View source: R/ontology_goseq.R
goseq_msigdb | R Documentation |
goseq is probably the easiest method to push varying data types into. Thus it was the first thing I thought of when looking to push MSigDB data into a GSEA method.
goseq_msigdb(
sig_genes,
signatures = "c2BroadSets",
data_pkg = "GSVAdata",
signature_category = "c2",
current_id = "ENSEMBL",
required_id = "ENTREZID",
length_db = NULL,
doplot = TRUE,
adjust = 0.1,
pvalue = 0.1,
length_keytype = "transcripts",
go_keytype = "entrezid",
goseq_method = "Wallenius",
padjust_method = "BH",
excel = NULL,
orgdb = "org.Hs.eg.db"
)
sig_genes |
Character list of genes deemed significant. I think in the current implementation this must be just a list of IDs as opposed to the full dataframe of interesting genes because we likely need to convert IDs. |
signatures |
Used by load_gmt_signatures(), the signature file or set. |
data_pkg |
Used by load_gmt_signatures(). |
signature_category |
Ibid, but the name of the signatures group. |
current_id |
Used by convert_msig_ids(), when converting IDs, the name of the existing type. |
required_id |
What type to convert to in convert_msig_ids(). |
length_db |
Dataframe of lengths. It is worth noting that goseq explicitly states that one might wish to use other potentially confounding factors here, but they only examine lenghts in their paper. Starting with this parameter, everything is just passed directly to simple_goseq() |
doplot |
Print the prior plot? |
adjust |
passed to simple_goseq() |
pvalue |
passed to simple_goseq() |
length_keytype |
passed to simple_goseq() |
go_keytype |
passed to simple_goseq() |
goseq_method |
passed to simple_goseq() |
padjust_method |
passed to simple_goseq() |
excel |
passed to simple_goseq() |
orgdb |
Ideally used to help goseq collect lengths. |
Some goseq data!
[gsva] [goseq]
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