Initial commit

README.Rmd

@@ -25,7 +25,7 @@ This R package complements the performance evaluation analysis for the manuscrip
 You can install the released version of HVSlimPred from gitlab with:
 
 ``` r
-devtools::install_gitlab("https://gitlab.ebi.ac.uk/petsalakilab/hvslimpred")
+devtools::install_gitlab("petsalakilab/HVSlimPred", host = "gitlab.ebi.ac.uk")
 ```
 
 ## Get Protein-level evaluation metrics

README.md

+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+# HVSlimPred
+
+<!-- badges: start -->
+
+<!-- badges: end -->
+
+This R package complements the performance evaluation analysis for the
+manuscript entitled “Identifying novel functional linear motifs using
+the host-viral protein interaction network and the principle of
+convergent evolution”.
+
+## Installation
+
+You can install the released version of HVSlimPred from gitlab with:
+
+``` r
+devtools::install_gitlab("petsalakilab/HVSlimPred", host = "gitlab.ebi.ac.uk")
+```
+
+## Get Protein-level evaluation metrics
+
+For protein-level enrichment, we measured the enrichment of
+true-positives in our predicted dataset using a one-tailed fisher-exact
+test, where the odds ratio represents the magnitude of the enrichment.
+True-positives are the number of motif-carrying proteins present in both
+the predicted dataset and the ELM dataset regardless of whether the
+predicted protein has the right motif or found in the right location
+
+The output of the following command is a data frame containing all the
+relevant protein-level performance metrics for each domain enrichment
+filter in addition to the non-filtered qslim output.
+
+``` r
+library(HVSlimPred)
+prot_eval_metrics = HV_prot_level_eval()
+```
+
+## Get Motif-level evaluation metrics
+
+For motif-level enrichment, we simply cannot use a binary classification
+as we did for protein-level evaluation because in reality, predicted
+motifs are partially correct to some extent as they might contain
+true-positive residues in a given sequence stretch, and therefore we
+used a re-implemented version of the evaluation protocol proposed in
+[Prytuliak et
+al. 2017](https://academic.oup.com/nar/article/45/W1/W470/3782606)
+instead of binary classification, where we computed the common
+performance metrics (Recall, precision F1, etc .. ) both residue-wise
+and site-wise given that the motif-carrying proteins are also found in
+the ELM benchmarking dataset. So this analysis was not performed on
+proteins not reported in the ELM dataset.
+
+The output of the following command is a data frame containing all the
+relevant motif-level performance metrics for each domain enrichment
+filter in addition to the non-filtered qslim output.
+
+``` r
+library(HVSlimPred)
+motif_eval_metrics = HV_motif_level_eval()
+```
+
+## Get Protein-domain interactions evaluation metrics
+
+For evaluating protein-domain interactions we measured the enrichment of
+true-positive interactions between a given motif-carrying protein and
+its associated domains as reported in the ELM interaction dataset. As in
+the motif-level evaluation, this analysis was performed only on the
+motif-carrying proteins reported in the ELM interactions dataset, where
+true-positives represents the number of correctly associated domains for
+a given motif-carrying protein and then summed over all motif-carrying
+proteins in the predicted dataset.
+
+The output of the following command is a data frame containing all the
+relevant protein-domain interactions’ performance metrics for each
+domain enrichment filter.
+
+``` r
+library(HVSlimPred)
+ProtDom_int_eval_metrics = HV_prot_dom_int_eval()
+```