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Differential abundance with the Pattern Profiler cluster of each protein

Source: R/Functional_Input.R
deps_with_clusters.Rd

Joins the differential-abundance results to the soft clustering, giving the table a functional-enrichment or over-representation analysis needs: the statistics that say which proteins moved, and the cluster that says which pattern they follow.

Usage

deps_with_clusters(
  DEPs_results,
  pattern_profiler,
  assignment = c("primary", "all"),
  min_membership = NULL,
  comparison = NULL,
  significant_only = FALSE
)

Arguments

DEPs_results

Differential-abundance results, the DEPs_results element of process_proteomics(). Needs at least Protein.IDs and Comparison.

pattern_profiler

Result of pattern_profiler_analysis(), or NULL for no clustering at all, in which case the three cluster columns are returned as NA and the shape of the table is unchanged.

assignment

"primary" (default) for one row per protein and comparison, carrying the cluster of highest membership; "all" for one row per protein, comparison and cluster.

min_membership

Optional extra membership threshold. It can only be stricter than the one the analysis ran with: pairs below that one were discarded when long_output was built and cannot be recovered here. A protein left with no cluster keeps its row, with NA.

comparison

Optional character vector of comparisons to keep. The default, NULL, keeps them all.

significant_only

Keep only the rows classified Up or Down. The classification is the one the analysis made, with its own alpha and logFC_threshold; nothing is recomputed here.

Value

A data frame with the columns of DEPs_results followed by Cluster, Membership and ClusterRank (1 is the protein's dominant cluster).

Details

The join is a left join from the results, so every protein that was tested survives it, with or without a cluster. That matters more than it looks: the complete set of tested proteins is the background an enrichment is computed against, and a protein with no cluster – one that was never selected for the clustering, or that the clustering dropped – belongs in that background rather than in the bin.

Clustering is fuzzy, so a protein can belong to several clusters at once. assignment decides what to do about it. "primary" keeps only the dominant pattern of each protein and returns exactly one row per protein and comparison, which is what most enrichment tools expect. "all" keeps every association above the threshold, which is the honest representation of a soft clustering and the one to use when the analysis downstream can weight by membership.

See also

nadia_deps_with_clusters() for the same table read from a .nadia file, and pattern_profiler_analysis() for the clustering.

Examples

data(nadia_dia)
res <- process_proteomics(nadia_dia, verbose = FALSE)

# Without a clustering the shape is the same and the clusters are NA.
fi <- deps_with_clusters(res$DEPs_results, NULL)
#> Warning: No Pattern Profiler result: the cluster columns are all NA.
head(fi)
#>         Protein.IDs Comparison Gene.Names       logFC      P.Value    adj.P.Val
#> 1        A0A024RBG1        B-A     NUDT4B  0.08293965 5.335637e-01 7.389228e-01
#> 2        A0A024RBG1        D-A     NUDT4B -0.01391145 9.160610e-01 9.511693e-01
#> 3        A0A024RBG1        D-B     NUDT4B -0.09685110 4.687369e-01 7.151013e-01
#> 4 A0A140T897;P02769        B-A        ALB  0.13439560 5.731060e-04 3.118509e-03
#> 5 A0A140T897;P02769        D-B        ALB  0.20481573 1.713786e-05 7.760613e-05
#> 6 A0A140T897;P02769        D-A        ALB  0.33921133 1.394707e-07 5.778484e-07
#>      Change         Assay MissGlobal MissComp MissCND1 MissCND2 Cluster
#> 1 No Change Impseqrob_min          0        0        0        0      NA
#> 2 No Change Impseqrob_min          0        0        0        0      NA
#> 3 No Change Impseqrob_min          0        0        0        0      NA
#> 4        Up Impseqrob_min          0        0        0        0      NA
#> 5        Up Impseqrob_min          0        0        0        0      NA
#> 6        Up Impseqrob_min          0        0        0        0      NA
#>   Membership ClusterRank
#> 1         NA          NA
#> 2         NA          NA
#> 3         NA          NA
#> 4         NA          NA
#> 5         NA          NA
#> 6         NA          NA

if (requireNamespace("Mfuzz", quietly = TRUE) &&
    requireNamespace("Biobase", quietly = TRUE) &&
    requireNamespace("e1071", quietly = TRUE)) {
    pp <- pattern_profiler_analysis(res$se_proc, res$DEPs_results,
                                    assay_name = "Impseqrob_min",
                                    verbose = FALSE)

    primary <- deps_with_clusters(res$DEPs_results, pp)
    nrow(primary) == nrow(res$DEPs_results)   # the background is intact

    soft <- deps_with_clusters(res$DEPs_results, pp, assignment = "all")
    table(soft$ClusterRank)
}
#> 
#>    1    2 
#> 2382  219