PCA Analysis
Your Name
2025-02-02
Last updated: 2025-02-02
Checks: 7 0
Knit directory: CX5461_Project/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 56e44e6 | sayanpaul01 | 2025-02-02 | Fixed duplicate chunk labels in PCA analysis |
| html | 773671b | sayanpaul01 | 2025-02-01 | Build site. |
| Rmd | 91e6c2c | sayanpaul01 | 2025-02-01 | Fixed duplicate row names issue in count matrix |
Load Required Libraries
library(edgeR)Warning: package 'edgeR' was built under R version 4.3.1Warning: package 'limma' was built under R version 4.3.1library(ggplot2)Warning: package 'ggplot2' was built under R version 4.3.3library(reshape2)
library(dplyr)Warning: package 'dplyr' was built under R version 4.3.2library(Biobase)Warning: package 'Biobase' was built under R version 4.3.1Warning: package 'BiocGenerics' was built under R version 4.3.1library(limma)
library(tidyverse)Warning: package 'tidyverse' was built under R version 4.3.2Warning: package 'tidyr' was built under R version 4.3.3Warning: package 'readr' was built under R version 4.3.3Warning: package 'purrr' was built under R version 4.3.1Warning: package 'stringr' was built under R version 4.3.2Warning: package 'lubridate' was built under R version 4.3.1library(scales)Warning: package 'scales' was built under R version 4.3.2library(biomaRt)Warning: package 'biomaRt' was built under R version 4.3.2library(ggrepel)Warning: package 'ggrepel' was built under R version 4.3.3library(corrplot)Warning: package 'corrplot' was built under R version 4.3.3library(Hmisc)Warning: package 'Hmisc' was built under R version 4.3.3library(org.Hs.eg.db)Warning: package 'AnnotationDbi' was built under R version 4.3.2Warning: package 'IRanges' was built under R version 4.3.1Warning: package 'S4Vectors' was built under R version 4.3.1library(AnnotationDbi)
library(tidyr)
library(ggfortify)📍 Load the Count Matrix CSV file
📌Color palettes
drug_palc <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
drug_palc1 <- c("#8B006D","#F1B72B", "#3386DD","#707031")
drug_palc2 <- c("#8B006D","#F1B72B", "#3386DD")📌Load Metadata
📌PCA of Unfiltered log2(CPM)
prcomp_res <- prcomp(t(lcpm %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res, data = Metadata, colour = "Condition", shape = "Time", size =4, x=1, y=2) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Unfiltered")) +
theme_bw()Warning: ggrepel: 67 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 773671b | sayanpaul01 | 2025-02-01 |
prcomp_res <- prcomp(t(lcpm %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res, data = Metadata, colour = "Condition", shape = "Time", size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Unfiltered")) +
theme_bw()Warning: ggrepel: 33 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 773671b | sayanpaul01 | 2025-02-01 |
prcomp_res <- prcomp(t(lcpm %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res, data = Metadata, colour = "Condition", shape = "Time", size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Unfiltered")) +
theme_bw()Warning: ggrepel: 38 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 773671b | sayanpaul01 | 2025-02-01 |
📌PCA of Filtered log2(CPM) (RowMeans > 0)
prcomp_res1 <- prcomp(t(filcpm_matrix %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res1, data = Metadata, colour = "Condition", shape = "Time", size =4, x=1, y=2) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >0)")) +
theme_bw()Warning: ggrepel: 51 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 773671b | sayanpaul01 | 2025-02-01 |
prcomp_res1 <- prcomp(t(filcpm_matrix %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res1, data = Metadata, colour = "Condition", shape = "Time", size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >0)")) +
theme_bw()Warning: ggrepel: 22 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 773671b | sayanpaul01 | 2025-02-01 |
prcomp_res1 <- prcomp(t(filcpm_matrix %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res1, data = Metadata, colour = "Condition", shape = "Time", size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >0)")) +
theme_bw()Warning: ggrepel: 26 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 773671b | sayanpaul01 | 2025-02-01 |
📌PCA of Filtered log2(CPM) (RowMeans > 0.5)
prcomp_res2 <- prcomp(t(filcpm_matrix1 %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res2, data = Metadata, colour = "Condition", shape = "Time", size =4, x=1, y=2) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >0.5)")) +
theme_bw()Warning: ggrepel: 54 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 773671b | sayanpaul01 | 2025-02-01 |
prcomp_res2 <- prcomp(t(filcpm_matrix1 %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res2, data = Metadata, colour = "Condition", shape = "Time", size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >0.5)")) +
theme_bw()Warning: ggrepel: 28 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 773671b | sayanpaul01 | 2025-02-01 |
prcomp_res2 <- prcomp(t(filcpm_matrix1 %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res2, data = Metadata, colour = "Condition", shape = "Time", size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >0.5)")) +
theme_bw()Warning: ggrepel: 26 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 773671b | sayanpaul01 | 2025-02-01 |
📌PCA of Filtered log2(CPM) (RowMeans > 1)
prcomp_res3 <- prcomp(t(filcpm_matrix2 %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res3, data = Metadata, colour = "Condition", shape = "Time", size =4, x=1, y=2) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >1)")) +
theme_bw()Warning: ggrepel: 60 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 773671b | sayanpaul01 | 2025-02-01 |
prcomp_res3 <- prcomp(t(filcpm_matrix2 %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res3, data = Metadata, colour = "Condition", shape = "Time", size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >1)")) +
theme_bw()Warning: ggrepel: 31 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 773671b | sayanpaul01 | 2025-02-01 |
prcomp_res3 <- prcomp(t(filcpm_matrix2 %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res3, data = Metadata, colour = "Condition", shape = "Time", size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >1)")) +
theme_bw()Warning: ggrepel: 16 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| 773671b | sayanpaul01 | 2025-02-01 |
📌 PCA Analysis by Drugs
📌 PCA Analysis: CX-5461 & VEH
selected_columns <- grepl("VEH|CX.5461", colnames(matrix))
subset_matrix_CX <- matrix[, selected_columns]
subset_meta <- subset(Metadata, Metadata$Drug %in% c("VEH", "CX-5461"))
prcomp_res4 <- prcomp(t(subset_matrix_CX), center = TRUE)
ggplot2::autoplot(prcomp_res4, data = as.data.frame(subset_meta), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta$Ind) + # ✅ Corrected label
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Unfiltered (CX-5461 vs VEH)")) +
theme_bw()Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res5 <- prcomp(t(subset_matrix_CX[rowMeans(subset_matrix_CX) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res5, data = as.data.frame(subset_meta), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (CX-5461 vs VEH)")) +
theme_bw()Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res6 <- prcomp(t(subset_matrix_CX[rowMeans(subset_matrix_CX) > 0.5, ]), center = TRUE)
ggplot2::autoplot(prcomp_res6, data = as.data.frame(subset_meta), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0.5 (CX-5461 vs VEH)")) +
theme_bw()Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res7 <- prcomp(t(subset_matrix_CX[rowMeans(subset_matrix_CX) > 1, ]), center = TRUE)
ggplot2::autoplot(prcomp_res7, data = as.data.frame(subset_meta), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 1 (CX-5461 vs VEH)")) +
theme_bw()
📌 PCA Analysis: DOX & VEH
selected_columns <- grepl("VEH|DOX", colnames(matrix))
subset_matrix_DOX <- matrix[, selected_columns]
subset_meta_dox <- subset(Metadata, Metadata$Drug %in% c("VEH", "DOX"))
prcomp_res8 <- prcomp(t(subset_matrix_DOX), center = TRUE)
ggplot2::autoplot(prcomp_res8, data = as.data.frame(subset_meta_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Unfiltered (DOX vs VEH)")) +
theme_bw()Warning: ggrepel: 42 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res9 <- prcomp(t(subset_matrix_DOX[rowMeans(subset_matrix_DOX) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res9, data = as.data.frame(subset_meta_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (DOX vs VEH)")) +
theme_bw()Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res10 <- prcomp(t(subset_matrix_DOX[rowMeans(subset_matrix_DOX) > 0.5, ]), center = TRUE)
ggplot2::autoplot(prcomp_res10, data = as.data.frame(subset_meta_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0.5 (DOX vs VEH)")) +
theme_bw()Warning: ggrepel: 30 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res11 <- prcomp(t(subset_matrix_DOX[rowMeans(subset_matrix_DOX) > 1, ]), center = TRUE)
ggplot2::autoplot(prcomp_res11, data = as.data.frame(subset_meta_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 1 (DOX vs VEH)")) +
theme_bw()Warning: ggrepel: 34 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
📌 PCA Analysis: CX-5461 & DOX
selected_columns <- grepl("CX.5461|DOX", colnames(matrix))
subset_matrix_CX_DOX <- matrix[, selected_columns]
subset_meta_cx_dox <- subset(Metadata, Metadata$Drug %in% c("CX-5461", "DOX"))
prcomp_res12 <- prcomp(t(subset_matrix_CX_DOX), center = TRUE)
ggplot2::autoplot(prcomp_res12, data = as.data.frame(subset_meta_cx_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_cx_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Unfiltered (CX-5461 vs DOX)")) +
theme_bw()Warning: ggrepel: 30 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res13 <- prcomp(t(subset_matrix_CX_DOX[rowMeans(subset_matrix_CX_DOX) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res13, data = as.data.frame(subset_meta_cx_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_cx_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (CX-5461 vs DOX)")) +
theme_bw()Warning: ggrepel: 16 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res14 <- prcomp(t(subset_matrix_CX_DOX[rowMeans(subset_matrix_CX_DOX) > 0.5, ]), center = TRUE)
ggplot2::autoplot(prcomp_res14, data = as.data.frame(subset_meta_cx_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_cx_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0.5 (CX-5461 vs DOX)")) +
theme_bw()Warning: ggrepel: 15 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res15 <- prcomp(t(subset_matrix_CX_DOX[rowMeans(subset_matrix_CX_DOX) > 1, ]), center = TRUE)
ggplot2::autoplot(prcomp_res15, data = as.data.frame(subset_meta_cx_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_cx_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 1 (CX-5461 vs DOX)")) +
theme_bw()Warning: ggrepel: 21 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
📌 PCA Analysis by Timepoints
📌 3-Hour Timepoint
selected_columns <- grepl("_3", colnames(matrix))
subset_matrix_3hr <- matrix[, selected_columns]
subset_meta_3hr <- subset(Metadata, Metadata$Time == 3)
prcomp_res_3hr <- prcomp(t(subset_matrix_3hr[rowMeans(subset_matrix_3hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_3hr, data = as.data.frame(subset_meta_3hr), colour = "Condition", shape = "Drug", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_3hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (3 Hours)")) +
theme_bw()
prcomp_res_3hr <- prcomp(t(subset_matrix_3hr[rowMeans(subset_matrix_3hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_3hr, data = as.data.frame(subset_meta_3hr), colour = "Condition", shape = "Drug", size = 4, x=2, y=3) +
ggrepel::geom_text_repel(label = subset_meta_3hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (3 Hours)")) +
theme_bw()
prcomp_res_3hr <- prcomp(t(subset_matrix_3hr[rowMeans(subset_matrix_3hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_3hr, data = as.data.frame(subset_meta_3hr), colour = "Condition", shape = "Drug", size = 4, x=3, y=4) +
ggrepel::geom_text_repel(label = subset_meta_3hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (3 Hours)")) +
theme_bw()
📌 24-Hour Timepoint
selected_columns <- grepl("_24", colnames(matrix))
subset_matrix_24hr <- matrix[, selected_columns]
subset_meta_24hr <- subset(Metadata, Metadata$Time == 24)
prcomp_res_24hr <- prcomp(t(subset_matrix_24hr[rowMeans(subset_matrix_24hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_24hr, data = as.data.frame(subset_meta_24hr), colour = "Condition", shape = "Drug", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_24hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (24 Hours)")) +
theme_bw()
prcomp_res_24hr <- prcomp(t(subset_matrix_24hr[rowMeans(subset_matrix_24hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_24hr, data = as.data.frame(subset_meta_24hr), colour = "Condition", shape = "Drug", size = 4, x=2, y=3) +
ggrepel::geom_text_repel(label = subset_meta_24hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (24 Hours)")) +
theme_bw()
prcomp_res_24hr <- prcomp(t(subset_matrix_24hr[rowMeans(subset_matrix_24hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_24hr, data = as.data.frame(subset_meta_24hr), colour = "Condition", shape = "Drug", size = 4, x=3, y=4) +
ggrepel::geom_text_repel(label = subset_meta_24hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (24 Hours)")) +
theme_bw()
📌 48-Hour Timepoint
selected_columns <- grepl("_48", colnames(matrix))
subset_matrix_48hr <- matrix[, selected_columns]
subset_meta_48hr <- subset(Metadata, Metadata$Time == 48)
prcomp_res_48hr_1 <- prcomp(t(subset_matrix_48hr[rowMeans(subset_matrix_48hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_48hr_1, data = as.data.frame(subset_meta_48hr), colour = "Condition", shape = "Drug", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_48hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (48 Hours)")) +
theme_bw()Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res_48hr_1 <- prcomp(t(subset_matrix_48hr[rowMeans(subset_matrix_48hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_48hr_1, data = as.data.frame(subset_meta_48hr), colour = "Condition", shape = "Drug", size = 4, x=2, y=3) +
ggrepel::geom_text_repel(label = subset_meta_48hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (48 Hours)")) +
theme_bw()
prcomp_res_48hr_1 <- prcomp(t(subset_matrix_48hr[rowMeans(subset_matrix_48hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_48hr_1, data = as.data.frame(subset_meta_48hr), colour = "Condition", shape = "Drug", size = 4, x=3, y=4) +
ggrepel::geom_text_repel(label = subset_meta_48hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (48 Hours)")) +
theme_bw()
📌 PCA Analysis by Concentrations
📌0.1 µM Concentration
selected_columns <- grepl("_0.1_", colnames(matrix))
subset_matrix_0.1 <- matrix[, selected_columns]
subset_meta_0.1 <- subset(Metadata, Metadata$Conc. == 0.1)
prcomp_res_0.1 <- prcomp(t(subset_matrix_0.1[rowMeans(subset_matrix_0.1) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_0.1, data = as.data.frame(subset_meta_0.1), colour = "Drug", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_0.1$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (0.1 µM)")) +
theme_bw()
prcomp_res_0.1 <- prcomp(t(subset_matrix_0.1[rowMeans(subset_matrix_0.1) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_0.1, data = as.data.frame(subset_meta_0.1), colour = "Drug", shape = "Time", size = 4, x=2, y=3) +
ggrepel::geom_text_repel(label = subset_meta_0.1$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (0.1 µM)")) +
theme_bw()
prcomp_res_0.1 <- prcomp(t(subset_matrix_0.1[rowMeans(subset_matrix_0.1) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_0.1, data = as.data.frame(subset_meta_0.1), colour = "Drug", shape = "Time", size = 4, x=3, y=4) +
ggrepel::geom_text_repel(label = subset_meta_0.1$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (0.1 µM)")) +
theme_bw()
📌0.5 µM Concentration
selected_columns <- grepl("_0.5_", colnames(matrix))
subset_matrix_0.5 <- matrix[, selected_columns]
subset_meta_0.5 <- subset(Metadata, Metadata$Conc. == 0.5)
prcomp_res_0.5 <- prcomp(t(subset_matrix_0.5[rowMeans(subset_matrix_0.5) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_0.5, data = as.data.frame(subset_meta_0.5), colour = "Drug", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_0.5$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (0.5 µM)")) +
theme_bw()Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res_0.5 <- prcomp(t(subset_matrix_0.5[rowMeans(subset_matrix_0.5) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_0.5, data = as.data.frame(subset_meta_0.5), colour = "Drug", shape = "Time", size = 4, x=2, y=3) +
ggrepel::geom_text_repel(label = subset_meta_0.5$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (0.5 µM)")) +
theme_bw()
prcomp_res_0.5 <- prcomp(t(subset_matrix_0.5[rowMeans(subset_matrix_0.5) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_0.5, data = as.data.frame(subset_meta_0.5), colour = "Drug", shape = "Time", size = 4, x=3, y=4) +
ggrepel::geom_text_repel(label = subset_meta_0.5$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (0.5 µM)")) +
theme_bw()
sessionInfo()R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22631)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggfortify_0.4.17 org.Hs.eg.db_3.18.0 AnnotationDbi_1.64.1
[4] IRanges_2.36.0 S4Vectors_0.40.1 Hmisc_5.2-0
[7] corrplot_0.95 ggrepel_0.9.6 biomaRt_2.58.2
[10] scales_1.3.0 lubridate_1.9.3 forcats_1.0.0
[13] stringr_1.5.1 purrr_1.0.2 readr_2.1.5
[16] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
[19] Biobase_2.62.0 BiocGenerics_0.48.1 dplyr_1.1.4
[22] reshape2_1.4.4 ggplot2_3.5.1 edgeR_4.0.1
[25] limma_3.58.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] DBI_1.2.3 bitops_1.0-7 gridExtra_2.3
[4] rlang_1.1.3 magrittr_2.0.3 git2r_0.35.0
[7] compiler_4.3.0 RSQLite_2.3.3 getPass_0.2-4
[10] png_0.1-8 callr_3.7.6 vctrs_0.6.5
[13] pkgconfig_2.0.3 crayon_1.5.3 fastmap_1.1.1
[16] backports_1.5.0 dbplyr_2.5.0 XVector_0.42.0
[19] labeling_0.4.3 promises_1.3.0 rmarkdown_2.29
[22] tzdb_0.4.0 ps_1.8.1 bit_4.0.5
[25] xfun_0.50 zlibbioc_1.48.0 cachem_1.0.8
[28] GenomeInfoDb_1.38.8 jsonlite_1.8.9 progress_1.2.3
[31] blob_1.2.4 later_1.3.2 prettyunits_1.2.0
[34] cluster_2.1.6 R6_2.5.1 bslib_0.8.0
[37] stringi_1.8.3 rpart_4.1.23 jquerylib_0.1.4
[40] Rcpp_1.0.12 knitr_1.49 base64enc_0.1-3
[43] httpuv_1.6.15 nnet_7.3-19 timechange_0.3.0
[46] tidyselect_1.2.1 rstudioapi_0.17.1 yaml_2.3.10
[49] curl_6.0.1 processx_3.8.5 lattice_0.22-5
[52] plyr_1.8.9 withr_3.0.2 KEGGREST_1.42.0
[55] evaluate_1.0.3 foreign_0.8-87 BiocFileCache_2.10.2
[58] xml2_1.3.6 Biostrings_2.70.1 pillar_1.10.1
[61] filelock_1.0.3 whisker_0.4.1 checkmate_2.3.2
[64] generics_0.1.3 rprojroot_2.0.4 RCurl_1.98-1.13
[67] hms_1.1.3 munsell_0.5.1 glue_1.7.0
[70] tools_4.3.0 data.table_1.14.10 locfit_1.5-9.8
[73] fs_1.6.3 XML_3.99-0.17 grid_4.3.0
[76] colorspace_2.1-0 GenomeInfoDbData_1.2.11 htmlTable_2.4.3
[79] Formula_1.2-5 cli_3.6.1 rappdirs_0.3.3
[82] gtable_0.3.6 sass_0.4.9 digest_0.6.34
[85] farver_2.1.2 htmlwidgets_1.6.4 memoise_2.0.1
[88] htmltools_0.5.8.1 lifecycle_1.0.4 httr_1.4.7
[91] statmod_1.5.0 bit64_4.0.5