Adds feature metadata to the giotto object
Usage
addFeatMetadata(
gobject,
feat_type = NULL,
spat_unit = NULL,
new_metadata,
vector_name = NULL,
by_column = FALSE,
column_feat_ID = NULL
)Arguments
- gobject
giotto object
- feat_type
feature type
- spat_unit
spatial unit
- new_metadata
new metadata to use)
- vector_name
(optional) custom name if you provide a single vector
- by_column
merge metadata based on feat_ID column in
fDataDT- column_feat_ID
column name of new metadata to use if by_column = TRUE
Details
You can add additional feature metadata in several manners:
1. Provide a data.table or data.frame with feature annotations in the same order as the feat_ID column in fDataDT(gobject) This is a bit risky and not the most recommended.
2. Provide a data.table or data.frame with feature annotations and specify which column contains the feature IDs, these feature IDs need to match with the feat_ID column in fDataDT(gobject)
3. Provide a vector or factor that is named with the feature IDs they correspond to. These names will be matched against the feat_ID column in fDataDT(gobject).
Examples
g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> python already initialized in this session
#> active environment : 'giotto_env'
#> python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#> "/usr/share/miniconda/envs/giotto_env/bin/python"
m <- fDataDT(g)
m <- m[, "feat_ID"]
m$new_feat_ID <- paste0("gene_", m$feat_ID)
g <- addFeatMetadata(
g,
new_metadata = m,
by_column = TRUE,
column_feat_ID = "feat_ID"
)
fDataDT(g)
#> feat_ID nr_cells perc_cells total_expr mean_expr mean_expr_det
#> <char> <int> <num> <num> <num> <num>
#> 1: Gna12 502 80.448718 1987.1333 3.1845085 3.958433
#> 2: Ccnd2 357 57.211538 1254.4730 2.0103734 3.513930
#> 3: Btbd17 252 40.384615 781.9747 1.2531645 3.103074
#> 4: Sox9 309 49.519231 1019.7255 1.6341755 3.300083
#> 5: Sez6 504 80.769231 2177.4740 3.4895416 4.320385
#> ---
#> 630: Lhfpl3 235 37.660256 732.3595 1.1736530 3.116423
#> 631: C030029H02Rik 72 11.538462 203.0811 0.3254506 2.820572
#> 632: Gm19935 57 9.134615 172.1079 0.2758139 3.019436
#> 633: 9630013A20Rik 52 8.333333 157.2602 0.2520196 3.024235
#> 634: 2900040C04Rik 61 9.775641 179.7015 0.2879831 2.945926
#> hvf new_feat_ID
#> <char> <char>
#> 1: no gene_Gna12
#> 2: no gene_Ccnd2
#> 3: no gene_Btbd17
#> 4: no gene_Sox9
#> 5: no gene_Sez6
#> ---
#> 630: no gene_Lhfpl3
#> 631: no gene_C030029H02Rik
#> 632: no gene_Gm19935
#> 633: no gene_9630013A20Rik
#> 634: no gene_2900040C04Rik