Description Usage Arguments Value Examples

This function integrates the kinase-substrate predictions, directionality of phosphopeptide fold change and signficance to assess local connectivity (swing) of kinase-substrate networks. The final score is a normalised and weighted score of predicted kinase activity. If permutations are selected, network node:edges are permutated. P-values will be calculated for both ends of the distribution of swing scores (positive and negative swing scores).

1 2 3 |

`input_data` |
A data.frame of phoshopeptide data. Must contain 4 columns and the following format must be adhered to. Column 1 - Annotation, Column 2 - centered peptide sequence, Column 3 - Fold Change [-ve to +ve], Column 4 - p-value [0-1]. This must be the same dataframe used in scoreSequences() |

`pwm_in` |
List of PWMs created using buildPWM() |

`pwm_scores` |
List of PWM-substrate scores created using scoreSequences() |

`pseudo_count` |
Pseudo-count acts at two levels. 1) It adds a small number to the counts to avoid zero divisions, which also 2) avoids log-zero transformations. Note that this means that pos, neg and all values in the output table include the addition of the pseudo-count. Default: "1" |

`p_cut_pwm` |
Significance level for determining a significant kinase-substrate enrichment. Default: "0.05" |

`p_cut_fc` |
Significance level for determining a significant level of Fold-change in the phosphoproteomics data. Default: "0.05" |

`permutations` |
Number of permutations to perform. This will shuffle the kinase-subtrate edges of the network n times. To not perform permutations and only generate the scores, set permutations=1 or permutations=FALSE. Default: "1000" |

`return_network` |
Option to return an interaction network for visualising in cystoscape. Default = FALSE |

`verbose` |
Turn verbosity on/off. To turn on, verbose=TRUE. Options are: "TRUE, FALSE". Default=FALSE |

A data.table of swing scores

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ```
## import data
data(example_phosphoproteome)
data(phosphositeplus_human)
## clean up the annotations
## sample 100 data points for demonstration
sample_data <- head(example_phosphoproteome, 100)
annotated_data <- cleanAnnotation(input_data = sample_data)
## build the PWM models:
set.seed(1234)
sample_pwm <- phosphositeplus_human[sample(nrow(phosphositeplus_human),
1000),]
pwms <- buildPWM(sample_pwm)
## score the PWM - substrate matches
## Using a "random" background, to calculate the p-value of the matches
## Using n = 100 for demonstration
## set.seed for reproducibility
set.seed(1234)
substrate_scores <- scoreSequences(input_data = annotated_data,
pwm_in = pwms,
background = "random",
n = 100)
## Use substrate_scores and annotated_data data to predict kinase activity.
## This will permute the network node and edges 10 times for demonstration.
## set.seed for reproducibility
set.seed(1234)
swing_output <- swing(input_data = annotated_data,
pwm_in = pwms,
pwm_scores = substrate_scores,
permutations = 10)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.