Calculate Derived Traits

This function calculates derived traits from a glyexp::GlycomicSE() or glyexp::GlycoproteomicSE() object. For glycomics data, it calculates the derived traits directly. For glycoproteomics data, each glycosite is treated as a separate glycome, and derived traits are calculated in a site-specific manner.

Usage

derive_traits(exp, trait_fns = NULL, mp_fns = NULL, mp_cols = NULL)

Arguments

exp: A glyexp::GlycomicSE() or glyexp::GlycoproteomicSE() object. Before using this function, you should preprocess the data using the glyclean package. For glycoproteomics data, the data should be aggregated to the "gfs" (glycoforms with structures) level using glyclean::aggregate(). Also, please make sure that the glycan_structure column is present in SummarizedExperiment::rowData(), as not all glycoproteomics identification softwares provide this information. "glycan_structure" can be a glyrepr::glycan_structure() vector, or a character vector of glycan structure strings supported by glyparse::auto_parse().
trait_fns: A named list of derived trait functions created by trait factories. Names of the list are the names of the derived traits. Default is NULL, which means all derived traits in traits_basic() are calculated.
mp_fns: A named list of meta-property functions. This parameter is useful if your trait functions use custom meta-properties other than those in all_mp_fns(). Default is NULL, which means all meta-properties in all_mp_fns() are used.
mp_cols: A character vector of column names in SummarizedExperiment::rowData() to use as meta-properties. If names are provided, they will be used as names of the meta-properties, otherwise the column names will be used. When mp_cols is specified, the selected columns overwrite meta-properties introduced by mp_fns with the same names, including built-in meta-properties. Default is NULL, which means all columns in rowData() are available as meta-properties by their existing names. In this default mode, meta-properties introduced by mp_fns take precedence over rowData() columns with the same names.

Value

New GlycomicSE and GlycoproteomicSE inputs return a plain SummarizedExperiment; compatible legacy glyexp inputs preserve their legacy container type. Instead of "quantification of each glycan on each glycosite in each sample", its assay contains "the value of each derived trait on each glycosite in each sample", with the following columns in rowData():

trait: derived trait name
explanation: a concise English explanation of the trait

For glycoproteomics data, with additional columns:

protein: protein ID
protein_site: the glycosite position on the protein

Other columns in rowData() (e.g. gene) are retained if they have "many-to-one" relationship with glycosites (unique combinations of protein, protein_site). That is, each glycosite cannot have multiple values for these columns. gene is a common example, as a glycosite can only be associate with one gene. Descriptions about glycans are not such a column, as a glycosite can have multiple glycans, thus having multiple descriptions. Columns not having this relationship with glycosites will be dropped. Don't worry if you cannot understand this logic, as long as you know that this function will try its best to preserve useful information.

colData() and metadata() are not modified, except that the exp_type field of metadata() is set to "traitomics" for glycomics data and "traitproteomics" for glycoproteomics data.

Examples

library(glyexp)
library(SummarizedExperiment)
library(glyclean)
#> 
#> Attaching package: ‘glyclean’
#> The following object is masked from ‘package:S4Vectors’:
#> 
#>     aggregate
#> The following object is masked from ‘package:stats’:
#> 
#>     aggregate

gp_se <- real_experiment |>
  auto_clean() |>
  slice_sample_row(n = 100)
#> 
#> ── Removing variables with too many missing values ──
#> 
#> ℹ Applying preset "discovery"...
#> ℹ Total removed: 24 (0.56%) variables.
#> ✔ Variable removal completed.
#> 
#> ── Normalizing data ──
#> 
#> ℹ Normalization method: `normalize_median()`
#> ℹ Reason: default for "glycoproteomics".
#> ✔ Normalization completed.
#> 
#> ── Imputing missing values ──
#> 
#> ℹ Imputation method: `impute_min_prob()`
#> ℹ Reason: default for "glycoproteomics" with n_samples < 30.
#> ✔ Imputation completed.
#> 
#> ── Aggregating data ──
#> 
#> ℹ Aggregating to "gfs" level
#> ✔ Aggregation completed.
#> 
#> ── Normalizing data again ──
#> 
#> ℹ Normalization method: `normalize_median()`
#> ℹ Reason: default for "glycoproteomics".
#> ✔ Normalization completed.
#> 
#> ── Correcting batch effects ──
#> 
#> ℹ Batch column batch not found in sample_info. Skipping batch correction.
#> ✔ Batch correction completed.
trait_se <- derive_traits(gp_se)
rowData(trait_se)
#> DataFrame with 798 rows and 5 columns
#>                     protein protein_site       trait        gene
#>                 <character>    <integer> <character> <character>
#> O75882-1073-TM       O75882         1073          TM        ATRN
#> O75882-1073-TH       O75882         1073          TH        ATRN
#> O75882-1073-TC       O75882         1073          TC        ATRN
#> O75882-1073-MM       O75882         1073          MM        ATRN
#> O75882-1073-CA2      O75882         1073         CA2        ATRN
#> ...                     ...          ...         ...         ...
#> Q14624-517-TFa       Q14624          517         TFa       ITIH4
#> Q14624-517-TB        Q14624          517          TB       ITIH4
#> Q14624-517-GS        Q14624          517          GS       ITIH4
#> Q14624-517-AG        Q14624          517          AG       ITIH4
#> Q14624-517-TS        Q14624          517          TS       ITIH4
#>                            explanation
#>                            <character>
#> O75882-1073-TM  Proportion of high-m..
#> O75882-1073-TH  Proportion of hybrid..
#> O75882-1073-TC  Proportion of comple..
#> O75882-1073-MM  Abundance-weighted m..
#> O75882-1073-CA2 Proportion of bi-ant..
#> ...                                ...
#> Q14624-517-TFa  Proportion of arm-fu..
#> Q14624-517-TB   Proportion of glycan..
#> Q14624-517-GS   Abundance-weighted m..
#> Q14624-517-AG   Abundance-weighted m..
#> Q14624-517-TS   Proportion of sialyl..
assay(trait_se)[1:5, 1:5]
#>                 C1 C2 C3 H1 H2
#> O75882-1073-TM   0  0  0  0  0
#> O75882-1073-TH   1  1  1  1  1
#> O75882-1073-TC   0  0  0  0  0
#> O75882-1073-MM  NA NA NA NA NA
#> O75882-1073-CA2 NA NA NA NA NA
colData(trait_se)
#> DataFrame with 12 rows and 1 column
#>        group
#>     <factor>
#> C1         C
#> C2         C
#> C3         C
#> H1         H
#> H2         H
#> ...      ...
#> M2         M
#> M3         M
#> Y1         Y
#> Y2         Y
#> Y3         Y

# By default, only basic traits are calculated
names(traits_basic())
#>  [1] "TM"  "TH"  "TC"  "MM"  "CA2" "CA3" "CA4" "TF"  "TFc" "TFa" "TB"  "GS" 
#> [13] "AG"  "TS" 

# You can calculate detailed traits in `traits_detailed()`
more_trait_se <- derive_traits(gp_se, trait_fns = traits_detailed())
more_trait_se
#> class: SummarizedExperiment 
#> dim: 3534 12 
#> metadata(3): exp_type glycan_type quant_method
#> assays(1): abundance
#> rownames(3534): O75882-1073-TM O75882-1073-TH ... Q14624-517-A3GS
#>   Q14624-517-A4GS
#> rowData names(5): protein protein_site trait gene explanation
#> colnames(12): C1 C2 ... Y2 Y3
#> colData names(1): group

Usage

Arguments

Value

See also

Examples