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Write up a short tidy multi-assay proteomics tutorial using QFeatures #20
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Here's, for illustration, an example pipeline that process data in a QFeatures object:
qf |>
filterFeatures(~ label > 0) |> ## 1
filterFeatures(~ rank == 1) |> ## 2
filterFeatures(~ spectrum_fdr < 0.05) |> ## 3
zeroIsNA(1:3) |> ## 4
logTransform(i = 1:3, ## 5
name = paste0("log_", names(qf))) |>
aggregateFeaturesOverAssays(i = 4:6, ## 6
fcol = "peptide",
name = sub("psm", "peptide", names(qf)),
fun = colMedians,
na.rm = TRUE) |>
joinAssays(i = 7:9, ## 7
name = "peptides") |>
normalize(i = 10, ## 8
name = "norm_peptides",
method = "center.median") |>
aggregateFeatures(i = "norm_peptides", ## 9
name = "proteins",
fcol = "proteins",
fun = colMedians,
na.rm = TRUE) From the sager vignette. |
And by the way, there's already a > fts1
An instance of class QFeatures containing 2 assays:
[1] assay1: SummarizedExperiment with 10 rows and 4 columns
[2] assay2: SummarizedExperiment with 4 rows and 4 columns
> colData(fts1)
DataFrame with 4 rows and 2 columns
Var1 Var2
<numeric> <character>
S1 -1.0588267 A
S2 0.0199355 B
S3 -0.0761972 C
S4 -1.0501452 D
> longFormat(fts1, colvars = names(colData(fts1)))
DataFrame with 56 rows and 7 columns
assay primary rowname colname value Var1 Var2
<character> <character> <factor> <factor> <integer> <numeric> <character>
1 assay1 S1 a S1 1 -1.05883 A
2 assay1 S1 b S1 2 -1.05883 A
3 assay1 S1 c S1 3 -1.05883 A
4 assay1 S1 d S1 4 -1.05883 A
5 assay1 S1 e S1 5 -1.05883 A
... ... ... ... ... ... ... ...
52 assay2 S3 d S3 12 -0.0761972 C
53 assay2 S4 a S4 13 -1.0501452 D
54 assay2 S4 b S4 14 -1.0501452 D
55 assay2 S4 c S4 15 -1.0501452 D
56 assay2 S4 d S4 16 -1.0501452 D |
This is great! I will change this challenge to 1) write up a short tutorial for |
This ideally would start with support for
MultiAssayExperiment
which would directly work for QFeaturesThe text was updated successfully, but these errors were encountered: