View source: R/proposed_steps.R

consider_previous_information | R Documentation |

This function combines the adjacency matrix A_prev obtained as a result of `first_GBM_step`

with the adjacency matrix A obtained as a result of `second_GBM_step`

. All the edges in the matrix A which have non-zero weights are given machine precision weights initially. We then perform a harmonic mean for each element of A_prev and A to obtain a regularized adjacency matrix (A_final). As a result of this procedure transcriptional regulations which were strong and present in both A_prev and A end up getting highest weights in A_final. We finally remove all edges whose weights are less than machine precision from A_final.

consider_previous_information(A, A_prev,real)

`A` |
Inferred GRN from the |

`A_prev` |
Inferred GRN from the |

`real` |
Numeric value 0 or 1 corresponding to simulated or real experiment respectively. |

Returns an adjacency matrix A_final of the form Ntfs-by-Ntargets

Raghvendra Mall <rmall@hbku.edu.qa>

`first_GBM_step`

, `second_GBM_step`

## The function is currently defined as function (A, A_prev) { #Utilize Past Information also to not remove true positives A_prev[A_prev==0] <- .Machine$double.eps; A_prev <- transform_importance_to_weights(A_prev); A[A==0] <- .Machine$double.eps; epsilon <- 1/log(1/.Machine$double.eps); A <- transform_importance_to_weights(A); A_final <- 2*A*A_prev/(A+A_prev); A_final <- A_final - epislon; A_final[A_final<0] <- 0.0; return(A_final); }

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