26975540
## CRP 0.12564072 0.05693904
## TNF_a 0.23665109 -0.01926692
## PAI_1 -0.02806754 0.06841327
## Lean_Mass_BI BMI IL_1B IL_6
## Insulin_2 0.5611285 0.64993630 -0.33073190 -0.08026782
## HOMA_IR 0.5757806 0.66693418 -0.31319967 -0.04596279
## Total_Cholesterol 0.2869940 0.23361641 -0.13574561 -0.42019533
## Triglycerides 0.3571451 0.33626177 -0.29294837 -0.19698430
## Apo_B 0.3921210 0.40643000 -0.20847191 -0.28064484
## Height 0.8666826 -0.27786606 -0.38964925 -0.39732188
## Fat_Percentage_BI -0.6916522 0.50618228 0.41635510 0.35055307
## Muscle_Mass_Percentage_BI 0.7278432 -0.51924169 -0.39872745 -0.26975540
## Lean_Mass_BI 1.0000000 0.14827306 -0.40283897 -0.19711086
## BMI 0.1482731 1.00000000 0.03226654 0.29440551
## IL_1B -0.4028390 0.03226654 1.00000000 0.67339372
## IL_6 -0.1971109 0.29440551 0.67339372 1.00000000
## CRP 0.2637706 0.35906780 0.20324175 0.64046095
## TNF_a -0.2583524 -0.34407315 0.56724912 0.25983851
## PAI_1 0.3273461 0.39889798 -0.52447928 -0.24496845
## CRP TNF_a PAI_1
## Insulin_2 0.218825166 -0.31716166 0.749099388
## HOMA_IR 0.301274990 -0.27830900 0.724678757
## Total_Cholesterol -0.257152247 -0.29344973 0.365938281
## Triglycerides 0.085335782 -0.44921705 0.657756577
## Apo_B -0.070823230 -0.42928887 0.536903753
## Height 0.005816120 -0.13148370 0.169121830
## Fat_Percentage_BI 0.125640719 0.23665109 -0.028067539
## Muscle_Mass_Percentage_BI 0.056939039 -0.01926692 0.068413265
## Lean_Mass_BI 0.263770621 -0.25835236 0.327346106
## BMI 0.359067797 -0.34407315 0.398897979
## IL_1B 0.203241752 0.56724912 -0.524479280
## IL_6 0.640460947 0.25983851 -0.244968446
## CRP 1.000000000 0.12810197 0.009407812
## TNF_a 0.128101966 1.00000000 -0.448337233
## PAI_1 0.009407812 -0.44833723 1.000000000corrplot(cor_matrix, method = "circle", type = "upper", tl.col = "black", tl.srt = 45, title = "Pearson Correlation Matrix")
melted_cor_matrix <- melt(cor_matrix)
# Crear el gráfico de correlación (heatmap)
ggplot(data = melted_cor_matrix, aes(x=Var1, y=Var2, fill=value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 9, hjust = 1)) +
coord_fixed() +
labs(title = "Pearson Correlation Heatmap", x = "", y = "") +
theme(plot.title = element_text(hjust = 0.5))
selected_vars <- data_clean %>%
select("hsa-miR-370-3p22", "hsa-miR-376c-3p20", "hsa-miR-769-5p86", "hsa-miR-3913-5p87", "hsa-miR-338-5p01", "hsa-miR-203a-3p64", "hsa-miR-16-5p69", "hsa-miR-15a-5p68", "hsa-let-7a-5p62", "hsa-miR-451047") # miRNAs
# Calcular la matriz de correlación de Pearson
cor_matrix <- cor(selected_vars, method = "pearson", use = "complete.obs")
print(cor_matrix)## hsa-miR-370-3p22 hsa-miR-376c-3p20 hsa-miR-769-5p86
## hsa-miR-370-3p22 1.00000000 0.4947731 0.8998516
## hsa-miR-376c-3p20 0.49477306 1.0000000 0.5336335
## hsa-miR-769-5p86 0.89985158 0.5336335 1.0000000
## hsa-miR-3913-5p87 0.97719530 0.6099285 0.8582944
## hsa-miR-338-5p01 0.02292609 0.8191406 0.2029753
## hsa-miR-203a-3p64 -0.24658214 -0.2626302 -0.3174541
## hsa-miR-16-5p69 -0.19241559 -0.1986226 -0.2813044
## hsa-miR-15a-5p68 -0.18714437 -0.2016896 -0.2724654
## hsa-let-7a-5p62 -0.16263059 -0.2371070 -0.2221143
## hsa-miR-451047 -0.12618130 -0.1922451 -0.1782774
## hsa-miR-3913-5p87 hsa-miR-338-5p01 hsa-miR-203a-3p64
## hsa-miR-370-3p22 0.9771953 0.02292609 -0.24658214
## hsa-miR-376c-3p20 0.6099285 0.81914062 -0.26263015
## hsa-miR-769-5p86 0.8582944 0.20297533 -0.31745411
## hsa-miR-3913-5p87 1.0000000 0.16618025 -0.24211395
## hsa-miR-338-5p01 0.1661802 1.00000000 -0.21544154
## hsa-miR-203a-3p64 -0.2421140 -0.21544154 1.00000000
## hsa-miR-16-5p69 -0.2108774 -0.17242871 -0.14976968
## hsa-miR-15a-5p68 -0.2050342 -0.18088519 -0.09664505
## hsa-let-7a-5p62 -0.2279390 -0.21721258 -0.06635874
## hsa-miR-451047 -0.1914747 -0.17898116 -0.10221485
## hsa-miR-16-5p69 hsa-miR-15a-5p68 hsa-let-7a-5p62
## hsa-miR-370-3p22 -0.19241559 -0.18714437 -0.16263059
## hsa-miR-376c-3p20 -0.19862261 -0.20168963 -0.23710704
## hsa-miR-769-5p86 -0.28130438 -0.27246542 -0.22211430
## hsa-miR-3913-5p87 -0.21087735 -0.20503415 -0.22793897
## hsa-miR-338-5p01 -0.17242871 -0.18088519 -0.21721258
## hsa-miR-203a-3p64 -0.14976968 -0.09664505 -0.06635874
## hsa-miR-16-5p69 1.00000000 0.98460430 -0.04070848
## hsa-miR-15a-5p68 0.98460430 1.00000000 -0.06917140
## hsa-let-7a-5p62 -0.04070848 -0.06917140 1.00000000
## hsa-miR-451047 -0.08014196 -0.11042910 0.99249034
## hsa-miR-451047
## hsa-miR-370-3p22 -0.12618130
## hsa-miR-376c-3p20 -0.19224508
## hsa-miR-769-5p86 -0.17827745
## hsa-miR-3913-5p87 -0.19147468
## hsa-miR-338-5p01 -0.17898116
## hsa-miR-203a-3p64 -0.10221485
## hsa-miR-16-5p69 -0.08014196
## hsa-miR-15a-5p68 -0.11042910
## hsa-let-7a-5p62 0.99249034
## hsa-miR-451047 1.00000000corrplot(cor_matrix, method = "circle", type = "upper", tl.col = "black", tl.srt = 45, title = "Pearson Correlation Matrix")
melted_cor_matrix <- melt(cor_matrix)
# Crear el gráfico de correlación (heatmap)
ggplot(data = melted_cor_matrix, aes(x=Var1, y=Var2, fill=value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 9, hjust = 1)) +
coord_fixed() +
labs(title = "Pearson Correlation Heatmap", x = "", y = "") +
theme(plot.title = element_text(hjust = 0.5))
library(ggplot2)
library(ggpubr)
#install.packages("ggpmisc")
library(ggpmisc)## Loading required package: ggpp## Registered S3 methods overwritten by 'ggpp':
## method from
## heightDetails.titleGrob ggplot2
## widthDetails.titleGrob ggplot2##
## Attaching package: 'ggpp'## The following objects are masked from 'package:ggpubr':
##
## as_npc, as_npcx, as_npcy## The following object is masked from 'package:ggplot2':
##
## annotatelibrary(Hmisc)
library(broom)create_regression_plot <- function(data, x_var, y_var, x_label, y_label) {
ggplot(data, aes_string(x = x_var, y = y_var)) +
geom_point(size = 2, alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE, color = "blue") +
stat_fit_glance(method = "lm",
method.args = list(formula = y ~ x),
aes(label = paste("R² =", signif(..r.squared.., 3), ", p =", signif(..p.value.., 3))),
label.x = 3, label.y = max(data[[y_var]], na.rm = TRUE) * 0.95) + # Mostrar R² y p-valor
labs(x = x_label, y = y_label, title = paste("Regression between", x_label, "and", y_label)) +
theme_minimal()
}
# Seleccionar las variables más significativas para la regresión
selected_vars_regression <- data_clean %>%
select(Insulin_2, HOMA_IR, BMI, IL_6, PAI_1, Apo_B, Total_Cholesterol, Lean_Mass_BI, "hsa-miR-3913-5p87")
# Crear los gráficos de regresión para las correlaciones significativas
plot1 <- create_regression_plot(selected_vars_regression, "Insulin_2", "HOMA_IR", "Insulin (μU/mL)", "HOMA-IR")## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.plot2 <- create_regression_plot(selected_vars_regression, "Insulin_2", "BMI", "Insulin (μU/mL)", "BMI (kg/m²)")
plot3 <- create_regression_plot(selected_vars_regression, "Insulin_2", "IL_6", "Insulin (μU/mL)", "IL-6 (pg/mL)")
plot4 <- create_regression_plot(selected_vars_regression, "Insulin_2", "PAI_1", "Insulin (μU/mL)", "PAI-1 (ng/mL)")
plot5 <- create_regression_plot(selected_vars_regression, "HOMA_IR", "BMI", "HOMA-IR", "BMI (kg/m²)")
plot6 <- create_regression_plot(selected_vars_regression, "HOMA_IR", "IL_6", "HOMA-IR", "IL-6 (pg/mL)")
plot7 <- create_regression_plot(selected_vars_regression, "HOMA_IR", "PAI_1", "HOMA-IR", "PAI-1 (ng/mL)")
plot8 <- create_regression_plot(selected_vars_regression, "Apo_B", "Total_Cholesterol", "Apo B (mg/dL)", "Total Cholesterol (mg/dL)")
plot9 <- create_regression_plot(selected_vars_regression, "Lean_Mass_BI", "Total_Cholesterol", "Lean Mass (kg)", "Total Cholesterol (mg/dL)")
plot10 <- create_regression_plot(selected_vars_regression, "`hsa-miR-3913-5p87`", "Insulin_2", "hsa-miR-3913-5p87", "Insulin (μU/mL)")
plot11 <- create_regression_plot(selected_vars_regression, "`hsa-miR-3913-5p87`", "HOMA_IR", "hsa-miR-3913-5p87", "HOMA-IR")
plot12 <- create_regression_plot(selected_vars_regression, "`hsa-miR-3913-5p87`", "IL_6", "hsa-miR-3913-5p87", "IL_6")
plot13 <- create_regression_plot(selected_vars_regression, "`hsa-miR-3913-5p87`", "Apo_B", "hsa-miR-3913-5p87", "Apo_B")
plot14 <- create_regression_plot(selected_vars_regression, "`hsa-miR-3913-5p87`", "Total_Cholesterol", "hsa-miR-3913-5p87", "Total_Cholesterol")
print(plot1)## Warning: The dot-dot notation (`..r.squared..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(r.squared)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_smooth()` using formula = 'y ~ x'
print(plot2)## `geom_smooth()` using formula = 'y ~ x'
print(plot3)## `geom_smooth()` using formula = 'y ~ x'
print(plot4)## `geom_smooth()` using formula = 'y ~ x'
print(plot5)## `geom_smooth()` using formula = 'y ~ x'
print(plot6)## `geom_smooth()` using formula = 'y ~ x'
print(plot7)## `geom_smooth()` using formula = 'y ~ x'
print(plot8)## `geom_smooth()` using formula = 'y ~ x'
print(plot9)## `geom_smooth()` using formula = 'y ~ x'
print(plot10)## `geom_smooth()` using formula = 'y ~ x'
print(plot11)## `geom_smooth()` using formula = 'y ~ x'
print(plot12)## `geom_smooth()` using formula = 'y ~ x'
print(plot13)## `geom_smooth()` using formula = 'y ~ x'
print(plot14)## `geom_smooth()` using formula = 'y ~ x'