Practica_9_Fabian_Zevallos
Fabian Zevallos
2025-10-16
library(ggplot2)
library(patchwork)
datos <- read.csv("Dataset expresión genes.csv", header = TRUE, sep = ",")
str(datos)## 'data.frame': 65 obs. of 104 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ edad : num 55.7 48.7 63.6 55.3 74.1 ...
## $ sexo : chr "mujer" "varon" "mujer" "mujer" ...
## $ exfumador : chr "si" "no" "no" "si" ...
## $ hta : chr "si" "si" "no" "no" ...
## $ dm : chr "no" "no" "no" "no" ...
## $ alergia : chr "si" "no" "no" "no" ...
## $ cardiopatia : chr "si" "no" "no" "no" ...
## $ ETE : chr "si" "no" "no" "no" ...
## $ neumopatia : chr "si" "si" "si" "no" ...
## $ hepatopatia : chr "no" "no" "no" "no" ...
## $ colelitiasis : chr "no" "no" "no" "no" ...
## $ utolitiasis : chr "no" "no" "no" "no" ...
## $ ITU : chr "si" "no" "no" "no" ...
## $ renal : chr "si" "no" "no" "no" ...
## $ neuropatia : chr "si" "si" "no" "no" ...
## $ corticoides : chr "si" "antiemesis" "si" "antiemesis" ...
## $ tos : chr "no" "no" "no" "no" ...
## $ disnea : chr "si" "no" "no" "no" ...
## $ expect : chr "no" "no" "no" "no" ...
## $ secrecion : chr "si" "si" "no" "no" ...
## $ dolor_garg : chr "no" "no" "no" "no" ...
## $ escalofrios : chr "no" "no" "no" "no" ...
## $ fiebre : chr "no" "no" "si" "no" ...
## $ diarrea : chr "no" "no" "si" "no" ...
## $ nauseas : chr "no" "no" "no" "no" ...
## $ vomitos : chr "no" "no" "no" "no" ...
## $ cefalea : chr "no" "no" "no" "si" ...
## $ mareo : chr "no" "no" "si" "no" ...
## $ cansancio : chr "si" "no" "si" "no" ...
## $ anosmia : chr "no" "no" "no" "no" ...
## $ disgueusia : chr "no" "no" "si" "no" ...
## $ dolor_hueso : chr "si" "no" "si" "no" ...
## $ dolor_abdo : chr "no" "no" "si" "no" ...
## $ perd_ape : chr "no" "no" "si" "no" ...
## $ glucosa : num 88 98 92 103 139 106 100 86 133 105 ...
## $ leucocitos : num 7.4 5.69 18.94 9.46 4.18 ...
## $ linfocitos : num 1.1 0.9 3.3 2.7 1.45 ...
## $ neutrofilos : num 5.3 4.2 14.5 6.2 2.05 ...
## $ score_dieta : int 4 6 8 9 11 6 9 2 12 4 ...
## $ chol : num 244 226 212 190 137 143 203 180 185 214 ...
## $ hdl : num 69 86 65 35 36 28 38 60 44 51 ...
## $ hierro : num 60 207 140 59 49 36 66 108 80 71 ...
## $ igA : num 194 264 424 206 387 ...
## $ igE : num 8 94.6 35 18 11 ...
## $ igG : num 756 631 930 509 910 ...
## $ igN : num 111 26 76 62 81 ...
## $ ldl : num 255 178 283 199 181 ...
## $ pcr : num 10.2 0.5 18.5 0.7 0.6 ...
## $ transferrina : num 290 316 155 253 349 271 262 192 303 248 ...
## $ trigliceridos : int 129 56 255 428 155 213 194 96 141 166 ...
## $ cpk : num 71.7 130 24.5 51 53 ...
## $ calidad_fisica: num 40.4 52.8 14.9 36.1 49.9 ...
## $ calidad_mental: num 52.9 50.9 49.1 61.9 54.1 ...
## $ tumor : chr "CCR" "CCR" "CM" "CCR" ...
## $ extension : chr "metastasico" "metastasico" "metastasico" "localizado" ...
## $ trat : chr "tratA" "tratB" "tratA" "tratA" ...
## $ AQ_ADIPOQ : num 4.11e-10 3.13e-10 0.00 1.25e-09 0.00 ...
## $ AQ_ALOX5 : num 2.44e-05 1.62e-05 5.17e-05 5.41e-05 1.26e-05 ...
## $ AQ_ARG1 : num 5.98e-07 5.20e-07 1.01e-05 2.01e-06 8.68e-07 ...
## $ AQ_BMP2 : num 2.17e-09 3.00e-09 1.54e-07 9.12e-09 6.01e-09 ...
## $ AQ_CCL2 : num 1.72e-08 4.00e-09 5.01e-08 1.24e-07 3.40e-09 ...
## $ AQ_CCL5 : num 5.86e-05 3.23e-05 8.56e-04 4.20e-04 2.27e-04 ...
## $ AQ_CCR5 : num 1.00e-06 7.48e-07 8.43e-06 3.66e-06 4.22e-06 ...
## $ AQ_CD274 : num 4.44e-07 8.36e-08 2.83e-06 9.11e-07 8.63e-07 ...
## $ AQ_CD36 : num 5.62e-06 4.28e-06 6.67e-05 1.72e-05 8.44e-06 ...
## $ AQ_CHKA : num 9.94e-08 9.81e-08 7.64e-07 0.00 2.61e-07 ...
## $ AQ_CPT1A : num 1.08e-06 1.25e-06 1.20e-05 4.73e-06 3.96e-06 ...
## $ AQ_CSF2 : num 4.11e-10 1.68e-09 0.00 7.85e-09 1.50e-08 ...
## $ AQ_CXCR1 : num 4.74e-06 3.01e-06 8.89e-05 9.08e-06 4.86e-06 ...
## $ AQ_FASN : num 2.88e-07 2.52e-07 4.28e-06 2.56e-06 7.13e-07 ...
## $ AQ_FOXO3 : num 4.62e-05 2.47e-05 2.48e-04 3.59e-04 1.26e-04 ...
## $ AQ_FOXP3 : num 1.10e-07 5.81e-08 1.39e-06 8.41e-07 1.12e-07 ...
## $ AQ_G6PD : num 9.87e-06 4.50e-06 4.37e-05 2.99e-05 1.08e-05 ...
## $ AQ_GPD2 : num 0.00 4.97e-07 4.89e-06 1.25e-06 4.51e-07 ...
## $ AQ_GPX1 : num 6.26e-05 2.54e-05 1.48e-04 1.48e-04 5.19e-05 ...
## $ AQ_IFNG : num 0.00 3.55e-09 8.64e-07 1.07e-07 1.84e-07 ...
## $ AQ_IL10 : num 8.75e-09 4.51e-09 6.25e-08 2.28e-08 1.88e-08 ...
## $ AQ_IL1B : num 2.41e-06 1.64e-06 2.08e-05 4.65e-06 3.13e-06 ...
## $ AQ_IL6 : num 7.66e-09 1.63e-09 5.38e-08 1.25e-09 1.41e-08 ...
## $ AQ_IRS1 : num 3.84e-08 6.47e-08 1.40e-07 9.76e-08 5.01e-08 ...
## $ AQ_JAK1 : num 1.14e-05 1.25e-05 1.21e-04 7.57e-05 2.70e-05 ...
## $ AQ_JAK3 : num 3.17e-06 1.67e-06 5.26e-05 4.56e-06 7.54e-06 ...
## $ AQ_LDHA : num 5.48e-06 6.69e-06 3.16e-05 1.27e-05 7.18e-06 ...
## $ AQ_LIF : num 5.73e-09 3.16e-09 0.00 1.41e-08 7.14e-09 ...
## $ AQ_MAPK1 : num 5.86e-06 2.05e-06 6.00e-05 2.50e-05 9.39e-06 ...
## $ AQ_NFE2L2 : num 5.13e-06 2.70e-06 3.47e-05 1.70e-05 1.31e-05 ...
## $ AQ_NFKB1 : num 2.58e-06 5.06e-07 2.50e-05 6.13e-06 2.28e-06 ...
## $ AQ_NLRP3 : num 1.11e-06 1.03e-06 4.25e-06 2.77e-06 1.43e-06 ...
## $ AQ_NOS2 : num 4.11e-10 3.13e-10 0.00 1.25e-09 1.09e-09 ...
## $ AQ_NOX5 : num 4.11e-10 3.13e-10 0.00 1.25e-09 0.00 ...
## $ AQ_PDCD1 : num 9.51e-08 3.19e-08 1.64e-06 1.14e-06 2.81e-07 ...
## $ AQ_PPARG : num 1.18e-08 1.06e-08 4.05e-08 0.00 1.46e-08 ...
## $ AQ_PTAFR : num 1.19e-05 1.19e-05 6.66e-05 1.52e-05 1.65e-05 ...
## $ AQ_PTGS2 : num 2.09e-06 9.11e-07 7.41e-06 2.51e-06 2.09e-06 ...
## $ AQ_SLC2A4 : num 1.93e-07 8.05e-08 2.24e-07 3.14e-07 7.35e-08 ...
## $ AQ_SOD1 : num 4.02e-06 1.31e-06 1.90e-05 1.02e-05 8.32e-06 ...
## $ AQ_SREBF1 : num 1.63e-06 9.57e-07 6.62e-06 2.98e-06 1.10e-06 ...
## [list output truncated]summary(datos)## X id edad sexo
## Min. : 1.00 Min. : 1.00 Min. :30.35 Length:65
## 1st Qu.:18.00 1st Qu.:18.00 1st Qu.:57.62 Class :character
## Median :35.00 Median :35.00 Median :66.49 Mode :character
## Mean :34.51 Mean :34.51 Mean :65.46
## 3rd Qu.:51.00 3rd Qu.:51.00 3rd Qu.:75.09
## Max. :67.00 Max. :67.00 Max. :94.06
## exfumador hta dm alergia
## Length:65 Length:65 Length:65 Length:65
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## cardiopatia ETE neumopatia hepatopatia
## Length:65 Length:65 Length:65 Length:65
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## colelitiasis utolitiasis ITU renal
## Length:65 Length:65 Length:65 Length:65
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## neuropatia corticoides tos disnea
## Length:65 Length:65 Length:65 Length:65
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## expect secrecion dolor_garg escalofrios
## Length:65 Length:65 Length:65 Length:65
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## fiebre diarrea nauseas vomitos
## Length:65 Length:65 Length:65 Length:65
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## cefalea mareo cansancio anosmia
## Length:65 Length:65 Length:65 Length:65
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## disgueusia dolor_hueso dolor_abdo perd_ape
## Length:65 Length:65 Length:65 Length:65
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## glucosa leucocitos linfocitos neutrofilos
## Min. : 76.0 Min. : 1.345 Min. :0.350 Min. : 0.800
## 1st Qu.: 91.0 1st Qu.: 4.385 1st Qu.:1.100 1st Qu.: 2.350
## Median :105.0 Median : 5.690 Median :1.400 Median : 3.650
## Mean :108.9 Mean : 6.500 Mean :1.465 Mean : 4.256
## 3rd Qu.:114.0 3rd Qu.: 7.525 3rd Qu.:1.800 3rd Qu.: 5.200
## Max. :208.0 Max. :23.400 Max. :3.300 Max. :19.600
## score_dieta chol hdl hierro
## Min. : 2.000 Min. :101.0 Min. : 22.00 Min. : 19.00
## 1st Qu.: 7.000 1st Qu.:146.0 1st Qu.: 37.00 1st Qu.: 53.00
## Median : 9.000 Median :178.0 Median : 48.00 Median : 67.00
## Mean : 8.323 Mean :176.8 Mean : 49.17 Mean : 72.36
## 3rd Qu.:10.000 3rd Qu.:195.0 3rd Qu.: 57.00 3rd Qu.: 84.00
## Max. :12.000 Max. :263.0 Max. :108.00 Max. :207.00
## igA igE igG igN
## Min. : 34.0 Min. : 2.00 Min. : 252.0 Min. : 26.00
## 1st Qu.:153.0 1st Qu.: 13.00 1st Qu.: 711.9 1st Qu.: 62.00
## Median :190.8 Median : 59.59 Median : 854.9 Median : 79.00
## Mean :210.5 Mean : 66.20 Mean : 868.9 Mean : 87.73
## 3rd Qu.:255.1 3rd Qu.: 94.60 3rd Qu.: 938.9 3rd Qu.: 96.00
## Max. :495.0 Max. :366.00 Max. :1828.0 Max. :290.00
## ldl pcr transferrina trigliceridos
## Min. :124.0 Min. : 0.10 Min. :131.0 Min. : 43.0
## 1st Qu.:170.9 1st Qu.: 1.10 1st Qu.:231.0 1st Qu.: 90.0
## Median :195.0 Median : 4.80 Median :253.0 Median :129.0
## Mean :205.2 Mean : 12.22 Mean :255.6 Mean :145.5
## 3rd Qu.:220.0 3rd Qu.: 11.00 3rd Qu.:274.0 3rd Qu.:173.0
## Max. :484.0 Max. :194.20 Max. :378.0 Max. :469.0
## cpk calidad_fisica calidad_mental tumor
## Min. : 15.00 Min. :14.94 Min. :22.75 Length:65
## 1st Qu.: 37.00 1st Qu.:32.23 1st Qu.:46.57 Class :character
## Median : 57.00 Median :39.43 Median :50.89 Mode :character
## Mean : 68.99 Mean :38.99 Mean :48.62
## 3rd Qu.: 91.00 3rd Qu.:49.89 3rd Qu.:56.82
## Max. :197.00 Max. :58.05 Max. :66.76
## extension trat AQ_ADIPOQ AQ_ALOX5
## Length:65 Length:65 Min. :0.00000 Min. :0.000e+00
## Class :character Class :character 1st Qu.:0.00000 1st Qu.:1.987e-05
## Mode :character Mode :character Median :0.00000 Median :4.428e-05
## Mean :0.01538 Mean :6.958e-05
## 3rd Qu.:0.00000 3rd Qu.:1.100e-04
## Max. :1.00000 Max. :2.573e-04
## AQ_ARG1 AQ_BMP2 AQ_CCL2
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:8.099e-07 1st Qu.:1.365e-08 1st Qu.:1.381e-08
## Median :2.116e-06 Median :2.936e-08 Median :3.829e-08
## Mean :4.051e-06 Mean :5.385e-08 Mean :1.185e-07
## 3rd Qu.:5.037e-06 3rd Qu.:7.544e-08 3rd Qu.:1.284e-07
## Max. :2.657e-05 Max. :2.902e-07 Max. :1.303e-06
## AQ_CCL5 AQ_CCR5 AQ_CD274
## Min. :0.0000000 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:0.0001168 1st Qu.:3.201e-06 1st Qu.:5.795e-07
## Median :0.0002944 Median :5.585e-06 Median :1.307e-06
## Mean :0.0005178 Mean :9.236e-06 Mean :1.930e-06
## 3rd Qu.:0.0007839 3rd Qu.:1.206e-05 3rd Qu.:2.849e-06
## Max. :0.0024790 Max. :4.677e-05 Max. :1.012e-05
## AQ_CD36 AQ_CHKA AQ_CPT1A
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:9.511e-06 1st Qu.:1.499e-07 1st Qu.:2.688e-06
## Median :2.284e-05 Median :4.550e-07 Median :5.168e-06
## Mean :2.868e-05 Mean :7.393e-07 Mean :9.178e-06
## 3rd Qu.:4.474e-05 3rd Qu.:1.105e-06 3rd Qu.:1.323e-05
## Max. :8.596e-05 Max. :3.703e-06 Max. :3.898e-05
## AQ_CSF2 AQ_CXCR1 AQ_FASN
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:4.883e-10 1st Qu.:4.703e-06 1st Qu.:1.065e-06
## Median :1.499e-08 Median :9.432e-06 Median :3.039e-06
## Mean :2.965e-08 Mean :1.832e-05 Mean :4.137e-06
## 3rd Qu.:3.840e-08 3rd Qu.:2.404e-05 3rd Qu.:5.387e-06
## Max. :1.543e-07 Max. :8.887e-05 Max. :1.685e-05
## AQ_FOXO3 AQ_FOXP3 AQ_G6PD
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:7.279e-05 1st Qu.:3.306e-07 1st Qu.:1.681e-05
## Median :1.258e-04 Median :1.047e-06 Median :3.525e-05
## Mean :1.822e-04 Mean :1.639e-06 Mean :4.274e-05
## 3rd Qu.:2.552e-04 3rd Qu.:1.897e-06 3rd Qu.:5.995e-05
## Max. :7.050e-04 Max. :1.504e-05 Max. :1.759e-04
## AQ_GPD2 AQ_GPX1 AQ_IFNG
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:4.969e-07 1st Qu.:5.104e-05 1st Qu.:4.492e-08
## Median :1.706e-06 Median :7.844e-05 Median :2.153e-07
## Mean :2.531e-06 Mean :9.614e-05 Mean :3.374e-07
## 3rd Qu.:4.140e-06 3rd Qu.:1.401e-04 3rd Qu.:4.211e-07
## Max. :1.563e-05 Max. :2.836e-04 Max. :1.855e-06
## AQ_IL10 AQ_IL1B AQ_IL6
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:9.934e-09 1st Qu.:3.217e-06 1st Qu.:1.744e-09
## Median :4.052e-08 Median :8.477e-06 Median :1.200e-08
## Mean :9.988e-08 Mean :1.358e-05 Mean :3.067e-08
## 3rd Qu.:1.021e-07 3rd Qu.:1.939e-05 3rd Qu.:3.408e-08
## Max. :1.233e-06 Max. :8.704e-05 Max. :3.061e-07
## AQ_IRS1 AQ_JAK1 AQ_JAK3
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:5.753e-08 1st Qu.:3.977e-05 1st Qu.:6.108e-06
## Median :9.505e-08 Median :7.981e-05 Median :1.898e-05
## Mean :1.328e-07 Mean :1.117e-04 Mean :2.981e-05
## 3rd Qu.:1.877e-07 3rd Qu.:1.596e-04 3rd Qu.:3.938e-05
## Max. :5.487e-07 Max. :4.581e-04 Max. :2.503e-04
## AQ_LDHA AQ_LIF AQ_MAPK1
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:1.266e-05 1st Qu.:1.423e-09 1st Qu.:8.631e-06
## Median :2.536e-05 Median :8.366e-09 Median :1.977e-05
## Mean :3.035e-05 Mean :2.182e-08 Mean :2.760e-05
## 3rd Qu.:4.428e-05 3rd Qu.:2.235e-08 3rd Qu.:4.114e-05
## Max. :8.948e-05 Max. :1.531e-07 Max. :1.100e-04
## AQ_NFE2L2 AQ_NFKB1 AQ_NLRP3
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:6.425e-06 1st Qu.:2.915e-06 1st Qu.:1.800e-06
## Median :2.352e-05 Median :1.353e-05 Median :5.013e-06
## Mean :3.090e-05 Mean :1.576e-05 Mean :6.757e-06
## 3rd Qu.:4.180e-05 3rd Qu.:2.513e-05 3rd Qu.:9.657e-06
## Max. :1.008e-04 Max. :5.448e-05 Max. :2.814e-05
## AQ_NOS2 AQ_NOX5 AQ_PDCD1 AQ_PPARG
## Min. :0.000e+00 Min. :0.00000 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:0.000e+00 1st Qu.:0.00000 1st Qu.:4.073e-07 1st Qu.:1.456e-08
## Median :7.045e-10 Median :0.00000 Median :8.844e-07 Median :5.469e-08
## Mean :3.118e-09 Mean :0.01538 Mean :1.694e-06 Mean :1.429e-07
## 3rd Qu.:2.827e-09 3rd Qu.:0.00000 3rd Qu.:1.651e-06 3rd Qu.:1.911e-07
## Max. :2.258e-08 Max. :1.00000 Max. :1.209e-05 Max. :1.115e-06
## AQ_PTAFR AQ_PTGS2 AQ_SLC2A4
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:1.594e-05 1st Qu.:2.093e-06 1st Qu.:7.946e-08
## Median :3.280e-05 Median :2.834e-06 Median :1.438e-07
## Mean :4.573e-05 Mean :4.857e-06 Mean :1.936e-07
## 3rd Qu.:6.716e-05 3rd Qu.:7.218e-06 3rd Qu.:2.441e-07
## Max. :1.501e-04 Max. :2.633e-05 Max. :1.432e-06
## AQ_SOD1 AQ_SREBF1 AQ_STAT3
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:7.001e-06 1st Qu.:2.927e-06 1st Qu.:9.751e-06
## Median :1.563e-05 Median :6.505e-06 Median :2.840e-05
## Mean :2.058e-05 Mean :8.676e-06 Mean :3.802e-05
## 3rd Qu.:2.386e-05 3rd Qu.:1.428e-05 3rd Qu.:4.876e-05
## Max. :8.115e-05 Max. :3.157e-05 Max. :2.000e-04
## AQ_TGFB1 AQ_TLR3 AQ_TLR4
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:9.901e-05 1st Qu.:4.106e-08 1st Qu.:5.644e-06
## Median :1.896e-04 Median :2.333e-07 Median :1.333e-05
## Mean :2.738e-04 Mean :4.426e-07 Mean :1.886e-05
## 3rd Qu.:3.815e-04 3rd Qu.:4.701e-07 3rd Qu.:2.881e-05
## Max. :1.296e-03 Max. :3.253e-06 Max. :8.054e-05
## AQ_TNF
## Min. :0.000e+00
## 1st Qu.:2.683e-06
## Median :6.582e-06
## Mean :1.104e-05
## 3rd Qu.:1.611e-05
## Max. :6.215e-05#Ejercicio 1, comparación de la expresión génica según tratamiento
genes <- c("AQ_ALOX5", "AQ_CD274", "AQ_CHKA", "AQ_CSF2", "AQ_FOXO3",
"AQ_IL6", "AQ_LDHA", "AQ_LIF", "AQ_MAPK1", "AQ_NOS2",
"AQ_IFNG", "AQ_PDCD1", "AQ_PPARG", "AQ_TGFB1", "AQ_TNF")#Creacion de graficos
plots <- lapply(genes, function(gen) {
ggplot(datos, aes(x = trat, y = .data[[gen]], fill = trat)) +
geom_boxplot(outlier.colour = "gray40", alpha = 0.7) +
scale_fill_manual(values = c("#66C2A5", "#FC8D62")) +
labs(title = gen,
x = "Trat",
y = "expresión") +
theme_minimal(base_size = 9) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 7),
axis.text.y = element_text(size = 7),
plot.title = element_text(face = "bold", size = 9),
legend.position = "none"
)
})#Union de graficos con pathwork
colnames(datos)## [1] "X" "id" "edad" "sexo"
## [5] "exfumador" "hta" "dm" "alergia"
## [9] "cardiopatia" "ETE" "neumopatia" "hepatopatia"
## [13] "colelitiasis" "utolitiasis" "ITU" "renal"
## [17] "neuropatia" "corticoides" "tos" "disnea"
## [21] "expect" "secrecion" "dolor_garg" "escalofrios"
## [25] "fiebre" "diarrea" "nauseas" "vomitos"
## [29] "cefalea" "mareo" "cansancio" "anosmia"
## [33] "disgueusia" "dolor_hueso" "dolor_abdo" "perd_ape"
## [37] "glucosa" "leucocitos" "linfocitos" "neutrofilos"
## [41] "score_dieta" "chol" "hdl" "hierro"
## [45] "igA" "igE" "igG" "igN"
## [49] "ldl" "pcr" "transferrina" "trigliceridos"
## [53] "cpk" "calidad_fisica" "calidad_mental" "tumor"
## [57] "extension" "trat" "AQ_ADIPOQ" "AQ_ALOX5"
## [61] "AQ_ARG1" "AQ_BMP2" "AQ_CCL2" "AQ_CCL5"
## [65] "AQ_CCR5" "AQ_CD274" "AQ_CD36" "AQ_CHKA"
## [69] "AQ_CPT1A" "AQ_CSF2" "AQ_CXCR1" "AQ_FASN"
## [73] "AQ_FOXO3" "AQ_FOXP3" "AQ_G6PD" "AQ_GPD2"
## [77] "AQ_GPX1" "AQ_IFNG" "AQ_IL10" "AQ_IL1B"
## [81] "AQ_IL6" "AQ_IRS1" "AQ_JAK1" "AQ_JAK3"
## [85] "AQ_LDHA" "AQ_LIF" "AQ_MAPK1" "AQ_NFE2L2"
## [89] "AQ_NFKB1" "AQ_NLRP3" "AQ_NOS2" "AQ_NOX5"
## [93] "AQ_PDCD1" "AQ_PPARG" "AQ_PTAFR" "AQ_PTGS2"
## [97] "AQ_SLC2A4" "AQ_SOD1" "AQ_SREBF1" "AQ_STAT3"
## [101] "AQ_TGFB1" "AQ_TLR3" "AQ_TLR4" "AQ_TNF"wrap_plots(plots, ncol = 6) + plot_layout(guides = "collect")
ggsave("expresion_genes.png", width = 14, height = 10 )“Interpretación: Al comparar la expresión de los 15 genes seleccionados entre los tratamientos A y B, se observó una variabilidad notable en los niveles de AQ. Los genes AQ_IL6, AQ_TNF y AQ_CD274 presentaron una mayor expresión en el grupo con tratamiento B, lo cual podría asociarse con una activación de rutas inflamatorias e inmunorreguladoras. En contraste, AQ_PPARG y AQ_TGFB1 mostraron valores más elevados en el tratamiento A, sugiriendo un posible efecto regulador o antiinflamatorio. La dispersión dentro de cada caja indica una heterogeneidad interindividual, reflejando la variabilidad biológica propia de las respuestas génicas. En conjunto, estos resultados sugieren que el tratamiento podría modular diferencialmente las vías inflamatorias y metabólicas, lo cual tendría implicaciones en la eficacia terapéutica y en la respuesta inmunológica de los pacientes.”
#Ejercicio 2, distribución de parámetros bioquímicos
bio_vars <- c("glucosa", "leucocitos", "linfocitos", "neutrofilos",
"chol", "hdl", "hierro", "igA", "igE", "igG", "igN",
"ldl", "pcr", "transferrina", "trigliceridos", "cpk")
plots_bio <- lapply(bio_vars, function(var) {
ggplot(datos, aes(x = .data[[var]])) +
geom_histogram(aes(y = ..density..),
bins = 30,
fill = "#56B4E9", color = "white", alpha = 0.7) +
geom_density(color = "red", size = 1) +
labs(title = paste("Distribución de", var),
x = var, y = "Densidad") +
theme_minimal()
})## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.#Grafico combinado
library(patchwork)
wrap_plots(plots_bio, ncol = 3)## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
“La visualización de histogramas muestra que la mayoría de los parámetros bioquímicos presentan una amplia heterogeneidad entre pacientes. Variables metabólicas como glucosa, triglicéridos y PCR exhiben distribuciones sesgadas a la derecha, con colas largas que indican la presencia de sujetos con valores elevados (posibles casos con hiperglucemia, dislipidemia o inflamación sistémica). Por su parte, algunos marcadores inmunológicos y de recuento celular (linfocitos, leucocitos, neutrófilos) muestran distribuciones más concentradas, aunque con dispersión suficiente que denota variabilidad individual. En general, las distribuciones observadas no siguen una campana de Gauss, lo cual sugiere la necesidad de emplear pruebas no paramétricas (Wilcoxon, Kruskal–Wallis) o transformaciones logarítmicas en futuras comparaciones. Conclusión El análisis gráfico permitió visualizar diferencias en la expresión génica y en los parámetros bioquímicos de la población estudiada. Esto constituye una base para investigaciones posteriores sobre biomarcadores asociados a tratamientos o estados clínicos específicos.” “Interpretación de la distribución: La mayoría de las variables bioquímicas presentan una distribución asimétrica, especialmente triglicéridos, pcr y cpk, que muestran colas derechas pronunciadas, típicas de concentraciones que aumentan en respuesta a procesos inflamatorios o metabólicos. Variables como glucosa y hdl tienden a distribuciones más cercanas a la normalidad, aunque con cierta dispersión. Este comportamiento sugiere que la población estudiada es heterogénea, con presencia de individuos en rangos fisiológicos y patológicos.”