Working with glyexp
with-exp.RmdWhen Motifs Meet Experiments: A Perfect Partnership 🤝
The real power of glymotif shines brightest when it
joins forces with other tools in the glycoverse ecosystem.
If you’re already using glyread to import your
glycoproteomics results and glyexp to manage your
experimental data, you’re in for a treat! glymotif provides
some incredibly useful functions to perform experiment
manipulation about motifs—think of it as adding a new lens to your
analytical microscope. 🔬
Important note: This vignette assumes you’re
familiar with the glyexp package. If you haven’t met it
yet, we highly recommend checking out its introduction
first. Trust us—it’s worth the detour! 🚀
library(glymotif)
library(glyexp)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:glyexp':
#>
#> select_var
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, unionMeet Our Star Player: Real Glycoproteomics Data 🌟
Time to roll up our sleeves and dive into the good stuff! 💪 Let’s
work with a real-world dataset that will showcase what
glymotif can do when it teams up with actual experimental
data. We will use real_experiment from the
glyexp package, an serum N-glycoproteomics study with 12
samples. Firstly, let’s use glyclean::auto_clean() to
preprocess the data.
library(glyclean)
#>
#> Attaching package: 'glyclean'
#> The following object is masked from 'package:stats':
#>
#> aggregate
exp <- auto_clean(real_experiment)
#> ℹ Normalizing data (Median)
#> ✔ Normalizing data (Median) [152ms]
#>
#> ℹ Removing variables with >50% missing values
#> ✔ Removing variables with >50% missing values [47ms]
#>
#> ℹ Imputing missing values
#> ℹ Sample size <= 30, using sample minimum imputation
#> ℹ Imputing missing values✔ Imputing missing values [25ms]
#>
#> ℹ Aggregating data
#> ✔ Aggregating data [1s]
#>
#> ℹ Normalizing data again
#> ✔ Normalizing data again [15ms]
exp
#>
#> ── Glycoproteomics Experiment ──────────────────────────────────────────────────
#> ℹ Expression matrix: 12 samples, 3880 variables
#> ℹ Sample information fields: group <fct>
#> ℹ Variable information fields: protein <chr>, glycan_composition <comp>, glycan_structure <struct>, protein_site <int>, gene <chr>Now, let’s peek under the hood and see what treasures we’re working with! 👀
get_var_info(exp)
#> # A tibble: 3,880 × 6
#> variable protein glycan_composition glycan_structure protein_site gene
#> <chr> <chr> <comp> <struct> <int> <chr>
#> 1 V1 P08185 Hex(5)HexNAc(4)NeuAc(2) NeuAc(??-?)Hex(?… 176 SERP…
#> 2 V2 P04196 Hex(5)HexNAc(4)NeuAc(1) NeuAc(??-?)Hex(?… 344 HRG
#> 3 V3 P04196 Hex(5)HexNAc(4) Hex(??-?)HexNAc(… 344 HRG
#> 4 V4 P04196 Hex(5)HexNAc(4)NeuAc(1) NeuAc(??-?)Hex(?… 344 HRG
#> 5 V5 P10909 Hex(6)HexNAc(5) Hex(??-?)HexNAc(… 291 CLU
#> 6 V6 P04196 Hex(5)HexNAc(4)NeuAc(2) NeuAc(??-?)Hex(?… 344 HRG
#> 7 V7 P04196 Hex(5)HexNAc(4) Hex(??-?)HexNAc(… 345 HRG
#> 8 V8 P04196 Hex(5)HexNAc(4)dHex(2) dHex(??-?)Hex(??… 344 HRG
#> 9 V9 P04196 Hex(4)HexNAc(3) Hex(??-?)HexNAc(… 344 HRG
#> 10 V10 P04196 Hex(4)HexNAc(4)NeuAc(1) NeuAc(??-?)Hex(?… 344 HRG
#> # ℹ 3,870 more rows
get_sample_info(exp)
#> # A tibble: 12 × 2
#> sample group
#> <chr> <fct>
#> 1 C1 C
#> 2 C2 C
#> 3 C3 C
#> 4 H1 H
#> 5 H2 H
#> 6 H3 H
#> 7 M1 M
#> 8 M2 M
#> 9 M3 M
#> 10 Y1 Y
#> 11 Y2 Y
#> 12 Y3 YWhat we have here is a beautiful N-glycoproteomics dataset featuring 500 PSMs (Peptide Spectrum Matches) across 12 samples — a perfect playground for motif analysis! 🎮
Pro tip: 💡 In real-world data analysis, you’ll
definitely want to use glyclean to perform data
preprocessing before diving into any analysis. Think of it as washing
your vegetables before cooking—essential for the best results! 🥕
Adding Motif Annotations to an Experiment 🏷️
Here’s where things get interesting! 🤔 We know that the variable information tibble contains all the juicy details about each glycoform - the proteins, sites, and glycan structures. But what if we want to sprinkle some motif magic into the mix? What if we want to add more columns that tell us about the motifs hiding in our glycans?
The simple approach: One motif at a time (very intuitive!) 🎯
exp |>
mutate_var(n_hex = have_motif(glycan_structure, "Hex(a1-")) |>
get_var_info() |>
select(variable, protein, glycan_structure, n_hex)
#> # A tibble: 3,880 × 4
#> variable protein glycan_structure n_hex
#> <chr> <chr> <struct> <lgl>
#> 1 V1 P08185 NeuAc(??-?)Hex(??-?)HexNAc(??-?)Hex(??-?)[NeuAc(??-?)… FALSE
#> 2 V2 P04196 NeuAc(??-?)Hex(??-?)HexNAc(??-?)[HexNAc(??-?)]Hex(??-… FALSE
#> 3 V3 P04196 Hex(??-?)HexNAc(??-?)Hex(??-?)[Hex(??-?)HexNAc(??-?)H… FALSE
#> 4 V4 P04196 NeuAc(??-?)Hex(??-?)HexNAc(??-?)Hex(??-?)[Hex(??-?)He… FALSE
#> 5 V5 P10909 Hex(??-?)HexNAc(??-?)Hex(??-?)HexNAc(??-?)Hex(??-?)[H… FALSE
#> 6 V6 P04196 NeuAc(??-?)Hex(??-?)HexNAc(??-?)Hex(??-?)[NeuAc(??-?)… FALSE
#> 7 V7 P04196 Hex(??-?)HexNAc(??-?)Hex(??-?)[Hex(??-?)HexNAc(??-?)H… FALSE
#> 8 V8 P04196 dHex(??-?)Hex(??-?)HexNAc(??-?)Hex(??-?)[dHex(??-?)He… FALSE
#> 9 V9 P04196 Hex(??-?)HexNAc(??-?)Hex(??-?)[Hex(??-?)]Hex(??-?)Hex… FALSE
#> 10 V10 P04196 NeuAc(??-?)Hex(??-?)HexNAc(??-?)Hex(??-?)[HexNAc(??-?… FALSE
#> # ℹ 3,870 more rowsThe tempting approach: Multiple motifs at once (you might be tempted to do this…) 🤷♀️
# Don't do this
exp |>
mutate_var(
n_hex = have_motif(glycan_structure, "Hex(a1-"),
n_hexna = have_motif(glycan_structure, "HexNAc(a1-"),
n_dhex = have_motif(glycan_structure, "dHex(a1-")
)Hold on there, speed racer! 🛑 While this approach works, it’s not
very efficient, and your computer won’t thank you for it. Here’s why:
those three separate calls to have_motif() all perform
time-consuming validations and conversions on the same set of glycan
structures. It’s like washing the same dishes three times instead of
doing them all at once! 🍽️ Plus, there’s a lot of repetitive typing. You
have to type have_motif and glycan_structure
three times — talk about finger fatigue! 😴
The smart approach: Use the
add_motifs_lgl() or add_motifs_int() functions
instead! ⚡ They might look like simple syntactic sugar, but they’re
actually optimized powerhouses designed specifically for this exact
scenario:
exp2 <- exp |>
add_motifs_lgl(c(motif1 = "Hex(??-", motif2 = "HexNAc(??-", motif3 = "dHex(??-"))Voilà! 🎉 The motif annotations are now seamlessly integrated into your variable information tibble.
exp2 |>
get_var_info() |>
select(variable, motif1, motif2, motif3)
#> # A tibble: 3,880 × 4
#> variable motif1 motif2 motif3
#> <chr> <lgl> <lgl> <lgl>
#> 1 V1 TRUE TRUE FALSE
#> 2 V2 TRUE TRUE FALSE
#> 3 V3 TRUE TRUE FALSE
#> 4 V4 TRUE TRUE FALSE
#> 5 V5 TRUE TRUE FALSE
#> 6 V6 TRUE TRUE FALSE
#> 7 V7 TRUE TRUE FALSE
#> 8 V8 TRUE TRUE TRUE
#> 9 V9 TRUE TRUE FALSE
#> 10 V10 TRUE TRUE FALSE
#> # ℹ 3,870 more rowsBut wait, what can you actually do with these shiny new columns? 🤔 The possibilities are endless, but here’s a tantalizing example to get your creative juices flowing:
# You can perform pathway enrichment on all glycoproteins containing some motif:
exp2 |>
filter_var(motif1 == TRUE) |>
gly_enrich_reactome() # from the `glystats` package