V_plot
Montserrat Espinosa
2026-03-20
paqueterÃa
install.packages("pacman")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)library ("pacman")
p_load("ggplot2", "dplyr", "readr","ggrepel", "matrixTests")base de datos
datos <- read_csv(file="https://raw.githubusercontent.com/ManuelLaraMVZ/Transcript-mica/refs/heads/main/datos_miRNAs.csv")## Rows: 363 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Gen, Condicion
## dbl (6): Cx1, Cx2, Cx3, T1, T2, T3
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(datos)## # A tibble: 6 × 8
## Gen Condicion Cx1 Cx2 Cx3 T1 T2 T3
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 U6 snRNA-001973 Control 13.8 11.7 11.9 13.2 13.0 12.6
## 2 ath-miR159a-000338 Target 35 35 35 35 35 35
## 3 hsa-let-7a-000377 Target 20.5 21.0 21.0 20.4 19.6 20.9
## 4 hsa-let-7b-002619 Target 18.4 19.0 19.1 18.3 17.4 19.0
## 5 hsa-let-7c-000379 Target 22.2 23.7 23.8 22.9 22.0 24.0
## 6 hsa-let-7d-002283 Target 22.2 22.7 21.9 22.0 21.2 21.9dct
Controles <- datos %>%
filter(Condicion=="Control")
head(Controles)## # A tibble: 3 × 8
## Gen Condicion Cx1 Cx2 Cx3 T1 T2 T3
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 U6 snRNA-001973 Control 13.8 11.7 11.9 13.2 13.0 12.6
## 2 RNU44-001094 Control 17.3 16.9 16.8 17.7 17.3 16.8
## 3 RNU48-001006 Control 15.1 16.4 17.0 16.0 15.1 16.4promedio_controles <- Controles %>%
summarise(Mean_C1 = mean(Cx1),
Mean_C2 = mean(Cx2),
Mean_C3 = mean(Cx3),
Mean_T1 = mean(T1),
Mean_T2 = mean(T2),
Mean_T3 = mean(T3)) %>%
mutate(Gen="Promedio_controles") %>%
select(2,1,2,3,4,5,6)
promedio_controles## # A tibble: 1 × 6
## Mean_C2 Mean_C1 Mean_C3 Mean_T1 Mean_T2 Mean_T3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 15.0 15.4 15.3 15.6 15.1 15.3genes <- datos %>%
filter(Condicion=="Target") %>%
select(-2)
head(genes)## # A tibble: 6 × 7
## Gen Cx1 Cx2 Cx3 T1 T2 T3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ath-miR159a-000338 35 35 35 35 35 35
## 2 hsa-let-7a-000377 20.5 21.0 21.0 20.4 19.6 20.9
## 3 hsa-let-7b-002619 18.4 19.0 19.1 18.3 17.4 19.0
## 4 hsa-let-7c-000379 22.2 23.7 23.8 22.9 22.0 24.0
## 5 hsa-let-7d-002283 22.2 22.7 21.9 22.0 21.2 21.9
## 6 hsa-let-7e-002406 18.0 18.4 18.5 18.0 17.3 18.6DCT <- genes %>%
mutate(DCT_C1=2^-(Cx1-promedio_controles$Mean_C1),
DCT_C2=2^-(Cx2-promedio_controles$Mean_C2),
DCT_C3=2^-(Cx3-promedio_controles$Mean_C3),
DCT_T1=2^-(T1-promedio_controles$Mean_T1),
DCT_T2=2^-(T2-promedio_controles$Mean_T2),
DCT_T3=2^-(T3-promedio_controles$Mean_T3)) %>%
select(-2,-3,-4,-5,-6,-7)
DCT## # A tibble: 360 × 7
## Gen DCT_C1 DCT_C2 DCT_C3 DCT_T1 DCT_T2 DCT_T3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ath-miR159a-000338 0.00000124 0.000000960 0.00000114 1.47e-6 1.03e-6 1.16e-6
## 2 hsa-let-7a-000377 0.0289 0.0157 0.0185 3.70e-2 4.49e-2 1.98e-2
## 3 hsa-let-7b-002619 0.127 0.0615 0.0677 1.62e-1 1.98e-1 7.75e-2
## 4 hsa-let-7c-000379 0.00900 0.00243 0.00272 6.40e-3 8.67e-3 2.39e-3
## 5 hsa-let-7d-002283 0.00893 0.00492 0.0102 1.23e-2 1.46e-2 9.87e-3
## 6 hsa-let-7e-002406 0.167 0.0960 0.108 1.91e-1 2.21e-1 1.03e-1
## 7 hsa-let-7f-000382 0.00190 0.000882 0.00146 3.58e-3 4.25e-3 1.24e-3
## 8 hsa-let-7g-002282 0.00963 0.00720 0.00659 9.86e-3 8.59e-3 5.50e-3
## 9 hsa-miR-1-002222 0.0000150 0.00000404 0.00000661 5.99e-6 9.45e-6 3.04e-6
## 10 hsa-miR-100-000437 0.287 0.390 0.498 4.10e-1 2.69e-1 6.25e-1
## # ℹ 350 more rowspromedio_genes <- DCT %>%
mutate(Mean_DCT_Cx=(DCT_C1+DCT_C2+DCT_C3)/3,
Mean_DCT_Tx=(DCT_T1+DCT_T2+DCT_T3)/3)
promedio_genes## # A tibble: 360 × 9
## Gen DCT_C1 DCT_C2 DCT_C3 DCT_T1 DCT_T2 DCT_T3 Mean_DCT_Cx Mean_DCT_Tx
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ath-… 1.24e-6 9.60e-7 1.14e-6 1.47e-6 1.03e-6 1.16e-6 0.00000111 0.00000122
## 2 hsa-… 2.89e-2 1.57e-2 1.85e-2 3.70e-2 4.49e-2 1.98e-2 0.0210 0.0339
## 3 hsa-… 1.27e-1 6.15e-2 6.77e-2 1.62e-1 1.98e-1 7.75e-2 0.0856 0.146
## 4 hsa-… 9.00e-3 2.43e-3 2.72e-3 6.40e-3 8.67e-3 2.39e-3 0.00472 0.00582
## 5 hsa-… 8.93e-3 4.92e-3 1.02e-2 1.23e-2 1.46e-2 9.87e-3 0.00802 0.0123
## 6 hsa-… 1.67e-1 9.60e-2 1.08e-1 1.91e-1 2.21e-1 1.03e-1 0.123 0.172
## 7 hsa-… 1.90e-3 8.82e-4 1.46e-3 3.58e-3 4.25e-3 1.24e-3 0.00142 0.00302
## 8 hsa-… 9.63e-3 7.20e-3 6.59e-3 9.86e-3 8.59e-3 5.50e-3 0.00781 0.00798
## 9 hsa-… 1.50e-5 4.04e-6 6.61e-6 5.99e-6 9.45e-6 3.04e-6 0.00000856 0.00000616
## 10 hsa-… 2.87e-1 3.90e-1 4.98e-1 4.10e-1 2.69e-1 6.25e-1 0.392 0.434
## # ℹ 350 more rowsprueba estadÃstica t-student
tvalue <- row_t_welch(promedio_genes[,c("DCT_C1",
"DCT_C2",
"DCT_C3")],
promedio_genes[,c("DCT_T1",
"DCT_T2",
"DCT_T3")])
FCyPV <- promedio_genes %>%
select(1,8,9) %>%
mutate(p_value = tvalue$pvalue,
fold_change = Mean_DCT_Tx/Mean_DCT_Cx) %>%
select(1,4,5)
FCyPV## # A tibble: 360 × 3
## Gen p_value fold_change
## <chr> <dbl> <dbl>
## 1 ath-miR159a-000338 0.534 1.10
## 2 hsa-let-7a-000377 0.221 1.61
## 3 hsa-let-7b-002619 0.236 1.70
## 4 hsa-let-7c-000379 0.716 1.23
## 5 hsa-let-7d-002283 0.115 1.53
## 6 hsa-let-7e-002406 0.323 1.39
## 7 hsa-let-7f-000382 0.214 2.13
## 8 hsa-let-7g-002282 0.918 1.02
## 9 hsa-miR-1-002222 0.571 0.719
## 10 hsa-miR-100-000437 0.743 1.11
## # ℹ 350 more rowsLogs <- FCyPV %>%
mutate(LPV = -log10(p_value),
LFC = log2(fold_change)) %>%
select(1,4,5)
Logs## # A tibble: 360 × 3
## Gen LPV LFC
## <chr> <dbl> <dbl>
## 1 ath-miR159a-000338 0.272 0.132
## 2 hsa-let-7a-000377 0.655 0.688
## 3 hsa-let-7b-002619 0.627 0.768
## 4 hsa-let-7c-000379 0.145 0.303
## 5 hsa-let-7d-002283 0.938 0.613
## 6 hsa-let-7e-002406 0.491 0.476
## 7 hsa-let-7f-000382 0.669 1.09
## 8 hsa-let-7g-002282 0.0370 0.0320
## 9 hsa-miR-1-002222 0.244 -0.475
## 10 hsa-miR-100-000437 0.129 0.150
## # ℹ 350 more rowsgráfica
vulcano <- ggplot(Logs, mapping = aes(x=LFC,
y=LPV))+
geom_point(size=2,
color="grey")+
theme_classic()+
labs(title="Análisis comparativo de miRNAs",
caption="Creador: Montserrat Espinosa",
x = "Log2 (2^-DDCT)",
y = "-Log10 (p value)")
vulcano
#lÃmites
limite_p <- 0.05
limite_FC <- 1.5
down_regulated <- Logs %>%
filter(LFC < -log2(limite_FC),
LPV > -log10(limite_p))
down_regulated## # A tibble: 1 × 3
## Gen LPV LFC
## <chr> <dbl> <dbl>
## 1 hsa-miR-502-3p-002083 1.49 -0.658up_regulated<- Logs %>%
filter(LFC > log2(limite_FC),
LPV > -log10(limite_p))
up_regulated## # A tibble: 2 × 3
## Gen LPV LFC
## <chr> <dbl> <dbl>
## 1 hsa-miR-148a-000470 1.45 2.46
## 2 hsa-miR-429-001024 1.34 1.63top_down_regulated <- down_regulated %>%
arrange(desc(LPV)) %>%
head()
top_down_regulated## # A tibble: 1 × 3
## Gen LPV LFC
## <chr> <dbl> <dbl>
## 1 hsa-miR-502-3p-002083 1.49 -0.658top_up_regulated <- up_regulated %>%
arrange(desc(LPV)) %>%
head()
top_up_regulated## # A tibble: 2 × 3
## Gen LPV LFC
## <chr> <dbl> <dbl>
## 1 hsa-miR-148a-000470 1.45 2.46
## 2 hsa-miR-429-001024 1.34 1.63vulcano2 <- vulcano+
geom_hline(yintercept = -log10(limite_p),
linetype = "dashed")+
geom_vline(xintercept = c(-log2(limite_FC), log2(limite_FC)),
linetype="dashed")
vulcano2
vulcano3 <- vulcano2+
geom_point(data=up_regulated,
x=up_regulated$LFC,
y=up_regulated$LPV,
alpha=1,
size=3,
color="#D96E2B")+
geom_point(data=down_regulated,
x=down_regulated$LFC,
y=down_regulated$LPV,
alpha=1,
size=3,
color="#8336C2")
vulcano3
vulcano4 <- vulcano3+
geom_label_repel(data=top_up_regulated,
mapping=aes(x = top_up_regulated$LFC,
y = top_up_regulated$LPV),
label = top_up_regulated$Gen,
max.overlaps = 50)+
geom_label_repel(data=top_down_regulated,
mapping=aes(x = top_down_regulated$LFC,
y = top_down_regulated$LPV),
label = top_down_regulated$Gen,
max.overlaps = 50)
vulcano4## Warning: Use of `top_up_regulated$LFC` is discouraged.
## ℹ Use `LFC` instead.## Warning: Use of `top_up_regulated$LPV` is discouraged.
## ℹ Use `LPV` instead.## Warning: Use of `top_down_regulated$LFC` is discouraged.
## ℹ Use `LFC` instead.## Warning: Use of `top_down_regulated$LPV` is discouraged.
## ℹ Use `LPV` instead.
gráfica sin empalme de labels
vulcano5 <- vulcano3+
geom_label_repel(data = top_up_regulated,
aes(x=LFC, y=LPV, label=Gen),
box.padding = unit(0.35, "lines"),
point.padding = unit(0.5, "lines"),
segment.color = "grey50",
segment.size = 0.2,
max.overlaps = 50,
nudge_x = 1.0,
nudge_y = -0.5,
show.legend = FALSE)+
geom_label_repel(data = top_down_regulated,
aes(x=LFC, y=LPV, label=Gen),
box.padding = unit(0.35, "lines"),
point.padding = unit(0.5, "lines"),
segment.color = "grey50",
segment.size = 0.2,
max.overlaps = 50,
nudge_x = -0.5,
nudge_y = -0.5,
show.legend = FALSE)
vulcano5