Last updated: 2023-06-14

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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

First, let’s load the raw data and the associated metadata for all samples.

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

Analysis

Calculate missingness per sample (# NA values)

As a first QC, we will calculate the amount of missing data (NA values) per sample in the proteomics data.

missing <- colSums(is.na(prot_res))[6:ncol(prot_res)]
non_missing <-colSums(!is.na(prot_res))[6:ncol(prot_res)]
missingness <- missing / (non_missing + missing)
missingness <- as.data.frame(missingness)
missingness$sample <- rownames(missingness)

## Add group to counts
missingness <- missingness %>%
  mutate("group" = if_else(grepl("control",sample),"control",
                           if_else(grepl("MI_IZ",sample),"MI_IZ","MI_remote")))
## Set order of groups
missingness$group <- factor(missingness$group,
                                levels = c("control","MI_remote","MI_IZ"))

avg_missingness <- mean(missingness$missingness)

ggplot(missingness,aes(sample,missingness,fill = group)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_fill_npg() +
  labs(x = "Samples",
       y = "% missing values") +
  geom_hline(yintercept = avg_missingness, linetype = 2)

Version Author Date
2d015dd FloWuenne 2023-06-14

Filter out proteins that are identified in < 3 MI samples

Then we are going to calculate the missingness per sample group (the treatment group and area that was excised by laser microdissection) and 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)

Exclude contaminants (keratins etc.)

After filtering based on missingness, we will filter out any proteions that are considered contaminants based on know contaminations based on Frankenfeld 2022

## 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))

Merge metadata with protein counts per sample

Here, we will plot the number of proteins identified per group.

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")

Version Author Date
2d015dd FloWuenne 2023-06-14

Create final filtered protein table

Finally, we will create a final filtered protein table, that we will use for imputation.

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

write.table(prot_res_final,
            file = "./output/proteomics.filtered_proteins.tsv",
            sep = "\t",
            col.names = TRUE,
            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] ggsci_3.0.0       here_1.0.1        cowplot_1.1.1     ggpubr_0.6.0     
 [5] data.table_1.14.8 lubridate_1.9.2   forcats_1.0.0     stringr_1.5.0    
 [9] dplyr_1.1.2       purrr_1.0.1       readr_2.1.4       tidyr_1.3.0      
[13] tibble_3.2.1      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] tools_4.2.3      glue_1.6.2       hms_1.1.3        processx_3.8.0  
[57] abind_1.4-5      fastmap_1.1.1    yaml_2.3.7       timechange_0.2.0
[61] colorspace_2.1-0 rstatix_0.7.2    knitr_1.42       sass_0.4.6