Description Usage Arguments Value Examples

Function `diffusion.input`

computes the diffusion input
score to perform diffusion in graphs.
It uses the functions `diffusion.input.Binary`

and
`diffusion.input.probability`

Function `diffusion.input.Binary`

computes the binary
diffusion input score.

Function `diffusion.input.probability`

computes the
probability diffusion input score.

Function `finalResults`

prepares the final table ranked by
the diffusion scores computed.

Function `modifiedTabs`

prepares the tables that result from
the matching stage and the
filtering stage to evaluate their performance.

Function `set.diffusion`

diffuses heat in a specific
Kernel given a matrix of compounds and its
diffusion input score.

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 | ```
diffusion.input(
df,
input.type = "probability",
background = NULL,
Unique.Annotation = FALSE,
do.Par = TRUE,
nClust = 2
)
diffusion.input.Binary(df, do.Par = TRUE, nClust = 2)
diffusion.input.probability(df, do.Par = TRUE, nClust = 2)
finalResults(Diff.Tab, score, do.Par = TRUE, nClust)
modifiedTabs(df, do.Par = TRUE, nClust)
set.diffusion(
df,
graph = NULL,
graph.name = "fella",
K = NULL,
scores = c("raw", "ber_s", "z"),
do.Par = TRUE,
nClust = 2
)
``` |

`df` |
Object returned by the |

`input.type` |
Diffusion input type per compound. "binary" 1 if the compound is proposed. "probability" computes the probability of existence of each compound. |

`background` |
Vector containing a list of KEGG identifiers which will be set to 0 in the diffusion process. This will have an effect in the normalization process performed when using the z score. If NULL, the background will be set to all the compounds available in df. |

`Unique.Annotation` |
Logical (only available when input type="binary"). If TRUE, the binary diffusion input is computed by only considering those peaks with a unique annotation (Def: FALSE). |

`do.Par` |
TRUE if parallel computing is required. Def: TRUE |

`nClust` |
Number of clusters that may be used. Def: Number of clusters - 1. |

`Diff.Tab` |
Data frame that results from the diffusion step. |

`score` |
Method of diffusion. Possible values are: "raw", "ber_s" and "z" |

`graph` |
Diffusion graph where nodes correspond to KEGG compounds.
If NULL, the diffusion graph indicated in |

`graph.name` |
Name of the diffusion graphs available in mWISE. The options are "fella", "RClass3levels" or "RClass2levels" (Def: "fella"). |

`K` |
Regularised Laplacian kernel. If NULL, it will be computed
using the |

`scores` |
Method of diffusion. Def: c("raw", "ber_s", "z") |

Function `diffusion.input`

returns a list containing the
diffusion input data frame and a character
specifying the diffusion input type.

Function `diffusion.input.Binary`

returns a data
frame containing the
binary diffusion input.

Function `diffusion.input.probability`

returns a data
frame containing the probability diffusion input.

Function `finalResults`

returns a table containing the final
potential candidates ranked by diffusion score.

Function `modifiedTabs`

returns a table containing the potential
candidates in a specific mWISE stage without repeated peaks.

Function `set.diffusion`

returns a list containing
the diffusion results, the compounds discarded during
the diffusion process, the compounds present in the network
and the background used.

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 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | ```
data("sample.keggDB")
Cpd.Add <- CpdaddPreparation(KeggDB = sample.keggDB, do.Par = FALSE)
data(sample.dataset)
Peak.List <- sample.dataset$Positive$Input
Annotated.List <- matchingStage(Peak.List = Peak.List, Cpd.Add = Cpd.Add,
polarity = "positive", do.Par = FALSE)
Intensity.idx <- seq(27,38)
clustered <- featuresClustering(Peak.List = Peak.List,
Intensity.idx = Intensity.idx,
do.Par = FALSE)
Annotated.Tab <- Annotated.List$Peak.Cpd
Annotated.Tab <- merge(Annotated.Tab,
clustered$Peak.List[,c("Peak.Id", "pcgroup")],
by = "Peak.Id")
MH.Tab <- clusterBased.filter(df = Annotated.Tab,
polarity = "positive")
Input.diffusion <- diffusion.input(df = MH.Tab,
input.type = "probability",
Unique.Annotation = FALSE,
do.Par = FALSE)
data("sample.keggDB")
Cpd.Add <- CpdaddPreparation(KeggDB = sample.keggDB, do.Par = FALSE)
data(sample.dataset)
Peak.List <- sample.dataset$Positive$Input
Annotated.List <- matchingStage(Peak.List = Peak.List, Cpd.Add = Cpd.Add,
polarity = "positive", do.Par = FALSE)
Intensity.idx <- seq(27,38)
clustered <- featuresClustering(Peak.List = Peak.List,
Intensity.idx = Intensity.idx,
do.Par = FALSE)
Annotated.Tab <- Annotated.List$Peak.Cpd
Annotated.Tab <- merge(Annotated.Tab,
clustered$Peak.List[,c("Peak.Id", "pcgroup")],
by = "Peak.Id")
MH.Tab <- clusterBased.filter(df = Annotated.Tab,
polarity = "positive")
Input.diffusion <- diffusion.input(df = MH.Tab,
input.type = "probability",
Unique.Annotation = FALSE,
do.Par = FALSE)
data("sample.graph")
gMetab <- igraph::as.undirected(sample.graph)
diff.Cpd <- set.diffusion(df = Input.diffusion,
scores = "z",
graph = gMetab,
do.Par = FALSE)
``` |

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