Deep Visual Proteomics - Filter proteomics data
Florian Wuennemann
Last updated: 2024-03-21
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Knit directory: mi_spatialomics/
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| Rmd | c1350a5 | FloWuenne | 2024-03-07 | Updated proteomic analsysi and several plots for molkart. |
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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$missing_prot <- missing
missingness$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 = rev(c("control","MI_remote","MI_IZ")))
avg_missingness <- mean(missingness$missingness)
ggplot(missingness,aes(group,missingness,fill = group)) +
geom_violin(color = "black") +
geom_point(fill = "white",pch = 21, color= "black",size = 4) +
coord_flip() +
scale_fill_manual(values = proteome_palette,
labels = c("Control","MI_remote","MI_IZ")) +
labs(x = "Samples",
y = "% missing values") +
geom_hline(yintercept = avg_missingness, linetype = 2)
Filter out proteins that are identified in < 3 MI samples
Then we are going to calculate the missingness per sample group (the treatment group and area that was excised by laser microdissection) and exclude proteins that are only identified in a subset of samples per group. For controls, we expect a protein to be measured in at least 2/3 samples (33% missingness allowed). For MI samples, we expect a protein to be measured in at least 3/4 samples (25% missingness allowed)
na_mi <- prot_res %>%
pivot_longer(control_r1:MI_remote_r4,
names_to = "sample",
values_to = "protein_exp") %>%
mutate("group" = if_else(grepl("control",sample),"control",
if_else(grepl("MI_IZ",sample),"MI_IZ","MI_remote"))) %>%
group_by(Protein_Ids,Genes,group) %>%
summarise(na_count = sum(is.na(protein_exp))) %>%
ungroup() %>%
mutate("percent_na" = if_else(group == "control",na_count / 3, na_count / 4)) %>%
select(-na_count) %>% ## control: n = 3 and MI: n = 4
pivot_wider(names_from = "group",values_from = percent_na) %>%
mutate("retain" = if_else(control <= 0.3 | MI_remote <= 0.25 | MI_IZ <= 0.25, "yes","no"))`summarise()` has grouped output by 'Protein_Ids', 'Genes'. You can override
using the `.groups` argument.prot_res_filt <- subset(prot_res,Protein_Ids %in% subset(na_mi,retain == "yes")$Protein_Ids)Exclude contaminants (keratins etc.)
After filtering based on missingness, we will filter out any proteins that are considered contaminants 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.Merge metadata with protein counts per sample
Here, we will plot the number of proteins identified per group.
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")))
## Add missingness to table
metadata_counts <- left_join(metadata_counts,missingness, by = c("sample","group"))
## Set order of groups
metadata_counts$group <- factor(metadata_counts$group,
levels = rev(c("control","MI_remote","MI_IZ")))
## Summarize observations across group
metadata_counts_stat <- data_summary(metadata_counts,
varname = "wo_cont", groupnames = c("group"))Loading required package: plyr------------------------------------------------------------------------------You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)------------------------------------------------------------------------------
Attaching package: 'plyr'The following object is masked from 'package:here':
hereThe following object is masked from 'package:ggpubr':
mutateThe following objects are masked from 'package:dplyr':
arrange, count, desc, failwith, id, mutate, rename, summarise,
summarizeThe following object is masked from 'package:purrr':
compact## Barplot to show the number of proteins detected per group (excluding contaminants)
ggplot(metadata_counts_stat,aes(group,wo_cont)) +
geom_bar(aes(fill = group),stat="identity", color="black",
position=position_dodge()) +
geom_point(data = metadata_counts, aes(group,wo_cont),
fill = "white",pch = 21, color= "black",size = 5) +
geom_errorbar(aes(ymin=wo_cont, ymax=wo_cont+sd), width=.2,
position=position_dodge(.9)) +
coord_flip() +
scale_fill_manual(values = proteome_palette,
labels = c("Control","MI_remote","MI_IZ")) +
labs(x = "Samples",
y = "Proteins detected") +
theme(legend.position = "none")
Save protein count and missingness stats for figures
write.table(metadata_counts,
file = "./output/proteomics/proteomics.protein_missing_stats.tsv",
sep = "\t",
col.names = TRUE,
row.names = FALSE,
quote =FALSE)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/proteomics.filtered_proteins.tsv",
sep = "\t",
col.names = TRUE,
row.names = FALSE,
quote = FALSE)
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.1.2
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Berlin
tzcode source: internal
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] plyr_1.8.9 RColorBrewer_1.1-3 ggsci_3.0.0 here_1.0.1
[5] cowplot_1.1.2 ggpubr_0.6.0 data.table_1.14.10 lubridate_1.9.3
[9] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
[13] readr_2.1.5 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
[17] tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
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[4] processx_3.8.3 rstatix_0.7.2 callr_3.7.3
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[10] ps_1.7.6 generics_0.1.3 fansi_1.0.6
[13] highr_0.10 pkgconfig_2.0.3 lifecycle_1.0.4
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[34] tidyselect_1.2.0 digest_0.6.34 stringi_1.8.3
[37] labeling_0.4.3 rprojroot_2.0.4 fastmap_1.1.1
[40] grid_4.3.1 colorspace_2.1-0 cli_3.6.2
[43] magrittr_2.0.3 utf8_1.2.4 broom_1.0.5
[46] withr_2.5.2 scales_1.3.0 promises_1.2.1
[49] backports_1.4.1 timechange_0.2.0 rmarkdown_2.25
[52] httr_1.4.7 ggsignif_0.6.4 hms_1.1.3
[55] evaluate_0.23 knitr_1.45 rlang_1.1.3
[58] Rcpp_1.0.12 glue_1.7.0 BiocManager_1.30.22
[61] renv_1.0.3 rstudioapi_0.15.0 jsonlite_1.8.8
[64] R6_2.5.1 fs_1.6.3