Last updated: 2023-06-12

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Rmd ebb85a5 FloWuenne 2023-06-12 Updated organization for proteomics data analysis.

Introduction

Here we will process and analyze the data generated via laser-microdissection coupled with high-sensitiviy proteomics of the endocardial layer from control hearts and hearts one day after myocardial infarction. We have a total of 3 groups in this dataset, all representing laser microdissected regions of endocardium from mouse hearts: 1) control = Mouse hearts without infarct 2) MI_IZ = Mouse hearts 1 day post MI. Endocardial region adjacent to the infarct. 3) MI_remote = Mouse hearts 1 day post MI. Endocardial region remote to the infarct.

Load data

prot_res <- fread("./data/proteomics_endocardial_layer.tsv")
metadata <- fread("./data/metadata.proteomics_endocardial_layer.txt")

Analysis

Filter out proteins that are identified in < 3 MI samples

## Exclude proteins that are only identified in a subset of samples per group
## For controls, we expect a protein to be measured in at least 2/3 samples (33% missingness allowed)
## For MI samples, we expect a protein to be measured in at least 2/4 samples (50% missingness allowed)
na_mi <- prot_res %>%
  pivot_longer(control_r1:MI_remote_r4,
               names_to = "sample",
               values_to = "protein_exp") %>%
  mutate("group" = if_else(grepl("control",sample),"control",
                           if_else(grepl("MI_IZ",sample),"MI_IZ","MI_remote"))) %>%
  group_by(Protein_Ids,Genes,group) %>%
  summarise(na_count = sum(is.na(protein_exp))) %>%
  ungroup() %>%
  mutate("percent_na" = if_else(group == "control",na_count / 3, na_count / 4)) %>%
  select(-na_count) %>% ## control: n = 3 and MI: n = 4
  pivot_wider(names_from = "group",values_from = percent_na) %>%
  mutate("retain" = if_else(control <= 0.3 | MI_remote <= 0.25 | MI_IZ <= 0.25, "yes","no"))
`summarise()` has grouped output by 'Protein_Ids', 'Genes'. You can override
using the `.groups` argument.
prot_res_filt <- subset(prot_res,Protein_Ids %in% subset(na_mi,retain == "yes")$Protein_Ids)

Merge metadata with protein counts per sample

## Count number of detected proteins with contaminants
prot_res_quant_cont <- prot_res_filt %>%
  select(control_r1:MI_remote_r4) %>%
  summarise_all(funs(sum(!is.na(.))))
Warning: `funs()` was deprecated in dplyr 0.8.0.
ℹ Please use a list of either functions or lambdas:

# Simple named list: list(mean = mean, median = median)

# Auto named with `tibble::lst()`: tibble::lst(mean, median)

# Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
## Count number of detected proteins without contaminants
prot_res_filt <- prot_res_filt %>%
  mutate("contaminant" = if_else(grepl("Cont",Protein_Ids),"yes","no"))

prot_res_quant <-  prot_res_filt %>%
  subset(contaminant == "no") %>%
  select(control_r1:MI_remote_r4) %>%
  summarise_all(funs(sum(!is.na(.))))
Warning: `funs()` was deprecated in dplyr 0.8.0.
ℹ Please use a list of either functions or lambdas:

# Simple named list: list(mean = mean, median = median)

# Auto named with `tibble::lst()`: tibble::lst(mean, median)

# Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
final_counts <- t(rbind(prot_res_quant_cont,
                      prot_res_quant))
colnames(final_counts) <- c("w_cont","wo_cont")
final_counts <- as.data.frame(final_counts) %>%
  mutate("sample" = rownames(final_counts))

## Add protein counts to metadata
metadata_counts <- left_join(metadata,final_counts, by = "sample")

## Add group to counts
metadata_counts <- metadata_counts %>%
  mutate("group" = if_else(grepl("control",sample),"control",
                           if_else(grepl("MI_IZ",sample),"MI_IZ","MI_remote")))

## Set order of groups
metadata_counts$group <- factor(metadata_counts$group,
                                levels = c("control","MI_remote","MI_IZ"))

## Plot number of proteins without contaminants per sample
ggbarplot(metadata_counts,
          x = "group",
          y = "wo_cont",
          fill = "group",
          add = c("mean_se"),
          error.plot = "upper_errorbar",
          color = "black",
          palette = "npg") +
  theme_minimal_hgrid()  +
  labs (x = "Treatment group",
         y = "# Proteins identified") +
  font("xlab", size = 16, color = "black", face = "bold") +
  font("ylab", size = 16, color = "black", face = "bold") +
  scale_y_continuous(
    expand = expansion(mult = c(0, 0.05))
  ) +
  rremove("legend")

prot_res_final <- subset(prot_res_filt,contaminant == "no") %>%
  select(-c(contaminant,First_Protein_Description))

Process protein counts

## Impute missing values, similar as to

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] here_1.0.1        cowplot_1.1.1     ggpubr_0.6.0      data.table_1.14.8
 [5] lubridate_1.9.2   forcats_1.0.0     stringr_1.5.0     dplyr_1.1.2      
 [9] purrr_1.0.1       readr_2.1.4       tidyr_1.3.0       tibble_3.2.1     
[13] ggplot2_3.4.2     tidyverse_2.0.0   workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.10      getPass_0.2-2    ps_1.7.4         rprojroot_2.0.3 
 [5] digest_0.6.31    utf8_1.2.3       R6_2.5.1         backports_1.4.1 
 [9] evaluate_0.21    highr_0.10       httr_1.4.6       pillar_1.9.0    
[13] rlang_1.1.1      rstudioapi_0.14  whisker_0.4.1    car_3.1-2       
[17] callr_3.7.3      jquerylib_0.1.4  rmarkdown_2.21   labeling_0.4.2  
[21] munsell_0.5.0    broom_1.0.5      compiler_4.2.3   httpuv_1.6.11   
[25] xfun_0.39        pkgconfig_2.0.3  htmltools_0.5.5  tidyselect_1.2.0
[29] fansi_1.0.4      crayon_1.5.2     tzdb_0.4.0       withr_2.5.0     
[33] later_1.3.1      grid_4.2.3       jsonlite_1.8.4   gtable_0.3.3    
[37] lifecycle_1.0.3  git2r_0.32.0     magrittr_2.0.3   scales_1.2.1    
[41] cli_3.6.1        stringi_1.7.12   cachem_1.0.8     carData_3.0-5   
[45] farver_2.1.1     renv_0.17.3      ggsignif_0.6.4   fs_1.6.2        
[49] promises_1.2.0.1 bslib_0.4.2      generics_0.1.3   vctrs_0.6.2     
[53] ggsci_3.0.0      tools_4.2.3      glue_1.6.2       hms_1.1.3       
[57] processx_3.8.0   abind_1.4-5      fastmap_1.1.1    yaml_2.3.7      
[61] timechange_0.2.0 colorspace_2.1-0 rstatix_0.7.2    knitr_1.42      
[65] sass_0.4.6