PCA Analysis

workflowr

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• Past versions

Last updated: 2025-04-02

Checks: 6 1

Knit directory: CX5461_Project/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20250129)

The command set.seed(20250129) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

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Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

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Repository version: 491cd15

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The results in this page were generated with repository version 491cd15. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
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    Ignored:    0.1 box.svg
    Ignored:    Rplot04.svg

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Unstaged changes:
    Modified:   .gitignore
    Modified:   analysis/PCA.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/PCA.Rmd) and HTML (docs/PCA.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	3f3d8c0	sayanpaul01	2025-02-02	Build site.
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

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Load Required Libraries

library(edgeR)

Warning: package 'edgeR' was built under R version 4.3.1

Warning: package 'limma' was built under R version 4.3.1

library(ggplot2)

Warning: package 'ggplot2' was built under R version 4.3.3

library(reshape2)
library(dplyr)

Warning: package 'dplyr' was built under R version 4.3.2

library(Biobase)

Warning: package 'Biobase' was built under R version 4.3.1

Warning: package 'BiocGenerics' was built under R version 4.3.1

library(limma)
library(tidyverse)

Warning: package 'tidyverse' was built under R version 4.3.2

Warning: package 'tidyr' was built under R version 4.3.3

Warning: package 'readr' was built under R version 4.3.3

Warning: package 'purrr' was built under R version 4.3.1

Warning: package 'stringr' was built under R version 4.3.2

Warning: package 'lubridate' was built under R version 4.3.1

library(scales)

Warning: package 'scales' was built under R version 4.3.2

library(biomaRt)

Warning: package 'biomaRt' was built under R version 4.3.2

library(ggrepel)

Warning: package 'ggrepel' was built under R version 4.3.3

library(corrplot)

Warning: package 'corrplot' was built under R version 4.3.3

library(Hmisc)

Warning: package 'Hmisc' was built under R version 4.3.3

library(org.Hs.eg.db)

Warning: package 'AnnotationDbi' was built under R version 4.3.2

Warning: package 'IRanges' was built under R version 4.3.1

Warning: package 'S4Vectors' was built under R version 4.3.1

library(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

Past versions of pca_unfiltered-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02
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

Past versions of pca_unfiltered2-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02
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

Past versions of pca_unfiltered3-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02
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 gene expression (log2 cpm)")) +
  theme_bw()

Warning: ggrepel: 51 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Past versions of pca_filtered_0-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02
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

Past versions of pca_filtered_0_1-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02
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

Past versions of pca_filtered_0_2-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02
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

Past versions of pca_filtered_0.5-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02
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

Past versions of pca_filtered_0.5_1-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02
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

Past versions of pca_filtered_0.5_2-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02
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

Past versions of pca_filtered_1-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02
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

Past versions of pca_filtered_1_1-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02
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

Past versions of pca_filtered_1_2-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02
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

Past versions of pca_drug-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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

Past versions of pca_drug_1-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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

Past versions of pca_drug_2-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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()

Past versions of pca_drug_3-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

📌 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

Past versions of pca_drug_dox_veh-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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

Past versions of pca_drug_dox_veh_1-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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

Past versions of pca_drug_dox_veh_2-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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

Past versions of pca_drug_dox_veh_3-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

📌 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

Past versions of pca_drug_cx_dox-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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

Past versions of pca_drug_cx_dox_1-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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

Past versions of pca_drug_cx_dox_2-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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

Past versions of pca_drug_cx_dox_3-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

📌 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()

Past versions of pca_3hr-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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()

Past versions of pca_3hr_2-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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()

Past versions of pca_3hr_3-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

📌 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()

Past versions of pca_24hr-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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()

Past versions of pca_24hr_1-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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()

Past versions of pca_24hr_2-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

📌 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

Past versions of pca_48hr-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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()

Past versions of pca_48hr_1-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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()

Past versions of pca_48hr_2-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

📌 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()

Past versions of pca_0.1-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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()

Past versions of pca_0.1_1-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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()

Past versions of pca_0.1_2-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

📌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

Past versions of pca_0.5-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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()

Past versions of pca_0.5_1-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

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()

Past versions of pca_0.5_2-1.png

Version	Author	Date
3f3d8c0	sayanpaul01	2025-02-02

Session information

sessionInfo()

R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 26100)

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