1.4 Create Quantitative Cross-tab
This is the step where MS/MS IDs and reporter ions are linked together and aggregated to the peptide or accession (i.e. protein) level. To retain protein IDs while aggregating to peptide level, set aggregation_level <- c("accession","peptide"). The aggregation level can be any column or combination of columns in psms(msnid). If specified by the study design tables, the intensities are converted to relative intensities by dividing by a reference. Then, they are log\(_2\)-transformed.
# Create protein-level cross-tab by aggregating to accession level
crosstab <- create_crosstab(msnid = msnid,
reporter_intensities = masic_data,
aggregation_level = "accession",
fractions = fractions,
samples = samples,
references = references)| S1_1 | S1_2 | S1_3 | S1_4 | S1_5 | S1_6 | S1_7 | S1_8 | S1_9 | S2_1 | S2_2 | S2_3 | S2_4 | S2_5 | S2_6 | S2_7 | S2_8 | S2_9 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AP_004893.1 | 0.1419768 | 0.1653552 | 0.7628195 | 0.9453172 | 0.8662554 | -1.9294467 | -0.6460065 | -1.2831873 | -0.4321433 | -1.0271227 | 0.4883309 | -0.9390945 | -0.7029685 | -1.7148628 | -0.1912097 | -0.8794712 | -0.2440478 | 0.3964607 |
| AP_004894.1 | 0.7947114 | -0.3151990 | -0.0913574 | 0.1974134 | 0.3033858 | -0.1750536 | -0.3527197 | -1.1762004 | -0.6438817 | -0.5124954 | -0.4428327 | -0.2364175 | -0.6711809 | -1.3730408 | -0.7462995 | -1.3515366 | -0.2227493 | -0.8338103 |
| AP_004895.1 | 0.2078433 | -0.6089756 | -0.2867209 | -0.3840271 | -0.1162062 | -0.6908468 | -1.1240967 | -0.7140383 | -0.6652575 | 0.2843676 | -0.1312555 | -0.1477038 | -0.4352950 | -0.6371609 | -0.6150788 | -0.6819180 | -0.1602120 | -0.3978979 |
| AP_004896.1 | -0.1494849 | -0.7314368 | -0.3664339 | -0.5352280 | -0.1742391 | -1.0372327 | -1.2945071 | -0.8299749 | -0.7060783 | 0.1939540 | -0.1688422 | -0.2274358 | -0.4222698 | -0.5251264 | -0.6741064 | -0.6543311 | -0.0441485 | -0.3994149 |
| AP_004898.1 | 0.0362964 | 0.4252227 | 0.7497227 | 1.1580326 | 0.4913660 | -0.3640632 | 0.1211536 | -0.8291744 | -0.3019505 | -0.8407749 | -0.4130732 | -0.2796091 | -0.9449498 | -1.5747761 | -0.1774225 | -1.8439756 | -0.4175363 | -1.1083199 |
| AP_004899.1 | 0.7140968 | -0.3732752 | -0.1781542 | -0.0615626 | 0.3494902 | -0.8550940 | -2.1679002 | -1.4519278 | -0.9026145 | -0.3158081 | -0.4644758 | -0.4056811 | -0.9023044 | -0.2805080 | -0.8052899 | -1.0482424 | -0.3959923 | -0.6675429 |
Now that we have the cross-tab, we should save it.
# Save cross-tab
write.table(crosstab, file = "data/3442_global_crosstab.txt",
sep = "\t", quote = FALSE, row.names = TRUE)We will also save the proteins (row names) of this cross-tab in order to demonstrate prioritized inference later on.
# Save global proteins
global_proteins <- rownames(crosstab)
save(global_proteins, file = "data/3442_global_proteins.RData")