proteomics.bulk_de_analysis
FloWuenne
2023-06-14
Last updated: 2023-06-20
Checks: 7 0
Knit directory: mi_spatialomics/
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| File | Version | Author | Date | Message |
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| Rmd | 8493a79 | FloWuenne | 2023-06-20 | Saving intermediate analysis for proteomics. |
| html | 8493a79 | FloWuenne | 2023-06-20 | Saving intermediate analysis for proteomics. |
library(data.table)
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.1
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(factoextra)Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(ggsci)
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:lubridate':
stamplibrary(here)here() starts at /Users/florian_wuennemann/1_Projects/MI_project/mi_spatialomicslibrary(proDA)
library(limma)
library(dplyr)
library(tibble)
library(tidyr)
library(ggplot2)
library(pheatmap)
library(ggrepel)
library(eulerr)
source(here("./code/params_R.R"))Introduction
Load data
First, let’s load the filtered protein table.
## Import imputed protein table (our "bulk" data)
clean_proteins <- fread(file = "./output/proteomics.filtered_proteins.tsv")Principal component analysis of proteomic samples
As a fist QC, we want to look at a principal component embedding of the samples in the first two principle components. Due to the strong perturbation effect of a myocardial infarction, we expect to find samples distributed by group (control, MI_remote, MI_IZ) in the first principle component.
clean_proteins_mat <- clean_proteins %>%
select(-c(Protein_Group,Protein_Names,Protein_Ids,Genes))%>%
drop_na()
## Calculate principal components and plot the firs two PCs
res.pca <- prcomp(t(log2(clean_proteins_mat)), scale = TRUE)
fviz_eig(res.pca)
| Version | Author | Date |
|---|---|---|
| 8493a79 | FloWuenne | 2023-06-20 |
pcs <- as.data.frame(res.pca$x)
pcs$sample <- colnames(clean_proteins_mat)
pcs <- pcs %>%
mutate("group" = if_else(grepl("control",sample),"control",
if_else(grepl("MI_IZ",sample),"MI_IZ","MI_remote"))
)
## Set order of groups
pcs$group <- factor(pcs$group,
levels = c("control","MI_remote","MI_IZ"))
## Plot PCs
ggplot(pcs,aes(PC1,PC2)) +
geom_point(size = 5,pch = 21,color = "black", aes(fill = group)) +
scale_fill_npg(labels = c("Control","MI_remote","MI_IZ")) +
labs(color = "Group") +
guides(fill=guide_legend(title="Treatment group"))
| Version | Author | Date |
|---|---|---|
| 8493a79 | FloWuenne | 2023-06-20 |
As we expected, samples separate in PC1, based on their group category, confirming that the strongest effect between samples is whether the heart experienced an infarct and the location of the endocardial cells relative to the infarct zone.
DEPs using proDA
## First we format the protein matrix for proDA
protein_ids <- clean_proteins$Protein_Ids
gene_mapping <- clean_proteins %>%
select(Protein_Ids,Genes)
abundance_matrix <- clean_proteins %>%
select(-c(Protein_Group,Protein_Ids,Protein_Names,Genes))
abundance_matrix <- log2(abundance_matrix)
rownames(abundance_matrix) <- protein_idsbarplot(colSums(is.na(abundance_matrix)),
ylab = "# missing values",
xlab = "Sample 1 to 36")
| Version | Author | Date |
|---|---|---|
| 8493a79 | FloWuenne | 2023-06-20 |
boxplot(abundance_matrix,
ylab = "Intensity Distribution",
xlab = "Sample 1 to 36")
| Version | Author | Date |
|---|---|---|
| 8493a79 | FloWuenne | 2023-06-20 |
normalized_abundance_matrix <- median_normalization(as.matrix(abundance_matrix))da <- dist_approx(normalized_abundance_matrix)plot_mat <- as.matrix(da$mean)
pheatmap::pheatmap(plot_mat)
| Version | Author | Date |
|---|---|---|
| 8493a79 | FloWuenne | 2023-06-20 |
design_matrix <- pcs %>%
select(sample,group)
control_mi_cont <- c(0,0,0,1,1,1,1)
iz_remote_cont <- c(0,0,0,0,1,1,1,1)
## column numbers of different groups
control_idx <- c(1,2,3)
mi_iz_idx <- c(4,5,6,7)
mi_remote_idx <- c(8,9,10,11)run proDA model fit
fit_full <- proDA(normalized_abundance_matrix, design = ~ group,
col_data = design_matrix, reference_level = "control")MI_IZ vs control
test_res_miiz <- test_diff(fit_full, "groupMI_IZ", pval_adjust_method = "fdr")
test_res_miiz$gene <- gene_mapping$Genes
test_res_miiz$protein_ids <- gene_mapping$Protein_Ids
test_res_miiz <- test_res_miiz %>%
arrange(adj_pval)
volc_test_miiz <- ggplot(data=test_res_miiz, aes(x=diff, y=-log10(pval), label = gene)) +
geom_point(aes(color = as.factor(n_obs))) +
theme_minimal() +
facet_wrap(~ n_obs)
volc_test_miiz
| Version | Author | Date |
|---|---|---|
| 8493a79 | FloWuenne | 2023-06-20 |
MI_remote vs control
test_res_miremote <- test_diff(fit_full, "groupMI_remote", pval_adjust_method = "fdr")
test_res_miremote$gene <- gene_mapping$Genes
test_res_miremote$protein_ids <- gene_mapping$Protein_Ids
test_res_miremote <- test_res_miremote %>%
arrange(adj_pval)
volc_test_miiz <- ggplot(data=test_res_miremote, aes(x=diff, y=-log10(pval), label = gene)) +
geom_point(aes(color = as.factor(n_obs))) +
theme_minimal() +
facet_wrap(~ n_obs)
volc_test_miiz
| Version | Author | Date |
|---|---|---|
| 8493a79 | FloWuenne | 2023-06-20 |
IZ vs remote
normalized_abundance_matrix_sub <- normalized_abundance_matrix[,c(mi_iz_idx,mi_remote_idx)]
fit_iz_remote <- proDA(normalized_abundance_matrix_sub, design = as.factor(c("MI_IZ","MI_IZ","MI_IZ","MI_IZ","MI_remote","MI_remote","MI_remote","MI_remote")))test_res_iz_remote <- test_diff(fit_iz_remote, MI_IZ - MI_remote, pval_adjust_method = "fdr")
test_res_iz_remote$gene <- gene_mapping$Genes
test_res_iz_remote$protein_ids <- gene_mapping$Protein_Ids
test_res_iz_remote <- test_res_iz_remote %>%
arrange(adj_pval)
volc_test_iz_remote <- ggplot(data=test_res_iz_remote, aes(x=diff, y=-log10(pval), label = gene)) +
geom_point(aes(color = as.factor(n_obs))) +
theme_minimal() +
facet_wrap(~ n_obs)
volc_test_iz_remote
| Version | Author | Date |
|---|---|---|
| 8493a79 | FloWuenne | 2023-06-20 |
Venn Diagram of DE genes
test_res_miiz_genes <- subset(test_res_miiz,adj_pval <= 0.1)$gene
test_res_miremote_genes <- subset(test_res_miremote,adj_pval <= 0.1)$gene
test_res_iz_remote_genes <- subset(test_res_iz_remote,adj_pval <= 0.1)$gene
venn_vec <- test_res_miremote$gene
venn_df <- data.frame("MI_control" = venn_vec %in% test_res_miiz_genes,
"remote_control" = venn_vec %in% test_res_miremote_genes,
"MI_IZ_MI_remote" = venn_vec %in% test_res_iz_remote_genes)
venn_df$gene <- venn_vec
## Plot the 3 group Venn diagram
plot(venn(venn_df[,1:3]))
| Version | Author | Date |
|---|---|---|
| 8493a79 | FloWuenne | 2023-06-20 |
## get proteins that are deferentially expressed between MI vs control and MI_IZ and MI_remote
MI_IZ_proteins <- subset(venn_df,MI_control == TRUE & remote_control == FALSE & MI_IZ_MI_remote == TRUE)$gene
test_res_iz_remote_uniq <- subset(test_res_iz_remote,gene %in% MI_IZ_proteins)write.table(test_res_miiz_genes,
file = "./output/mi_iz_specific_proteins.tsv",
col.names = FALSE,
row.names = FALSE,
quote = FALSE)
sessionInfo()R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] eulerr_7.0.0 ggrepel_0.9.3 pheatmap_1.0.12 limma_3.54.2
[5] proDA_1.13.0 here_1.0.1 cowplot_1.1.1 ggsci_3.0.0
[9] factoextra_1.0.7 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[13] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[17] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0 data.table_1.14.8
[21] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] bitops_1.0-7 matrixStats_1.0.0
[3] fs_1.6.2 RColorBrewer_1.1-3
[5] httr_1.4.6 rprojroot_2.0.3
[7] GenomeInfoDb_1.34.9 backports_1.4.1
[9] tools_4.2.3 bslib_0.4.2
[11] utf8_1.2.3 R6_2.5.1
[13] BiocGenerics_0.44.0 colorspace_2.1-0
[15] withr_2.5.0 tidyselect_1.2.0
[17] processx_3.8.0 compiler_4.2.3
[19] git2r_0.32.0 cli_3.6.1
[21] Biobase_2.58.0 DelayedArray_0.24.0
[23] labeling_0.4.2 sass_0.4.6
[25] scales_1.2.1 callr_3.7.3
[27] digest_0.6.31 rmarkdown_2.21
[29] extraDistr_1.9.1 XVector_0.38.0
[31] pkgconfig_2.0.3 htmltools_0.5.5
[33] MatrixGenerics_1.10.0 highr_0.10
[35] fastmap_1.1.1 rlang_1.1.1
[37] rstudioapi_0.14 farver_2.1.1
[39] jquerylib_0.1.4 generics_0.1.3
[41] jsonlite_1.8.4 car_3.1-2
[43] RCurl_1.98-1.12 magrittr_2.0.3
[45] GenomeInfoDbData_1.2.9 Matrix_1.5-3
[47] Rcpp_1.0.10 munsell_0.5.0
[49] S4Vectors_0.36.2 fansi_1.0.4
[51] abind_1.4-5 lifecycle_1.0.3
[53] stringi_1.7.12 whisker_0.4.1
[55] yaml_2.3.7 carData_3.0-5
[57] SummarizedExperiment_1.28.0 zlibbioc_1.44.0
[59] grid_4.2.3 promises_1.2.0.1
[61] lattice_0.20-45 hms_1.1.3
[63] polylabelr_0.2.0 knitr_1.42
[65] ps_1.7.4 pillar_1.9.0
[67] ggpubr_0.6.0 GenomicRanges_1.50.2
[69] ggsignif_0.6.4 stats4_4.2.3
[71] glue_1.6.2 evaluate_0.21
[73] getPass_0.2-2 renv_0.17.3
[75] BiocManager_1.30.21 vctrs_0.6.2
[77] tzdb_0.4.0 httpuv_1.6.11
[79] polyclip_1.10-4 gtable_0.3.3
[81] cachem_1.0.8 xfun_0.39
[83] broom_1.0.5 rstatix_0.7.2
[85] later_1.3.1 IRanges_2.32.0
[87] timechange_0.2.0