Retrieves predicted protein-protein interactions from the PrePPI database (http://honig.c2b2.columbia.edu/preppi). The interactions in this table are supposed to be correct with a > 0.5 probability.
Arguments
- ...
Minimum values for the scores. The available scores are: str, protpep, str_max, red, ort, phy, coexp, go, total, exp and final. Furthermore, an operator can be passed, either
.op = '&'or.op = '|', which is then used for combined filtering by multiple scores.
Details
PrePPI is a combination of many prediction methods, each resulting a score. For an explanation of the scores see https://honiglab.c2b2.columbia.edu/hfpd/help/Manual.html. The minimum, median and maximum values of the scores:
| Score | Minimum | Median | Maximum |
| ------- | ------- | -------- | ------------------ |
| str | 0 | 5.5 | 6,495 |
| protpep | 0 | 3.53 | 38,138 |
| str_max | 0 | 17.9 | 38,138 |
| red | 0 | 1.25 | 24.4 |
| ort | 0 | 0 | 5,000 |
| phy | 0 | 2.42 | 2.42 |
| coexp | 0 | 2.77 | 45.3 |
| go | 0 | 5.86 | 181 |
| total | 0 | 1,292 | 106,197,000,000 |
| exp | 1 | 958 | 4,626 |
| final | 600 | 1,778 | 4.91e14 |Examples
preppi <- preppi_download()
preppi
#> # A tibble: 1,545,710 × 15
#> prot1 prot2 str_s…¹ protp…² str_m…³ red_s…⁴ ort_s…⁵ phy_s…⁶ coexp…⁷ go_sc…⁸
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Q13131 P14625 18.6 6.45 18.6 4.25 0.615 2.42 9.47 10.8
#> 2 P06400 Q96N96 1.83 14.3 14.3 4.25 0 2.42 2.11 10.8
#> 3 Q7Z6V5 Q8NCE0 4.57 0 4.57 0 0 1.60 9.47 24.1
#> 4 P37058 P15428 485. 0 485. 1.77 0.615 2.42 1.18 2.07
#> 5 O00468 Q9NR23 34.0 0 34.0 0.512 0 2.42 2.77 5.86
#> 6 Q9Y2X7 Q9NPJ6 0 2.59 2.59 1.01 0 2.42 1.58 0
#> 7 Q8IY84 Q9Y3S1 254. 7.35 254. 0.913 0.615 2.42 2.11 2.07
#> 8 Q13315 Q96T68 1.21 9.34 9.34 19.3 0.615 2.42 9.47 0.89
#> 9 Q6GYQ0 Q92529 2.67 20.6 20.6 0 0 2.42 3.90 5.86
#> 10 P08575 O14545 0 0 0 0 0 2.42 3.90 105.
#> # … with 1,545,700 more rows, 5 more variables: total_score <dbl>, dbs <chr>,
#> # pubs <chr>, exp_score <dbl>, final_score <dbl>, and abbreviated variable
#> # names ¹str_score, ²protpep_score, ³str_max_score, ⁴red_score, ⁵ort_score,
#> # ⁶phy_score, ⁷coexp_score, ⁸go_score
# # A tibble: 1,545,710 x 15
# prot1 prot2 str_score protpep_score str_max_score red_score ort_score
# <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 Q131. P146. 18.6 6.45 18.6 4.25 0.615
# 2 P064. Q96N. 1.83 14.3 14.3 4.25 0
# 3 Q7Z6. Q8NC. 4.57 0 4.57 0 0
# 4 P370. P154. 485. 0 485. 1.77 0.615
# 5 O004. Q9NR. 34.0 0 34.0 0.512 0
# # . with 1,545,700 more rows, and 8 more variables: phy_score <dbl>,
# # coexp_score <dbl>, go_score <dbl>, total_score <dbl>, dbs <chr>,
# # pubs <chr>, exp_score <dbl>, final_score <dbl>