1 Evaluate Previous Result: version: 20171205
1.1 Reading in the existing result
As per Yan’s suggestion, I am using the existing results, found in the file: “2017_0811BrikenMultiConsensus.xlsx”
Unfortunately, this table is in a particularly strange format: it is a triply nested table of tables of tables in one worksheet in Excel. At one level, this is a really novel and interesting use of Excel, but on every other level it is sub-optimal. The primary reason it is sub-optimal is that the column headings end up being repeated once for every new sub-table (874 of the inner tables plus however many middle tables), which makes it nearly unparseable.
I am displeased I need to do this level of data wrangling.
The following function is my first attempt at handling this parsing puzzle.
For the interested, the structure is:
- all_protein_groups is a list of two elements: group_id => (“summary”, “data”)
- for every protein group discovered, one of these two element lists is created.
- The summary slot gets the one-row summary for the protein group from the excel.
- The data slot is a list of groups: protein => (“summary”, “data”)
- Once again, the summary is the single row from excel.
- Again, the data slot is a list of peptides: peptide => data.frame(peptides)
- Each peptide data frame contains all of the inner-most rows describing the observed peptides.
parse_difficult_excel <- function(xlsx_file, test_row=92150) {
old_options <- options(java.parameters="-Xmx20G")
message(paste0("Reading ", xlsx_file))
result <- readxl::read_xlsx(path=xlsx_file, sheet=1, col_names=FALSE)
message("Starting parse.")
group_data <- list()
bar <- utils::txtProgressBar(style=3)
for (r in 1:nrow(result)) {
row <- as.data.frame(result[r, ])
row[, is.na(row)] <- ""
pct_done <- r / nrow(result)
setTxtProgressBar(bar, pct_done)
## The following 3 stanzas handle the creation of the levels of our data structure
## The first defines the protein group
if (row[, 1] == "Checked") {
group_colnames <- as.character(row)
group_keepers <- !grepl(pattern="^$", x=group_colnames)
group_keepers[1] <- FALSE
group_colnames <- group_colnames[group_keepers]
next
}
## When the 2nd column is 'Checked', then this defines a new protein in the group.
if (row[, 2] == "Checked") {
protein_colnames <- as.character(row)
protein_keepers <- !grepl(pattern="^$", x=protein_colnames)
protein_keepers[2] <- FALSE
protein_colnames <- protein_colnames[protein_keepers]
next
}
## When the 3rd column is 'Checked', then this starts a peptide definition
if (row[, 3] == "Checked") {
peptide_colnames <- as.character(row)
peptide_keepers <- !grepl(pattern="^$", x=peptide_colnames)
peptide_keepers[3] <- FALSE
peptide_colnames <- peptide_colnames[peptide_keepers]
next
}
## Once the column names for the data are defined, we consider how to
## Fill in the actual data, the protein group is probably the least interesting.
if (row[, 1] == FALSE | row[, 1] == TRUE) {
group_information <- row[group_keepers]
colnames(group_information) <- group_colnames
group_information[["ID"]] <- sub(pattern="^.* GN=(\\w+) .*$",
replacement="\\1",
x=group_information[["Group Description"]])
group_accession <- group_information[["Protein Group ID"]]
group_list <- list(
"summary" = group_information,
"data" = list())
group_data[[group_accession]] <- group_list
next
}
## When the 2nd column is FALSE, then this defined a protein in the group.
## The protein data structure is likely the most interesting.
if (row[, 2] == FALSE | row[, 2] == TRUE) {
protein_information <- row[protein_keepers]
colnames(protein_information) <- protein_colnames
protein_information[["ID"]] <- sub(pattern="^.* GN=(\\w+) .*$",
replacement="\\1",
x=protein_information[["Description"]])
protein_accession <- protein_information[["Accession"]]
protein_list <- list(
"summary" = protein_information,
"data" = data.frame())
group_data[[group_accession]][["data"]][[protein_accession]] <- protein_list
next
}
## When the 3rd group is FALSE, then this adds a peptide.
## The peptide data structure is the most detailed, but probably not the most interesting.
if (row[, 3] == FALSE | row[, 3] == TRUE) {
peptide_information <- row[peptide_keepers]
colnames(peptide_information) <- peptide_colnames
current <- group_data[[group_accession]][["data"]][[protein_accession]][["data"]]
new <- rbind(current, peptide_information)
group_data[[group_accession]][["data"]][[protein_accession]][["data"]] <- new
next
}
} ## End iterating over ever row of this unholy stupid data structure.
close(bar)
message("Finished parsing, reorganizing the protein data.")
protein_df <- data.frame()
peptide_df <- data.frame()
protein_names <- c()
message(paste0("Starting to iterate over ", length(group_data), " groups."))
bar <- utils::txtProgressBar(style=3)
for (g in 1:length(group_data)) {
pct_done <- g / length(group_data)
setTxtProgressBar(bar, pct_done)
group <- as.character(names(group_data)[g])
protein_group <- group_data[[group]][["data"]]
protein_accessions <- names(protein_group)
for (p in 1:length(protein_accessions)) {
protein <- protein_accessions[p]
protein_names <- c(protein_names, protein)
protein_summary <- group_data[[group]][["data"]][[protein]][["summary"]]
protein_df <- rbind(protein_df, protein_summary)
peptide_data <- group_data[[group]][["data"]][[protein]][["data"]]
## peptide_df <- rbind(peptide_df, peptide_data)
}
} ## End of the for loop
close(bar)
retlist <- list(
"group_data" = group_data,
"protein_data" = protein_df,
"peptide_data" = peptide_df)
return(retlist)
}
##xlsx_file <- "previous_results/2017_0811BrikenMultiConsensus.xlsx"
xlsx_file <- "previous_results/2017_1101BrikenMulti_PE_PPE_Complete.xlsx"
less_difficult <- parse_difficult_excel(xlsx_file)## Reading previous_results/2017_1101BrikenMulti_PE_PPE_Complete.xlsx## Starting parse.##
|
| | 0%
|
| | 1%
|
|= | 1%
|
|= | 2%
|
|== | 2%
|
|== | 3%
|
|=== | 3%
|
|=== | 4%
|
|==== | 4%
|
|==== | 5%
|
|==== | 6%
|
|===== | 6%
|
|===== | 7%
|
|====== | 7%
|
|====== | 8%
|
|======= | 8%
|
|======= | 9%
|
|======== | 9%
|
|======== | 10%
|
|======== | 11%
|
|========= | 11%
|
|========= | 12%
|
|========== | 12%
|
|========== | 13%
|
|=========== | 13%
|
|=========== | 14%
|
|============ | 14%
|
|============ | 15%
|
|============ | 16%
|
|============= | 16%
|
|============= | 17%
|
|============== | 17%
|
|============== | 18%
|
|=============== | 18%
|
|=============== | 19%
|
|================ | 19%
|
|================ | 20%
|
|================ | 21%
|
|================= | 21%
|
|================= | 22%
|
|================== | 22%
|
|================== | 23%
|
|=================== | 23%
|
|=================== | 24%
|
|==================== | 24%
|
|==================== | 25%
|
|==================== | 26%
|
|===================== | 26%
|
|===================== | 27%
|
|====================== | 27%
|
|====================== | 28%
|
|======================= | 28%
|
|======================= | 29%
|
|======================== | 29%
|
|======================== | 30%
|
|======================== | 31%
|
|========================= | 31%
|
|========================= | 32%
|
|========================== | 32%
|
|========================== | 33%
|
|=========================== | 33%
|
|=========================== | 34%
|
|============================ | 34%
|
|============================ | 35%
|
|============================ | 36%
|
|============================= | 36%
|
|============================= | 37%
|
|============================== | 37%
|
|============================== | 38%
|
|=============================== | 38%
|
|=============================== | 39%
|
|================================ | 39%
|
|================================ | 40%
|
|================================ | 41%
|
|================================= | 41%
|
|================================= | 42%
|
|================================== | 42%
|
|================================== | 43%
|
|=================================== | 43%
|
|=================================== | 44%
|
|==================================== | 44%
|
|==================================== | 45%
|
|==================================== | 46%
|
|===================================== | 46%
|
|===================================== | 47%
|
|====================================== | 47%
|
|====================================== | 48%
|
|======================================= | 48%
|
|======================================= | 49%
|
|======================================== | 49%
|
|======================================== | 50%
|
|======================================== | 51%
|
|========================================= | 51%
|
|========================================= | 52%
|
|========================================== | 52%
|
|========================================== | 53%
|
|=========================================== | 53%
|
|=========================================== | 54%
|
|============================================ | 54%
|
|============================================ | 55%
|
|============================================ | 56%
|
|============================================= | 56%
|
|============================================= | 57%
|
|============================================== | 57%
|
|============================================== | 58%
|
|=============================================== | 58%
|
|=============================================== | 59%
|
|================================================ | 59%
|
|================================================ | 60%
|
|================================================ | 61%
|
|================================================= | 61%
|
|================================================= | 62%
|
|================================================== | 62%
|
|================================================== | 63%
|
|=================================================== | 63%
|
|=================================================== | 64%
|
|==================================================== | 64%
|
|==================================================== | 65%
|
|==================================================== | 66%
|
|===================================================== | 66%
|
|===================================================== | 67%
|
|====================================================== | 67%
|
|====================================================== | 68%
|
|======================================================= | 68%
|
|======================================================= | 69%
|
|======================================================== | 69%
|
|======================================================== | 70%
|
|======================================================== | 71%
|
|========================================================= | 71%
|
|========================================================= | 72%
|
|========================================================== | 72%
|
|========================================================== | 73%
|
|=========================================================== | 73%
|
|=========================================================== | 74%
|
|============================================================ | 74%
|
|============================================================ | 75%
|
|============================================================ | 76%
|
|============================================================= | 76%
|
|============================================================= | 77%
|
|============================================================== | 77%
|
|============================================================== | 78%
|
|=============================================================== | 78%
|
|=============================================================== | 79%
|
|================================================================ | 79%
|
|================================================================ | 80%
|
|================================================================ | 81%
|
|================================================================= | 81%
|
|================================================================= | 82%
|
|================================================================== | 82%
|
|================================================================== | 83%
|
|=================================================================== | 83%
|
|=================================================================== | 84%
|
|==================================================================== | 84%
|
|==================================================================== | 85%
|
|==================================================================== | 86%
|
|===================================================================== | 86%
|
|===================================================================== | 87%
|
|====================================================================== | 87%
|
|====================================================================== | 88%
|
|======================================================================= | 88%
|
|======================================================================= | 89%
|
|======================================================================== | 89%
|
|======================================================================== | 90%
|
|======================================================================== | 91%
|
|========================================================================= | 91%
|
|========================================================================= | 92%
|
|========================================================================== | 92%
|
|========================================================================== | 93%
|
|=========================================================================== | 93%
|
|=========================================================================== | 94%
|
|============================================================================ | 94%
|
|============================================================================ | 95%
|
|============================================================================ | 96%
|
|============================================================================= | 96%
|
|============================================================================= | 97%
|
|============================================================================== | 97%
|
|============================================================================== | 98%
|
|=============================================================================== | 98%
|
|=============================================================================== | 99%
|
|================================================================================| 99%
|
|================================================================================| 100%## Finished parsing, reorganizing the protein data.## Starting to iterate over 2574 groups.##
|
| | 0%
|
| | 1%
|
|= | 1%
|
|= | 2%
|
|== | 2%
|
|== | 3%
|
|=== | 3%
|
|=== | 4%
|
|==== | 4%
|
|==== | 5%
|
|==== | 6%
|
|===== | 6%
|
|===== | 7%
|
|====== | 7%
|
|====== | 8%
|
|======= | 8%
|
|======= | 9%
|
|======== | 9%
|
|======== | 10%
|
|======== | 11%
|
|========= | 11%
|
|========= | 12%
|
|========== | 12%
|
|========== | 13%
|
|=========== | 13%
|
|=========== | 14%
|
|============ | 14%
|
|============ | 15%
|
|============ | 16%
|
|============= | 16%
|
|============= | 17%
|
|============== | 17%
|
|============== | 18%
|
|=============== | 18%
|
|=============== | 19%
|
|================ | 19%
|
|================ | 20%
|
|================ | 21%
|
|================= | 21%
|
|================= | 22%
|
|================== | 22%
|
|================== | 23%
|
|=================== | 23%
|
|=================== | 24%
|
|==================== | 24%
|
|==================== | 25%
|
|==================== | 26%
|
|===================== | 26%
|
|===================== | 27%
|
|====================== | 27%
|
|====================== | 28%
|
|======================= | 28%
|
|======================= | 29%
|
|======================== | 29%
|
|======================== | 30%
|
|======================== | 31%
|
|========================= | 31%
|
|========================= | 32%
|
|========================== | 32%
|
|========================== | 33%
|
|=========================== | 33%
|
|=========================== | 34%
|
|============================ | 34%
|
|============================ | 35%
|
|============================ | 36%
|
|============================= | 36%
|
|============================= | 37%
|
|============================== | 37%
|
|============================== | 38%
|
|=============================== | 38%
|
|=============================== | 39%
|
|================================ | 39%
|
|================================ | 40%
|
|================================ | 41%
|
|================================= | 41%
|
|================================= | 42%
|
|================================== | 42%
|
|================================== | 43%
|
|=================================== | 43%
|
|=================================== | 44%
|
|==================================== | 44%
|
|==================================== | 45%
|
|==================================== | 46%
|
|===================================== | 46%
|
|===================================== | 47%
|
|====================================== | 47%
|
|====================================== | 48%
|
|======================================= | 48%
|
|======================================= | 49%
|
|======================================== | 49%
|
|======================================== | 50%
|
|======================================== | 51%
|
|========================================= | 51%
|
|========================================= | 52%
|
|========================================== | 52%
|
|========================================== | 53%
|
|=========================================== | 53%
|
|=========================================== | 54%
|
|============================================ | 54%
|
|============================================ | 55%
|
|============================================ | 56%
|
|============================================= | 56%
|
|============================================= | 57%
|
|============================================== | 57%
|
|============================================== | 58%
|
|=============================================== | 58%
|
|=============================================== | 59%
|
|================================================ | 59%
|
|================================================ | 60%
|
|================================================ | 61%
|
|================================================= | 61%
|
|================================================= | 62%
|
|================================================== | 62%
|
|================================================== | 63%
|
|=================================================== | 63%
|
|=================================================== | 64%
|
|==================================================== | 64%
|
|==================================================== | 65%
|
|==================================================== | 66%
|
|===================================================== | 66%
|
|===================================================== | 67%
|
|====================================================== | 67%
|
|====================================================== | 68%
|
|======================================================= | 68%
|
|======================================================= | 69%
|
|======================================================== | 69%
|
|======================================================== | 70%
|
|======================================================== | 71%
|
|========================================================= | 71%
|
|========================================================= | 72%
|
|========================================================== | 72%
|
|========================================================== | 73%
|
|=========================================================== | 73%
|
|=========================================================== | 74%
|
|============================================================ | 74%
|
|============================================================ | 75%
|
|============================================================ | 76%
|
|============================================================= | 76%
|
|============================================================= | 77%
|
|============================================================== | 77%
|
|============================================================== | 78%
|
|=============================================================== | 78%
|
|=============================================================== | 79%
|
|================================================================ | 79%
|
|================================================================ | 80%
|
|================================================================ | 81%
|
|================================================================= | 81%
|
|================================================================= | 82%
|
|================================================================== | 82%
|
|================================================================== | 83%
|
|=================================================================== | 83%
|
|=================================================================== | 84%
|
|==================================================================== | 84%
|
|==================================================================== | 85%
|
|==================================================================== | 86%
|
|===================================================================== | 86%
|
|===================================================================== | 87%
|
|====================================================================== | 87%
|
|====================================================================== | 88%
|
|======================================================================= | 88%
|
|======================================================================= | 89%
|
|======================================================================== | 89%
|
|======================================================================== | 90%
|
|======================================================================== | 91%
|
|========================================================================= | 91%
|
|========================================================================= | 92%
|
|========================================================================== | 92%
|
|========================================================================== | 93%
|
|=========================================================================== | 93%
|
|=========================================================================== | 94%
|
|============================================================================ | 94%
|
|============================================================================ | 95%
|
|============================================================================ | 96%
|
|============================================================================= | 96%
|
|============================================================================= | 97%
|
|============================================================================== | 97%
|
|============================================================================== | 98%
|
|=============================================================================== | 98%
|
|=============================================================================== | 99%
|
|================================================================================| 99%
|
|================================================================================| 100%The function above is not immediately helpful, as it only wrangles the crazy excel into a less-crazy data structure, but if we wish to graph the data or play with it, we need to extract the elements of interest. For the moment those are primarily the peptide fragments and whether they were found in the trypsin digestions, chymotrypsin digestions, or both.
This data frame now contains everything we are likely to want to visualize.
1.2 Cleaning the data frame
Unfortunately, this data structure also suffers from a couple weaknesses:
- The protein IDs are not unique (because of the groups)
- The column names from Excel are insane (punctuation, special characters, and really long)
- Excel left the numeric columns of data as characters, not numbers!
Therefore, I will take a moment to fix these problems before trying any plots.
## Set some reasonable row names
protein_df <- less_difficult[["protein_data"]]
rownames(protein_df) <- make.names(protein_names, unique=TRUE)## Error in `row.names<-.data.frame`(`*tmp*`, value = value): invalid 'row.names' length## Set the column names to something legible.
## Each line denotes my mental organizational unit
new_colnames <- c("mascot_fdr", "sequest_fdr", "master", "accession", "description",
"mascot_q", "sequest_q", "coverage", "contaminant", "marked_as",
"num_peptides", "num_psms", "num_unique_pep", "num_prot_groups", "num_aas",
"mw", "pi", "mascot_score", "sequest_score", "num_peptides_mascot",
"num_peptides_sequest", "bp", "cc", "mf", "pfam_ids",
"entrez_gene_id", "ensembl_gene_id", "gene_symbol", "chromosome", "kegg",
"wikipathways", "found_trypsin_lysc_cf", "found_trypsin_lysc_wcl", "found_chymotrypsin_cf", "found_chymotrypsin_wcl",
"modifications", "id")
colnames(protein_df) <- new_colnames
## Now make sure the columns which should be numeric, are numeric.
numeric_cols <- c("mascot_q", "sequest_q", "coverage", "num_peptides", "num_psms",
"num_unique_pep", "num_prot_groups", "num_aas", "mw", "pi",
"mascot_score", "sequest_score",
"num_peptides_mascot", "num_peptides_sequest")
for (col in numeric_cols) {
protein_df[[col]] <- as.numeric(protein_df[[col]])
}
factor_cols <- c("found_trypsin_lysc_cf", "found_trypsin_lysc_wcl",
"found_chymotrypsin_cf", "found_chymotrypsin_wcl")
for (col in factor_cols) {
protein_df[[col]] <- as.factor(protein_df[[col]])
}
## Finally, set a 'state' column describing the 4 columns of chymotrypsin/trypsin digestions.
protein_df[["state"]] <- "ambiguous"
## If something is found in one chymo trypsin, but in neither trypsin, set it to chymo_only.
chymo_hits <- (protein_df[["found_chymotrypsin_cf"]] == "High" | protein_df[["found_chymotrypsin_wcl"]] == "High") &
(protein_df[["found_trypsin_lysc_cf"]] != "High" & protein_df[["found_trypsin_lysc_wcl"]] != "High")
protein_df[chymo_hits, "state"] <- "chymo_only"
## Conversely, if found in trypsin but not chymo, set it to tryp_only.
tryp_hits <- (protein_df[["found_trypsin_lysc_cf"]] == "High" | protein_df[["found_trypsin_lysc_wcl"]] == "High") &
(protein_df[["found_chymotrypsin_cf"]] != "High" & protein_df[["found_chymotrypsin_wcl"]] != "High")
protein_df[tryp_hits, "state"] <- "tryp_only"
no_hits <- protein_df[["found_trypsin_lysc_cf"]] == "Not Found" &
protein_df[["found_trypsin_lysc_wcl"]] == "Not Found" &
protein_df[["found_chymotrypsin_cf"]] == "Not Found" &
protein_df[["found_chymotrypsin_wcl"]] == "Not Found"
protein_df[no_hits, "state"] <- "None"
protein_df[["state"]] <- as.factor(protein_df[["state"]])
chymo_df <- protein_df[chymo_hits, ]
tryp_df <- protein_df[tryp_hits, ]1.3 Make a couple plots
Now that the data has been wrangled, we can use the various R plotters to look at the data relatively quickly and easily.
library(ggplot2)
coverage_wrt_pi <- ggplot(protein_df, aes_string(x="pi", y="coverage", colour="state")) +
geom_point(alpha=0.6, size=2) +
geom_density_2d()
coverage_wrt_mw <- ggplot(protein_df, aes_string(x="mw", y="coverage", colour="state")) +
geom_point(alpha=0.6, size=2) +
geom_density_2d()
protein_df$logmascot <- log2(protein_df$mascot_score)
scores_by_state <- ggplot(protein_df, aes_string(x="state", y="logmascot")) +
geom_boxplot(aes_string(fill="state"))1.3.1 Coverage vs. pI
Yan suggested that peptides with low-pIs would be less covered, especially in the chymotrypsin samples.
pp(file="images/coverage_wrt_pi.png")## NULLcoverage_wrt_pi## Warning: Computation failed in `stat_density2d()`:
## bandwidths must be strictly positivedev.off()## X11cairo
## 2## To me, it looks like there might be something to that idea.
1.3.2 Coverage vs. Molecular Weight
Maybe there is a trend of coverage and molecular weight by sample-type.
pp(file="images/coverage_wrt_mw.png")## NULLcoverage_wrt_mw## Warning: Computation failed in `stat_density2d()`:
## bandwidths must be strictly positivedev.off()## X11cairo
## 2## It looks to me that the trypsin is slightly better than chymotrypsin.
## It is not clear in the excel, but I presume this is in kDa.
1.3.3 Mascot scores
A quick boxplot showing mascot scores by what was observed.
scores_by_state## Warning: Removed 96 rows containing non-finite values (stat_boxplot).
all_de <- read.table("limma_result.csv", header=TRUE, sep="\t")
colnames(all_de) <- c("ID", "logFC", "AveExpr", "adj.P.Val", "abbrev_id", "description", "gene_length", "type")
all_de <- merge(all_de, mtb_annotations, by.x="ID", by.y="ID")
dim(all_de)## [1] 3995 21pe_ids <- read.table("reference/annotated_pe_genes.txt")
colnames(pe_ids) <- "locus_tag"
pe_annotations <- merge(pe_ids, mtb_annotations, by="locus_tag")
rownames(pe_annotations) <- pe_annotations[["ID"]]
dim(pe_annotations)## [1] 161 14uniprot_annot <- load_uniprot_annotations(file="reference/uniprot_3AUP000001584.txt.gz")##
|
| | 0%
|
| | 1%
|
|= | 1%
|
|= | 2%
|
|== | 2%
|
|== | 3%
|
|=== | 3%
|
|=== | 4%
|
|==== | 4%
|
|==== | 5%
|
|==== | 6%
|
|===== | 6%
|
|===== | 7%
|
|====== | 7%
|
|====== | 8%
|
|======= | 8%
|
|======= | 9%
|
|======== | 9%
|
|======== | 10%
|
|======== | 11%
|
|========= | 11%
|
|========= | 12%
|
|========== | 12%
|
|========== | 13%
|
|=========== | 13%
|
|=========== | 14%
|
|============ | 14%
|
|============ | 15%
|
|============ | 16%
|
|============= | 16%
|
|============= | 17%
|
|============== | 17%
|
|============== | 18%
|
|=============== | 18%
|
|=============== | 19%
|
|================ | 19%
|
|================ | 20%
|
|================ | 21%
|
|================= | 21%
|
|================= | 22%
|
|================== | 22%
|
|================== | 23%
|
|=================== | 23%
|
|=================== | 24%
|
|==================== | 24%
|
|==================== | 25%
|
|==================== | 26%
|
|===================== | 26%
|
|===================== | 27%
|
|====================== | 27%
|
|====================== | 28%
|
|======================= | 28%
|
|======================= | 29%
|
|======================== | 29%
|
|======================== | 30%
|
|======================== | 31%
|
|========================= | 31%
|
|========================= | 32%
|
|========================== | 32%
|
|========================== | 33%
|
|=========================== | 33%
|
|=========================== | 34%
|
|============================ | 34%
|
|============================ | 35%
|
|============================ | 36%
|
|============================= | 36%
|
|============================= | 37%
|
|============================== | 37%
|
|============================== | 38%
|
|=============================== | 38%
|
|=============================== | 39%
|
|================================ | 39%
|
|================================ | 40%
|
|================================ | 41%
|
|================================= | 41%
|
|================================= | 42%
|
|================================== | 42%
|
|================================== | 43%
|
|=================================== | 43%
|
|=================================== | 44%
|
|==================================== | 44%
|
|==================================== | 45%
|
|==================================== | 46%
|
|===================================== | 46%
|
|===================================== | 47%
|
|====================================== | 47%
|
|====================================== | 48%
|
|======================================= | 48%
|
|======================================= | 49%
|
|======================================== | 49%
|
|======================================== | 50%
|
|======================================== | 51%
|
|========================================= | 51%
|
|========================================= | 52%
|
|========================================== | 52%
|
|========================================== | 53%
|
|=========================================== | 53%
|
|=========================================== | 54%
|
|============================================ | 54%
|
|============================================ | 55%
|
|============================================ | 56%
|
|============================================= | 56%
|
|============================================= | 57%
|
|============================================== | 57%
|
|============================================== | 58%
|
|=============================================== | 58%
|
|=============================================== | 59%
|
|================================================ | 59%
|
|================================================ | 60%
|
|================================================ | 61%
|
|================================================= | 61%
|
|================================================= | 62%
|
|================================================== | 62%
|
|================================================== | 63%
|
|=================================================== | 63%
|
|=================================================== | 64%
|
|==================================================== | 64%
|
|==================================================== | 65%
|
|==================================================== | 66%
|
|===================================================== | 66%
|
|===================================================== | 67%
|
|====================================================== | 67%
|
|====================================================== | 68%
|
|======================================================= | 68%
|
|======================================================= | 69%
|
|======================================================== | 69%
|
|======================================================== | 70%
|
|======================================================== | 71%
|
|========================================================= | 71%
|
|========================================================= | 72%
|
|========================================================== | 72%
|
|========================================================== | 73%
|
|=========================================================== | 73%
|
|=========================================================== | 74%
|
|============================================================ | 74%
|
|============================================================ | 75%
|
|============================================================ | 76%
|
|============================================================= | 76%
|
|============================================================= | 77%
|
|============================================================== | 77%
|
|============================================================== | 78%
|
|=============================================================== | 78%
|
|=============================================================== | 79%
|
|================================================================ | 79%
|
|================================================================ | 80%
|
|================================================================ | 81%
|
|================================================================= | 81%
|
|================================================================= | 82%
|
|================================================================== | 82%
|
|================================================================== | 83%
|
|=================================================================== | 83%
|
|=================================================================== | 84%
|
|==================================================================== | 84%
|
|==================================================================== | 85%
|
|==================================================================== | 86%
|
|===================================================================== | 86%
|
|===================================================================== | 87%
|
|====================================================================== | 87%
|
|====================================================================== | 88%
|
|======================================================================= | 88%
|
|======================================================================= | 89%
|
|======================================================================== | 89%
|
|======================================================================== | 90%
|
|======================================================================== | 91%
|
|========================================================================= | 91%
|
|========================================================================= | 92%
|
|========================================================================== | 92%
|
|========================================================================== | 93%
|
|=========================================================================== | 93%
|
|=========================================================================== | 94%
|
|============================================================================ | 94%
|
|============================================================================ | 95%
|
|============================================================================ | 96%
|
|============================================================================= | 96%
|
|============================================================================= | 97%
|
|============================================================================== | 97%
|
|============================================================================== | 98%
|
|=============================================================================== | 98%
|
|=============================================================================== | 99%
|
|================================================================================| 99%
|
|================================================================================| 100%## Finished parsing, creating data frame.dim(uniprot_annot)## [1] 3993 41uniprot_rna <- merge(all_de, uniprot_annot, by.x="ID", by.y="loci")
dim(uniprot_rna)## [1] 3891 61uniprot_pe <- merge(pe_annotations, uniprot_annot, by.x="ID", by.y="loci")
dim(uniprot_pe)## [1] 157 54uniprot_pe_peptides <- merge(uniprot_annot, protein_df, by.x="primary_accessions", by.y="accession")
uniprot_pe_peptides <- merge(uniprot_pe_peptides, pe_ids, by.x="loci", by.y="locus_tag")
dim(uniprot_pe_peptides)## [1] 40 79uniprot_rna_protein <- merge(uniprot_rna, protein_df, by.x="primary_accessions", by.y="accession")
dim(protein_df)## [1] 2671 39dim(uniprot_rna_protein)## [1] 2511 99uniprot_rna_pe <- merge(uniprot_pe, uniprot_rna_protein, by="ID")## Warning in merge.data.frame(uniprot_pe, uniprot_rna_protein, by = "ID"): column names
## 'description.x', 'description.y' are duplicated in the resultdim(uniprot_rna_pe)## [1] 40 152## This is exhausting, I will call these big and little from now on
big <- uniprot_rna_protein
rownames(big) <- make.names(big$ID, unique=TRUE)
ordered <- order(rownames(big))
big <- big[ordered, ]
little <- uniprot_rna_pe
rownames(little) <- make.names(little$ID, unique=TRUE)
ordered <- order(rownames(little))
little <- little[ordered, ]
shared <- rownames(big) %in% rownames(little)## pe_de_annot <- merge(pe_de, mtb_annotations, by="locus_tag")
## The contrast is written as wt - delta, so add back the logFC I think
## Hopefully I did not reverse this in my head.
big$ave_minus_log <- big$AveExpr - big$logFC
big$ave_minus_log <- big$AveExpr - big$logFC
big$pseudo_rpkm <- (big$ave_minus_log / big$width) * 1000
maximum_rpkm <- max(big$pseudo_rpkm)
big$express_vs_max <- big$pseudo_rpkm / maximum_rpkm
## Since we constant ordered the dataframes above, this is valid.
little$pseudo_rpkm <- big$pseudo_rpkm[shared]
little$express_vs_max <- big$express_vs_max[shared]
write.csv(x=little, file="pe_relative_expression_annotation-20171205.csv")
library(hpgltools)
library(ggplot2)
test <- list(
"all_peptides" = big$express_vs_max,
"pe_peptides" = little$express_vs_max)
multi <- plot_multihistogram(test)## Used Bon Ferroni corrected t test(s) between columns.multi$plot
rpkm_vs_psms <- big[, c("pseudo_rpkm", "num_psms")]
little_rpkm_vs_psms <- little[, c("pseudo_rpkm", "num_psms")]
rpkm_vs_psms$num_psms <- log2(rpkm_vs_psms$num_psms + 1)
little_rpkm_vs_psms$num_psms <- log2(little_rpkm_vs_psms$num_psms + 1)
rpkm_psms_linear <- plot_linear_scatter(rpkm_vs_psms)## Used Bon Ferroni corrected t test(s) between columns.rpkm_psms_linear$scatter
rpkm_psms_linear$cor##
## Pearson's product-moment correlation
##
## data: df[, 1] and df[, 2]
## t = 5.8, df = 2500, p-value = 9e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07575 0.15296
## sample estimates:
## cor
## 0.1145rpkm_psms_linear$lm_rsq## [1] 0.007207big_little <- ggplot(data=rpkm_vs_psms, aes_string(x="pseudo_rpkm", y="num_psms")) +
geom_point()
big_little
big_little <- big_little +
geom_point(data=little_rpkm_vs_psms, aes_string(x="pseudo_rpkm", y="num_psms"), colour="red")
pp(file="rpkm_vs_psms.png")## NULLbig_little
dev.off()## X11cairo
## 2rpkm_vs_peptides <- big[, c("pseudo_rpkm", "num_peptides")]
rpkm_peptides_linear <- plot_linear_scatter(rpkm_vs_peptides)## Used Bon Ferroni corrected t test(s) between columns.rpkm_peptides_linear$scatter
rpkm_peptides_linear$cor##
## Pearson's product-moment correlation
##
## data: df[, 1] and df[, 2]
## t = -4.8, df = 2500, p-value = 2e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.13318 -0.05564
## sample estimates:
## cor
## -0.09455rpkm_psms_linear$lm_rsq## [1] 0.007207little_rpkm_vs_peptides <- little[, c("pseudo_rpkm", "num_peptides")]
big_little <- ggplot(data=rpkm_vs_peptides, aes_string(x="pseudo_rpkm", y="num_peptides")) +
geom_point()
big_little
big_little <- big_little +
geom_point(data=little_rpkm_vs_peptides, aes_string(x="pseudo_rpkm", y="num_peptides"), colour="red")
big_little
rpkm_vs_coverage <- big[, c("pseudo_rpkm", "coverage")]
rpkm_coverage_linear <- plot_linear_scatter(rpkm_vs_coverage)## Used Bon Ferroni corrected t test(s) between columns.rpkm_coverage_linear$scatter
rpkm_coverage_linear$cor##
## Pearson's product-moment correlation
##
## data: df[, 1] and df[, 2]
## t = 21, df = 2500, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3532 0.4198
## sample estimates:
## cor
## 0.387rpkm_coverage_linear$lm_rsq## [1] 0.1618little_rpkm_vs_coverage <- little[, c("pseudo_rpkm", "coverage")]
big_little <- ggplot(data=rpkm_vs_coverage, aes_string(x="pseudo_rpkm", y="coverage")) +
geom_point()
big_little
big_little <- big_little +
geom_point(data=little_rpkm_vs_coverage, aes_string(x="pseudo_rpkm", y="coverage"), colour="red")
big_little
pander::pander(sessionInfo())R version 3.4.3 (2017-11-30)
**Platform:** x86_64-pc-linux-gnu (64-bit)
locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C
attached base packages: stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: hpgltools(v.2017.10) and ggplot2(v.2.2.1)
loaded via a namespace (and not attached): reshape2(v.1.4.2), pander(v.0.6.1), colorspace(v.1.3-2), htmltools(v.0.3.6), yaml(v.2.1.15), base64enc(v.0.1-3), rlang(v.0.1.4), withr(v.2.1.0), BiocGenerics(v.0.24.0), readxl(v.1.0.0), foreach(v.1.4.3), plyr(v.1.8.4), robustbase(v.0.92-8), stringr(v.1.2.0), munsell(v.0.4.3), commonmark(v.1.4), gtable(v.0.2.0), cellranger(v.1.1.0), devtools(v.1.13.4), codetools(v.0.2-15), memoise(v.1.1.0), evaluate(v.0.10.1), labeling(v.0.3), Biobase(v.2.38.0), knitr(v.1.17), parallel(v.3.4.3), DEoptimR(v.1.0-8), Rcpp(v.0.12.14), readr(v.1.1.1), scales(v.0.5.0), backports(v.1.1.1), hms(v.0.4.0), digest(v.0.6.12), stringi(v.1.1.6), grid(v.3.4.3), rprojroot(v.1.2), tools(v.3.4.3), magrittr(v.1.5), lazyeval(v.0.2.1), tibble(v.1.3.4), pkgconfig(v.2.0.1), MASS(v.7.3-47), data.table(v.1.10.4-3), xml2(v.1.1.1), rmarkdown(v.1.8), roxygen2(v.6.0.1), iterators(v.1.0.8), R6(v.2.2.2) and compiler(v.3.4.3)
message(paste0("This is hpgltools commit: ", get_git_commit()))## If you wish to reproduce this exact build of hpgltools, invoke the following:## > git clone http://github.com/abelew/hpgltools.git## > git reset e98d5f5d939340766de61c5d354576f11c2769db## R> packrat::restore()## This is hpgltools commit: Tue Dec 5 22:18:58 2017 -0500: e98d5f5d939340766de61c5d354576f11c2769dbthis_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
message(paste0("Saving to ", this_save))## Saving to 02_eval_result_201711-v20171205.rda.xztmp <- sm(saveme(filename=this_save))---
title: "Evaluating some MS/MS peptide search metrics."
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
 html_document:
  code_download: true
  code_folding: show
  fig_caption: true
  fig_height: 7
  fig_width: 7
  highlight: default
  keep_md: false
  mode: selfcontained
  number_sections: true
  self_contained: true
  theme: readable
  toc: true
  toc_float:
    collapsed: false
    smooth_scroll: false
---

<style>
  body .main-container {
    max-width: 1600px;
  }
</style>

```{r options, include=FALSE}
library(hpgltools)
tt <- devtools::load_all("~/hpgltools")
knitr::opts_knit$set(progress=TRUE,
                     verbose=TRUE,
                     width=90,
                     echo=TRUE)
knitr::opts_chunk$set(error=TRUE,
                      fig.width=8,
                      fig.height=8,
                      dpi=96)
old_options <- options(digits=4,
                       stringsAsFactors=FALSE,
                       knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=10))
set.seed(1)
ver <- "20171205"
previous_file <- "01_annotation.Rmd"

tmp <- try(sm(loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz"))))

rmd_file <- "02_eval_result_201711.Rmd"
```

# Evaluate Previous Result: version: `r ver`

## Reading in the existing result

As per Yan's suggestion, I am using the existing results, found in the file:
"2017_0811BrikenMultiConsensus.xlsx"

Unfortunately, this table is in a particularly strange format: it is a triply
nested table of tables of tables in one worksheet in Excel.  At one level, this
is a really novel and interesting use of Excel, but on every other level it is
sub-optimal.  The primary reason it is sub-optimal is that the column headings
end up being repeated once for every new sub-table (874 of the inner tables plus
however many middle tables), which makes it nearly unparseable.

I am displeased I need to do this level of data wrangling.

The following function is my first attempt at handling this parsing puzzle.

For the interested, the structure is:

*  all_protein_groups is a list of two elements: group_id => ("summary", "data")
*  for every protein group discovered, one of these two element lists is created.
    *  The summary slot gets the one-row summary for the protein group from the excel.
*  The data slot is a list of groups: protein => ("summary", "data")
    *  Once again, the summary is the single row from excel.
*  Again, the data slot is a list of peptides: peptide => data.frame(peptides)
    *  Each peptide data frame contains all of the inner-most rows describing the
       observed peptides.

```{r extract_peptides}
parse_difficult_excel <- function(xlsx_file, test_row=92150) {
  old_options <- options(java.parameters="-Xmx20G")
  message(paste0("Reading ", xlsx_file))
  result <- readxl::read_xlsx(path=xlsx_file, sheet=1, col_names=FALSE)
  message("Starting parse.")
  group_data <- list()
  bar <- utils::txtProgressBar(style=3)
  for (r in 1:nrow(result)) {
    row <- as.data.frame(result[r, ])
    row[, is.na(row)] <- ""
    pct_done <- r / nrow(result)
    setTxtProgressBar(bar, pct_done)
    ## The following 3 stanzas handle the creation of the levels of our data structure
    ## The first defines the protein group
    if (row[, 1] == "Checked") {
      group_colnames <- as.character(row)
      group_keepers <- !grepl(pattern="^$", x=group_colnames)
      group_keepers[1] <- FALSE
      group_colnames <- group_colnames[group_keepers]
      next
    }
    ## When the 2nd column is 'Checked', then this defines a new protein in the group.
    if (row[, 2] == "Checked") {
      protein_colnames <- as.character(row)
      protein_keepers <- !grepl(pattern="^$", x=protein_colnames)
      protein_keepers[2] <- FALSE
      protein_colnames <- protein_colnames[protein_keepers]
      next
    }
    ## When the 3rd column is 'Checked', then this starts a peptide definition
    if (row[, 3] == "Checked") {
      peptide_colnames <- as.character(row)
      peptide_keepers <- !grepl(pattern="^$", x=peptide_colnames)
      peptide_keepers[3] <- FALSE
      peptide_colnames <- peptide_colnames[peptide_keepers]
      next
    }
    ## Once the column names for the data are defined, we consider how to
    ## Fill in the actual data, the protein group is probably the least interesting.
    if (row[, 1] == FALSE | row[, 1] == TRUE) {
      group_information <- row[group_keepers]
      colnames(group_information) <- group_colnames
      group_information[["ID"]] <- sub(pattern="^.* GN=(\\w+) .*$",
                                       replacement="\\1",
                                       x=group_information[["Group Description"]])
      group_accession <- group_information[["Protein Group ID"]]
      group_list <- list(
        "summary" = group_information,
        "data" = list())
      group_data[[group_accession]] <- group_list
      next
    }
    ## When the 2nd column is FALSE, then this defined a protein in the group.
    ## The protein data structure is likely the most interesting.
    if (row[, 2] == FALSE | row[, 2] == TRUE) {
      protein_information <- row[protein_keepers]
      colnames(protein_information) <- protein_colnames
      protein_information[["ID"]] <- sub(pattern="^.* GN=(\\w+) .*$",
                                         replacement="\\1",
                                         x=protein_information[["Description"]])
      protein_accession <- protein_information[["Accession"]]
      protein_list <- list(
        "summary" = protein_information,
        "data" = data.frame())
      group_data[[group_accession]][["data"]][[protein_accession]] <- protein_list
      next
    }
    ## When the 3rd group is FALSE, then this adds a peptide.
    ## The peptide data structure is the most detailed, but probably not the most interesting.
    if (row[, 3] == FALSE | row[, 3] == TRUE) {
      peptide_information <- row[peptide_keepers]
      colnames(peptide_information) <- peptide_colnames
      current <- group_data[[group_accession]][["data"]][[protein_accession]][["data"]]
      new <- rbind(current, peptide_information)
      group_data[[group_accession]][["data"]][[protein_accession]][["data"]] <- new
      next
    }
  } ## End iterating over ever row of this unholy stupid data structure.
  close(bar)
  message("Finished parsing, reorganizing the protein data.")
  protein_df <- data.frame()
  peptide_df <- data.frame()
  protein_names <- c()
  message(paste0("Starting to iterate over ", length(group_data),  " groups."))
  bar <- utils::txtProgressBar(style=3)
  for (g in 1:length(group_data)) {
    pct_done <- g / length(group_data)
    setTxtProgressBar(bar, pct_done)
    group <- as.character(names(group_data)[g])
    protein_group <- group_data[[group]][["data"]]
    protein_accessions <- names(protein_group)
    for (p in 1:length(protein_accessions)) {
      protein <- protein_accessions[p]
      protein_names <- c(protein_names, protein)
      protein_summary <- group_data[[group]][["data"]][[protein]][["summary"]]
      protein_df <- rbind(protein_df, protein_summary)
      peptide_data <- group_data[[group]][["data"]][[protein]][["data"]]
      ## peptide_df <- rbind(peptide_df, peptide_data)
    }
  } ## End of the for loop
  close(bar)
  retlist <- list(
    "group_data" = group_data,
    "protein_data" = protein_df,
    "peptide_data" = peptide_df)
  return(retlist)
}

##xlsx_file <- "previous_results/2017_0811BrikenMultiConsensus.xlsx"
xlsx_file <- "previous_results/2017_1101BrikenMulti_PE_PPE_Complete.xlsx"
less_difficult <- parse_difficult_excel(xlsx_file)
```

The function above is not immediately helpful, as it only wrangles the crazy
excel into a less-crazy data structure, but if we wish to graph the data or play
with it, we need to extract the elements of interest.  For the moment those are
primarily the peptide fragments and whether they were found in the trypsin
digestions, chymotrypsin digestions, or both.

This data frame now contains everything we are likely to want to visualize.

## Cleaning the data frame

Unfortunately, this data structure also suffers from a couple weaknesses:

1.  The protein IDs are not unique (because of the groups)
2.  The column names from Excel are insane (punctuation, special characters, and really long)
3.  Excel left the numeric columns of data as characters, not numbers!

Therefore, I will take a moment to fix these problems before trying any plots.

```{r df_cleaning}
## Set some reasonable row names
protein_df <- less_difficult[["protein_data"]]

rownames(protein_df) <- make.names(protein_names, unique=TRUE)
## Set the column names to something legible.
## Each line denotes my mental organizational unit
new_colnames <- c("mascot_fdr", "sequest_fdr", "master", "accession", "description",
                  "mascot_q", "sequest_q", "coverage", "contaminant", "marked_as",
                  "num_peptides", "num_psms", "num_unique_pep", "num_prot_groups", "num_aas",
                  "mw", "pi", "mascot_score", "sequest_score", "num_peptides_mascot",
                  "num_peptides_sequest", "bp", "cc", "mf", "pfam_ids",
                  "entrez_gene_id", "ensembl_gene_id", "gene_symbol", "chromosome", "kegg",
                  "wikipathways", "found_trypsin_lysc_cf", "found_trypsin_lysc_wcl", "found_chymotrypsin_cf", "found_chymotrypsin_wcl",
                  "modifications", "id")
colnames(protein_df) <- new_colnames
## Now make sure the columns which should be numeric, are numeric.
numeric_cols <- c("mascot_q", "sequest_q", "coverage", "num_peptides", "num_psms",
                  "num_unique_pep", "num_prot_groups", "num_aas", "mw", "pi",
                  "mascot_score", "sequest_score",
                  "num_peptides_mascot", "num_peptides_sequest")
for (col in numeric_cols) {
  protein_df[[col]] <- as.numeric(protein_df[[col]])
}
factor_cols <- c("found_trypsin_lysc_cf", "found_trypsin_lysc_wcl",
                 "found_chymotrypsin_cf", "found_chymotrypsin_wcl")
for (col in factor_cols) {
  protein_df[[col]] <- as.factor(protein_df[[col]])
}
## Finally, set a 'state' column describing the 4 columns of chymotrypsin/trypsin digestions.
protein_df[["state"]] <- "ambiguous"
## If something is found in one chymo trypsin, but in neither trypsin, set it to chymo_only.
chymo_hits <- (protein_df[["found_chymotrypsin_cf"]] == "High" | protein_df[["found_chymotrypsin_wcl"]] == "High") &
  (protein_df[["found_trypsin_lysc_cf"]] != "High" & protein_df[["found_trypsin_lysc_wcl"]] != "High")
protein_df[chymo_hits, "state"] <- "chymo_only"
## Conversely, if found in trypsin but not chymo, set it to tryp_only.
tryp_hits <- (protein_df[["found_trypsin_lysc_cf"]] == "High" | protein_df[["found_trypsin_lysc_wcl"]] == "High") &
  (protein_df[["found_chymotrypsin_cf"]] != "High" & protein_df[["found_chymotrypsin_wcl"]] != "High")
protein_df[tryp_hits, "state"] <- "tryp_only"
no_hits <- protein_df[["found_trypsin_lysc_cf"]] == "Not Found" &
  protein_df[["found_trypsin_lysc_wcl"]] == "Not Found" &
  protein_df[["found_chymotrypsin_cf"]] == "Not Found" &
  protein_df[["found_chymotrypsin_wcl"]] == "Not Found"
protein_df[no_hits, "state"] <- "None"
protein_df[["state"]] <- as.factor(protein_df[["state"]])

chymo_df <- protein_df[chymo_hits, ]
tryp_df <- protein_df[tryp_hits, ]
```

## Make a couple plots

Now that the data has been wrangled, we can use the various R plotters to look
at the data relatively quickly and easily.

```{r plots}
library(ggplot2)
coverage_wrt_pi <- ggplot(protein_df, aes_string(x="pi", y="coverage", colour="state")) +
  geom_point(alpha=0.6, size=2) +
  geom_density_2d()

coverage_wrt_mw <- ggplot(protein_df, aes_string(x="mw", y="coverage", colour="state")) +
  geom_point(alpha=0.6, size=2) +
  geom_density_2d()

protein_df$logmascot <- log2(protein_df$mascot_score)
scores_by_state <- ggplot(protein_df, aes_string(x="state", y="logmascot")) +
  geom_boxplot(aes_string(fill="state"))
```

### Coverage vs. pI

Yan suggested that peptides with low-pIs would be less covered, especially in
the chymotrypsin samples.

```{r cov_pi}
pp(file="images/coverage_wrt_pi.png")
coverage_wrt_pi
dev.off()
## To me, it looks like there might be something to that idea.
```

### Coverage vs. Molecular Weight

Maybe there is a trend of coverage and molecular weight by sample-type.

```{r cov_mw}
pp(file="images/coverage_wrt_mw.png")
coverage_wrt_mw
dev.off()
## It looks to me that the trypsin is slightly better than chymotrypsin.
## It is not clear in the excel, but I presume this is in kDa.
```

### Mascot scores

A quick boxplot showing mascot scores by what was observed.

```{r mascot}
scores_by_state
```

```{r xref_annotations}
all_de <- read.table("limma_result.csv", header=TRUE, sep="\t")
colnames(all_de) <- c("ID", "logFC", "AveExpr", "adj.P.Val", "abbrev_id", "description", "gene_length", "type")
all_de <- merge(all_de, mtb_annotations, by.x="ID", by.y="ID")
dim(all_de)

pe_ids <- read.table("reference/annotated_pe_genes.txt")
colnames(pe_ids) <- "locus_tag"
pe_annotations <- merge(pe_ids, mtb_annotations, by="locus_tag")
rownames(pe_annotations) <- pe_annotations[["ID"]]
dim(pe_annotations)

uniprot_annot <- load_uniprot_annotations(file="reference/uniprot_3AUP000001584.txt.gz")
dim(uniprot_annot)

uniprot_rna <- merge(all_de, uniprot_annot, by.x="ID", by.y="loci")
dim(uniprot_rna)

uniprot_pe <- merge(pe_annotations, uniprot_annot, by.x="ID", by.y="loci")
dim(uniprot_pe)
uniprot_pe_peptides <- merge(uniprot_annot, protein_df, by.x="primary_accessions", by.y="accession")
uniprot_pe_peptides <- merge(uniprot_pe_peptides, pe_ids, by.x="loci", by.y="locus_tag")
dim(uniprot_pe_peptides)

uniprot_rna_protein <- merge(uniprot_rna, protein_df, by.x="primary_accessions", by.y="accession")
dim(protein_df)
dim(uniprot_rna_protein)

uniprot_rna_pe <- merge(uniprot_pe, uniprot_rna_protein, by="ID")
dim(uniprot_rna_pe)

## This is exhausting, I will call these big and little from now on
big <- uniprot_rna_protein
rownames(big) <- make.names(big$ID, unique=TRUE)
ordered <- order(rownames(big))
big <- big[ordered, ]
little <- uniprot_rna_pe
rownames(little) <- make.names(little$ID, unique=TRUE)
ordered <- order(rownames(little))
little <- little[ordered, ]
shared <- rownames(big) %in% rownames(little)
```

```{r extract_rna}
## pe_de_annot <- merge(pe_de, mtb_annotations, by="locus_tag")
## The contrast is written as wt - delta, so add back the logFC I think
## Hopefully I did not reverse this in my head.
big$ave_minus_log <- big$AveExpr - big$logFC
big$ave_minus_log <- big$AveExpr - big$logFC
big$pseudo_rpkm <- (big$ave_minus_log / big$width) * 1000
maximum_rpkm <- max(big$pseudo_rpkm)
big$express_vs_max <- big$pseudo_rpkm / maximum_rpkm
## Since we constant ordered the dataframes above, this is valid.
little$pseudo_rpkm <- big$pseudo_rpkm[shared]
little$express_vs_max <- big$express_vs_max[shared]

write.csv(x=little, file="pe_relative_expression_annotation-20171205.csv")

library(hpgltools)
library(ggplot2)
test <- list(
  "all_peptides" = big$express_vs_max,
  "pe_peptides" = little$express_vs_max)
multi <- plot_multihistogram(test)
multi$plot

rpkm_vs_psms <- big[, c("pseudo_rpkm", "num_psms")]
little_rpkm_vs_psms <- little[, c("pseudo_rpkm", "num_psms")]
rpkm_vs_psms$num_psms <- log2(rpkm_vs_psms$num_psms + 1)
little_rpkm_vs_psms$num_psms <- log2(little_rpkm_vs_psms$num_psms + 1)
rpkm_psms_linear <- plot_linear_scatter(rpkm_vs_psms)
rpkm_psms_linear$scatter
rpkm_psms_linear$cor
rpkm_psms_linear$lm_rsq
big_little <- ggplot(data=rpkm_vs_psms, aes_string(x="pseudo_rpkm", y="num_psms")) +
  geom_point()
big_little
big_little <- big_little +
  geom_point(data=little_rpkm_vs_psms, aes_string(x="pseudo_rpkm", y="num_psms"), colour="red")
pp(file="rpkm_vs_psms.png")
big_little
dev.off()

rpkm_vs_peptides <- big[, c("pseudo_rpkm", "num_peptides")]
rpkm_peptides_linear <- plot_linear_scatter(rpkm_vs_peptides)
rpkm_peptides_linear$scatter
rpkm_peptides_linear$cor
rpkm_psms_linear$lm_rsq
little_rpkm_vs_peptides <- little[, c("pseudo_rpkm", "num_peptides")]
big_little <- ggplot(data=rpkm_vs_peptides, aes_string(x="pseudo_rpkm", y="num_peptides")) +
  geom_point()
big_little
big_little <- big_little +
  geom_point(data=little_rpkm_vs_peptides, aes_string(x="pseudo_rpkm", y="num_peptides"), colour="red")
big_little

rpkm_vs_coverage <- big[, c("pseudo_rpkm", "coverage")]
rpkm_coverage_linear <- plot_linear_scatter(rpkm_vs_coverage)
rpkm_coverage_linear$scatter
rpkm_coverage_linear$cor
rpkm_coverage_linear$lm_rsq
little_rpkm_vs_coverage <- little[, c("pseudo_rpkm", "coverage")]
big_little <- ggplot(data=rpkm_vs_coverage, aes_string(x="pseudo_rpkm", y="coverage")) +
  geom_point()
big_little
big_little <- big_little +
  geom_point(data=little_rpkm_vs_coverage, aes_string(x="pseudo_rpkm", y="coverage"), colour="red")
big_little
```

```{r saveme}
pander::pander(sessionInfo())
message(paste0("This is hpgltools commit: ", get_git_commit()))
this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
message(paste0("Saving to ", this_save))
tmp <- sm(saveme(filename=this_save))
```
