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)
Table 1.7: First 6 rows of the cross-tab.
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")