Heya,

First, thanks for the STRINGdb package :)

I was having an issue where incorrect STRING_ids were getting assigned, and looking into it i think I found the problem is coming from the get_aliases() method. Apologies for this being long, but just want to lay out what I've seen.

For example, searching for TAIR ID "AT1G01030" is returning a STRING_id of "3702.AT1G01040.2", while it should be returning "3702.AT1G01030.1". The mapping seems to be correct in the underlying protein.aliases table (as shown below), so I started looking into the method.

Demonstration of problem in R

library(tidyverse)
library(STRINGdb)

packageVersion("STRINGdb")
# ‘2.6.0’ # note i'm using 2.6.0, but the method in question looks like it has the same code in the latest 2.8.0

string_db <- STRINGdb$new(version = "11.5",
                          species = 3702)

string_db$mp("AT1G01030")
# "3702.AT1G01040.2"

# we can see this is what's set up by the get_aliases() function like so
alias_df <- string_db$get_aliases()

alias_df %>% filter(alias == "AT1G01030")
#              STRING_id     alias
# 54830 3702.AT1G01040.2 AT1G01030
    # where the alias is linked to the incorrect STRING_id

## and we can see the info doesn't come in that way from the initial table
initial_alias_df <- read.table(paste0(tempdir(), "/", "3702.protein.aliases.v11.5.txt.gz"), 
                               skip = 1, sep = "\t", header = FALSE, quote = "", 
                               stringsAsFactors = FALSE, fill = TRUE,
                               col.names = c("STRING_id", "alias", "sources"))

initial_alias_df %>% filter(alias == "AT1G01030")
#          STRING_id     alias                  sources
# 1 3702.AT1G01030.1 AT1G01030     Ensembl_ArrayExpress
# 2 3702.AT1G01030.1 AT1G01030          Ensembl_Genomes
# 3 3702.AT1G01030.1 AT1G01030       Ensembl_TAIR_LOCUS
# 4 3702.AT1G01030.1 AT1G01030 Ensembl_TAIR_TRANSLATION
# 5 3702.AT1G01030.1 AT1G01030             Ensembl_gene
# 6 3702.AT1G01030.1 AT1G01030            Plants_Source
# 7 3702.AT1G01030.1 AT1G01030                  TAIR_ID
    # as all are in there linked to 3702.AT1G01030.1

Where I think the problem is in get_aliases()

# in the get_aliases method, the protein table is read in
proteins <- string_db$get_proteins()

# and the alias table is read in
aliasDf <- read.table(paste0(tempdir(), "/", "3702.protein.aliases.v11.5.txt.gz"), 
                               skip = 1, sep = "\t", header = FALSE, quote = "", 
                               stringsAsFactors = FALSE, fill = TRUE,
                               col.names = c("STRING_id", "alias", "sources"))

# at this point we're good (same as above)
aliasDf %>% filter(alias == "AT1G01030")
#          STRING_id     alias                  sources
# 1 3702.AT1G01030.1 AT1G01030     Ensembl_ArrayExpress
# 2 3702.AT1G01030.1 AT1G01030          Ensembl_Genomes
# 3 3702.AT1G01030.1 AT1G01030       Ensembl_TAIR_LOCUS
# 4 3702.AT1G01030.1 AT1G01030 Ensembl_TAIR_TRANSLATION
# 5 3702.AT1G01030.1 AT1G01030             Ensembl_gene
# 6 3702.AT1G01030.1 AT1G01030            Plants_Source
# 7 3702.AT1G01030.1 AT1G01030                  TAIR_ID

# the aliasDf is then cut down to the first two columns
aliasDf = subset(aliasDf, select = c("STRING_id", "alias"))

# next 3 protein sub-tables are generated like so
pr1 <- data.frame(STRING_id = proteins$protein_external_id, 
                 alias = proteins$preferred_name,
                 stringsAsFactors = FALSE)

pr2 <- data.frame(STRING_id = proteins$protein_external_id,
                 alias = proteins$protein_external_id, 
                 stringsAsFactors = FALSE)

pr3 <- data.frame(STRING_id = proteins$protein_external_id,
                 alias = unlist(strsplit(proteins$protein_external_id,
                                "\\."))[seq(from = 2, to = 2 * nrow(proteins), by = 2)],
                 stringsAsFactors = FALSE)

# if we look at that 3rd table, the indexing on the split STRING_ids is not pulling out the core ID 
# each time, but is cycling through the taxid and version also

unlist(strsplit(proteins$protein_external_id, "\\."))[seq(from = 2, to = 2 * nrow(proteins), by = 2)] %>% head
# "AT1G01010" "3702"      "1"         "AT1G01030" "3702"      "2"

head(pr3)
#          STRING_id     alias
# 1 3702.AT1G01010.1 AT1G01010
# 2 3702.AT1G01020.1      3702
# 3 3702.AT1G01030.1         1
# 4 3702.AT1G01040.2 AT1G01030
# 5 3702.AT1G01050.1      3702
# 6 3702.AT1G01060.2         2
    # which happens to be improperly linking some, like on row 4 there our example "AT1G01030" to "3702.AT1G01040.2"

# this holds true after merging them and then in the final returned dataframe
aliasDf2 <- rbind(pr1, pr2, pr3, aliasDf)

aliasDf2 %>% filter(alias == "AT1G01030")
#         STRING_id     alias
# 1 3702.AT1G01040.2 AT1G01030
# 2 3702.AT1G01030.1 AT1G01030
# 3 3702.AT1G01030.1 AT1G01030
# 4 3702.AT1G01030.1 AT1G01030
# 5 3702.AT1G01030.1 AT1G01030
# 6 3702.AT1G01030.1 AT1G01030
# 7 3702.AT1G01030.1 AT1G01030
# 8 3702.AT1G01030.1 AT1G01030
    # where that first row is linking improperly

Suggested fix

# if the indexing step during the pr3-table part counts by 3s instead of 2s, things seem to work out
pr3_mod <- data.frame(STRING_id = proteins$protein_external_id,
                      alias = unlist(strsplit(proteins$protein_external_id,
                                     "\\."))[seq(from = 2, to = 3 * nrow(proteins), by = 3)],
                      stringsAsFactors = FALSE)

head(pr3_mod)
#          STRING_id     alias
# 1 3702.AT1G01010.1 AT1G01010
# 2 3702.AT1G01020.1 AT1G01020
# 3 3702.AT1G01030.1 AT1G01030
# 4 3702.AT1G01040.2 AT1G01040
# 5 3702.AT1G01050.1 AT1G01050
# 6 3702.AT1G01060.2 AT1G01060

# then after merging, only the correct ones are still linked
aliasDf2_mod <- rbind(pr1, pr2, pr3_mod, aliasDf)

aliasDf2_mod %>% filter(alias == "AT1G01030")
#          STRING_id     alias
# 1 3702.AT1G01030.1 AT1G01030
# 2 3702.AT1G01030.1 AT1G01030
# 3 3702.AT1G01030.1 AT1G01030
# 4 3702.AT1G01030.1 AT1G01030
# 5 3702.AT1G01030.1 AT1G01030
# 6 3702.AT1G01030.1 AT1G01030
# 7 3702.AT1G01030.1 AT1G01030
# 8 3702.AT1G01030.1 AT1G01030

I've never used STRING_ids before, but i suspect maybe there was a change at some point in how they are formatted such that the splitting/indexing above needs to be shifted ¯\_(ツ)_/¯

I couldn't find somewhere to report an issue, so I'm posting here as an "Ask a question". If there is somewhere I should log this as an issue, please point me there.

Thanks!

Session info

sessionInfo()
# R version 4.1.2 (2021-11-01)
# Platform: x86_64-conda-linux-gnu (64-bit)
# Running under: Ubuntu 20.04.4 LTS
# 
# Matrix products: default
# BLAS/LAPACK: /global/smf/miniconda38_admin/envs/RNAseq_Rtools_03-2022/lib/libopenblasp-r0.3.18.so
# 
# locale:
#  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
#  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
#  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
#  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
#  [9] LC_ADDRESS=C               LC_TELEPHONE=C
# [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base
# 
# other attached packages:
#  [1] STRINGdb_2.6.0  forcats_0.5.1   stringr_1.4.0   dplyr_1.0.7
#  [5] purrr_0.3.4     readr_2.1.2     tidyr_1.2.0     tibble_3.1.6
#  [9] ggplot2_3.3.5   tidyverse_1.3.1
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_1.0.8         lubridate_1.8.0    png_0.1-7          gtools_3.9.2
#  [5] assertthat_0.2.1   utf8_1.2.2         chron_2.3-56       R6_2.5.1
#  [9] cellranger_1.1.0   plyr_1.8.6         backports_1.4.1    reprex_2.0.1
# [13] RSQLite_2.2.8      httr_1.4.2         sqldf_0.4-11       pillar_1.7.0
# [17] gplots_3.1.1       rlang_0.4.12       readxl_1.3.1       rstudioapi_0.13
# [21] blob_1.2.2         hash_3.0.1         gsubfn_0.7         proto_1.0.0
# [25] igraph_1.2.11      RCurl_1.98-1.6     bit_4.0.4          munsell_0.5.0
# [29] broom_0.7.12       compiler_4.1.2     modelr_0.1.8       pkgconfig_2.0.3
# [33] tidyselect_1.1.1   fansi_1.0.2        crayon_1.5.0       tzdb_0.2.0
# [37] dbplyr_2.1.1       withr_2.5.0        bitops_1.0-7       grid_4.1.2
# [41] jsonlite_1.8.0     gtable_0.3.0       lifecycle_1.0.1    DBI_1.1.2
# [45] magrittr_2.0.2     scales_1.1.1       KernSmooth_2.23-20 cachem_1.0.6
# [49] cli_3.2.0          stringi_1.7.6      fs_1.5.2           xml2_1.3.3
# [53] ellipsis_0.3.2     generics_0.1.2     vctrs_0.3.8        RColorBrewer_1.1-2
# [57] tools_4.1.2        bit64_4.0.5        glue_1.6.2         hms_1.1.1
# [61] plotrix_3.8-2      fastmap_1.1.0      colorspace_2.0-3   caTools_1.18.2
# [65] rvest_1.0.2        memoise_2.0.1      haven_2.4.3