| Type: | Package |
| Title: | LS-TreeBoost and LAD-TreeBoost for Gene Regulatory Network Reconstruction |
| Version: | 1.0-11 |
| Date: | 2023-04-13 |
| Author: | Raghvendra Mall [aut, cre], Khalid Kunji [aut], Melissa O'Neill [ctb] |
| Maintainer: | Raghvendra Mall <raghvendra5688@gmail.com> |
| Repository: | CRAN |
| Description: | Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc). |
| License: | GPL (≥ 3) |
| LazyLoad: | yes |
| Depends: | foreach, plyr, doParallel |
| NeedsCompilation: | yes |
| Packaged: | 2023-04-13 05:20:19 UTC; raghvendra |
| Date/Publication: | 2023-04-14 08:50:14 UTC |
Calculate Gene Regulatory Network from Expression data using either LS-TreeBoost or LAD-TreeBoost
Description
This function calculates a Ntfs-by-Ntargets adjacency matrix A from N-by-p expression matrix E. E is expected to be given as input. E is assumed to have p columns corresponding to all the genes, Ntfs represents the number of transcription factors and Ntargets represents the number of target genes and N rows corresponding to different experiments. Additionally, GBM function takes matrix of initial perturbations of genes K of the same size as E, and other parameters including which loss function to use (LS = 1, LAD = 2). As a result, GBM returns a squared matrix A of edge confidences of size Ntfs-by-Ntargets. A subset of known transcription factors can be defined as a subset of all p genes.
Usage
GBM(E = matrix(rnorm(100), 10, 10), K = matrix(0, nrow(E), ncol(E)),
tfs = paste0("G",c(1:10)), targets = paste0("G",c(1:10)),
s_s = 1, s_f = 0.3, lf = 1,
M = 5000,nu = 0.001, scale = TRUE,center = TRUE, optimization.stage = 2)
Arguments
E |
N-by-p expression matrix. Columns correspond to genes, rows correspond to experiments. E is expected to be already normalized using standard methods, for example RMA. Colnames of E is the set of all genes. |
K |
N-by-p initial perturbation matrix. It directly corresponds to E matrix, e.g. if K[i,j] is equal to 1, it means that gene j was knocked-out in experiment i. Single gene knock-out experiments are rows of K with only one value 1. Colnames of K is set to be the set of all genes. By default it's a matrix of zeros of the same size as E, e.g. unknown initial perturbation state of genes. |
tfs |
List of names of transcription factors |
targets |
List of names of target genes |
s_s |
Sampling rate of experiments, 0<s_s<=1. Fraction of rows of E, which will be sampled with replacement to calculate each extension in boosting model. By default it's 1. |
s_f |
Sampling rate of transcription factors, 0<s_f<=1. Fraction of transcription factors from E, as indicated by |
lf |
Loss function: 1 -> Least Squares, 2 -> Least Absolute deviation |
M |
Number of extensions in boosting model, e.g. number of iterations of the main loop of RGBM algorithm. By default it's 5000. |
nu |
Shrinkage factor, learning rate, 0<nu<=1. Each extension to boosting model will be multiplied by the learning rate. By default it's 0.001. |
scale |
Logical flag indicating if each column of E should be scaled to be unit standard deviation. By default it's TRUE. |
center |
Logical flag indicating if each column of E should be scaled to be zero mean. By default it's TRUE. |
optimization.stage |
Numerical flag indicating if re-evaluation of edge confidences should be applied after calculating initial V, optimization.stage={0,1,2}. If optimization.stage=0, no re-evaluation will be applied. If optimization.stage=1, variance-based optimization will be applied. If optimization.stage=2, variance-based and z-score based optimizations will be applied. |
Value
A |
Gene Regulatory Network in form of a Ntfs-by-Ntargets adjacency matrix. |
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
See Also
Examples
# load RGBM library
library("RGBM")
# this step is optional, it helps speed up calculations, run in parallel on 2 processors
library(doParallel)
cl <- makeCluster(2)
# run network inference on a 100-by-100 dummy expression data.
V = GBM()
stopCluster(cl)
Test GBM predictor
Description
This function tests a regression model for a given X.test feature matrix, Y.test response vector, and working parameters.
Usage
GBM.test(model, X.test, Y.test, M.test)
Arguments
model |
Model returned by |
X.test |
Input N-by-p feature matrix of unseen samples. Columns correspond to features, rows correspond to samples. |
Y.test |
Input N-element response vector of unseen samples. |
M.test |
Number of extensions of boosting model to take when predicting response. Must be not greater than |
Value
Result of regression
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
See Also
Train GBM predictor
Description
This function trains a regression model for a given X.train feature matrix, Y.train response vector, and working parameters. A model returned by this function can be used to predict response for unseen data with GBM.test function.
Usage
GBM.train(X.train, Y.train, s_f = 0.3, s_s = 1, lf =1, M.train = 5000, nu = 0.001)
Arguments
X.train |
Input N-by-p feature matrix of training samples. Columns correspond to features, rows correspond to samples. |
Y.train |
Input N-element response vector of training samples. |
s_f |
Sampling rate of features, 0<s_f<=1. Fraction of columns from X.train, which will be sampled without replacement to calculate each extesion in boosting model. By default it's 0.3. |
s_s |
Sampling rate of samples, 0<s_s<=1. Fraction of rows from X.train, which will be sampled with replacement to calculate each extension in boosting model. By default it's 1. |
lf |
Loss function: 1-> Least Squares and 2 -> Least Absolute Deviation |
M.train |
Number of extensions in boosting model, e.g. number of iterations of the main loop of RGBM algorithm. By default it's 5000. |
nu |
Shrinkage factor, learning rate, 0<nu<=1. Each extension to boosting model will be multiplied by the learning rate. By default it's 0.001. |
Value
Regression model is a structure containing all the information needed to predict response for unseen data
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
See Also
Regularized Gradient Boosting Machine for inferring GRN
Description
This function performs the proposed regularized gradient boosting machines for reverse engineering GRN. It allows the user to provide prior information in the form of a mechanistic network g_M and after generation of an initially inferred GRN using the core GBM model undergoes a pruning step. Here we detect and remove isolated nodes using the select_ideal_k function along with identification of the optimal set of transcription factors for each target gene. We then re-iterate through the GBM followed by the refinement step to generate the final re-constructed GRN.
Usage
RGBM(E = matrix(rnorm(100), 10, 10), K = matrix(0, nrow(E), ncol(E)),
g_M = matrix(1, 10, 10), tfs = paste0("G", c(1:10)),
targets = paste0("G", c(1:10)), lf = 1, M = 5000, nu = 0.001, s_f = 0.3,
no_iterations = 2, mink = 0, experimentid = 1, outputpath= "DEFAULT",
sample_type = "Exp1_", real = 0)
Arguments
E |
N-by-p expression matrix. Columns correspond to genes, rows correspond to experiments. E is expected to be already normalized using standard methods, for example RMA. Colnames of E is the set of all p genes and Ntfs represents the number of transcription factors and Ntargets represents the number of target genes. |
K |
N-by-p initial perturbation matrix. It directly corresponds to E matrix, e.g. if K[i,j] is equal to 1, it means that gene j was knocked-out in experiment i. Single gene knock-out experiments are rows of K with only one value 1. Colnames of K is set to be the set of all genes. By default it's a matrix of zeros of the same size as E, e.g. unknown initial perturbation state of genes. |
g_M |
Initial mechanistic network in the form of an adajcency matrix (Ntf-by-Ntargets). Here each column is a binary vector where only those elements are 1 when the corresponding transcription factor has a connection with that target gene. Colnames of g_M should be same as names of targets and Rownames of g_M should be same as names of Tfs. By default it's a matrix of ones of size Ntfs x Ntargets. |
tfs |
List of names of transcription factors |
targets |
List of names of target genes |
lf |
Loss Function: 1 -> Least Squares and 2 -> Least Absolute Deviation |
M |
Number of extensions in boosting model, e.g. number of iterations of the main loop of RGBM algorithm. By default it's 5000. |
nu |
Shrinkage factor, learning rate, 0<nu<=1. Each extension to boosting model will be multiplied by the learning rate. By default it's 0.001. |
s_f |
Sampling rate of transcription factors, 0<s_f<=1. Fraction of transcription factors from E, as indicated by |
no_iterations |
Number of times initial GRN to be constructed and then averaged to generate smooth edge weights for the initial GRN as shown in |
mink |
specified threshold i.e. the minimum number of Tfs to be considered while optimizing the L-curve criterion. By default it's 0. |
experimentid |
The id of the experiment being conducted. It takes natural numbers like 1,2,3 etc. By default it's 1. |
outputpath |
Location where intermediate Adjacency_Matrix and Images folder will be created. By default it's a temp directory (e.g. /tmp/Rtmp...) |
sample_type |
String arguement representing a label for the experiment i.e. in case of DREAM3 challenge sample_type="DREAM3". |
real |
Numeric value 0 or 1 corresponding to simulated or real experiment respectively. |
Value
Returns the final inferred GRN of form Ntfs-by-Ntargets adjacency matrix.
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
See Also
select_ideal_k, first_GBM_step
Examples
# load RGBM library
library("RGBM")
# this step is optional, it helps speed up calculations, run in parallel on 2 processors
library(doParallel)
cl <- makeCluster(2)
# run network inference on a 100-by-100 dummy expression data.
A = RGBM()
stopCluster(cl)
Test rgbm predictor
Description
This function tests a regression model for a given X.test feature matrix, Y.test response vector, and working parameters.
Usage
RGBM.test(model, X.test, Y.test, M.test)
Arguments
model |
Model returned by |
X.test |
Input S-by-P feature matrix of unseen samples. Columns correspond to features, rows correspond to samples. |
Y.test |
Input S-element response vector of unseen samples. |
M.test |
Number of extensions of boosting model to take when predicting response. Must be not greater than |
Value
Result of regression
Author(s)
Raghvendra Mall <raghvendra5688@gmail.com>
Train RGBM predictor
Description
This function trains a regression model for a given X.train feature matrix, Y.train response vector, and working parameters. A model returned by this function can be used to predict response for unseen data with RGBM.test function.
Usage
RGBM.train(X.train, Y.train, s_f = 0.3, s_s = 1, lf = 1, M.train = 5000, nu = 0.001)
Arguments
X.train |
Input S-by-P feature matrix of training samples. Columns correspond to features, rows correspond to samples. |
Y.train |
Input S-element response vector of training samples. |
s_f |
Sampling rate of features, 0<s_f<=1. Fraction of columns from X.train, which will be sampled without replacement to calculate each extesion in boosting model. By default it's 0.3. |
s_s |
Sampling rate of samples, 0<s_s<=1. Fraction of rows from X.train, which will be sampled with replacement to calculate each extension in boosting model. By default it's 1. |
lf |
Loss function: 1-> Least Squares and 2 -> Least Absolute Deviation |
M.train |
Number of extensions in boosting model, e.g. number of iterations of the main loop of RGBM algorithm. By default it's 5000. |
nu |
Shrinkage factor, learning rate, 0<nu<=1. Each extension to boosting model will be multiplied by the learning rate. By default it's 0.001. |
Value
Regression model is a structure containing all the information needed to predict response for unseen data
Author(s)
Raghvendra Mall <raghvendra5688@gmail.com>
Add row and column names to the adjacency matrix A
Description
Here we add the names of the transcription factors (Tfs) as rownames and names of the target genes as column names to the adjacency matrix A.
Usage
add_names(A, tfs, targets)
Arguments
A |
Adjacency matrix A obtained as a result of GBM procedure. |
tfs |
List of names of transcription factors. |
targets |
List of names of target genes. |
Details
In case of DREAM Challenge datasets list of transcription factors is same as list of target genes and are referred as G1, ..., G100.
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
Apply row-wise deviation on the inferred GRN
Description
This function performs a row-wise standard deviation of network A to generate an S1 matrix which is then used to modify the weights in network A
Usage
apply_row_deviation(A,Ntfs,Ntargets)
Arguments
A |
Inferred GRN in the form of Ntfs-by-Ntargets matrix |
Ntfs |
Total number of transcription factors used in the experiment. |
Ntargets |
Total number of target genes used in the experiment |
Value
Refined adjacency matrix A in the form of Ntfs-by-Ntargets matrix
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
Remember the intermediate inferred GRN while generating the final inferred GRN
Description
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.
Usage
consider_previous_information(A, A_prev,real)
Arguments
A |
Inferred GRN from the |
A_prev |
Inferred GRN from the |
real |
Numeric value 0 or 1 corresponding to simulated or real experiment respectively. |
Value
Returns an adjacency matrix A_final of the form Ntfs-by-Ntargets
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
See Also
first_GBM_step, second_GBM_step
Examples
## 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);
}
Perform either LS-Boost or LAD-Boost (GBM) on expression matrix E followed by the null_model_refinement_step
Description
This function utilizes the core gradient boosting machine model (GBM) followed by the refinement step to generate the first adjacency matrix A of size p x p using the list of Tfs and the set of target genes. Several such adjacency matrices (A) are obtained based on the number of iterations to be performed. All these adjacency matrices are averaged to reduce the noise in the inferred intermediate GRN.
Usage
first_GBM_step(E, K, tfs, targets, Ntfs, Ntargets, lf, M, nu,s_f, no_iterations)
Arguments
E |
N-by-p expression matrix. Columns correspond to genes, rows correspond to experiments. E is expected to be already normalized using standard methods, for example RMA. Colnames of E is the set of all genes. |
K |
N-by-p initial perturbation matrix. It directly corresponds to E matrix, e.g. if K[i,j] is equal to 1, it means that gene j was knocked-out in experiment i. Single gene knock-out experiments are rows of K with only one value 1. Colnames of K is set to be the set of all genes. By default it's a matrix of zeros of the same size as E, e.g. unknown initial perturbation state of genes. |
tfs |
List of names of transcription factors. In case of presence of prior mechanistic network it is a subset of all the p genes whereas in absence of such a mechanistic network it is a list of names of all the p genes. |
targets |
List of names of target genes. In case of presence of prior mechanistic network it is a subset of all the p genes whereas in absence of such a mechanistic network it is a list of names of all the p genes. |
Ntfs |
Total number of transcription factors used in the experiment. |
Ntargets |
Total number of target genes used in the experiment. |
lf |
Loss Function: 1 -> Least Squares and 2 -> Least Absolute Deviation |
M |
Number of extensions in boosting model, e.g. number of iterations of the main loop of RGBM algorithm. By default it's 5000. |
nu |
Shrinkage factor, learning rate, 0<nu<=1. Each extension to boosting model will be multiplied by the learning rate. By default it's 0.001. |
s_f |
Sampling rate of transcription factors, 0<s_f<=1. Fraction of transcription factors from E, as indicated by |
no_iterations |
Number of iterations to perform equivalent to building that many core LS-Boost/LAD-Boost models and then averaging them to have smooth edge-weights in the inferred intermediate GRN. |
Value
Intermediate Gene Regulatory Network in form of a Ntfs-by-Ntargets adjacency matrix.
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
See Also
Get the indices of recitifed list of Tfs for individual target gene
Description
This function is used to identify the recitified list of transcription factors for individual target genes after analysing the variable importance scores (where non-essential Tfs are pruned). These list of Tfs are usually different for individual target genes. Hence we maintain this in the form an adjacency matrix where the rownames correspond to all the Tfs and colnames correspond to all the target genes. Each column is a binary vector where all the values corresponding to the rectified Tfs active for that target are 1 while rest of the values are zeros.
Usage
get_colids(A, ideal_k, tfs, targets, Ntfs, Ntargets)
Arguments
A |
Adjacency Matrix A obtained after the GBM and refinement step. |
ideal_k |
A vector containing the optimal value of k (no of active TFs) for each target gene obtained from |
tfs |
List of names of transcription factors. |
targets |
List of names of target genes. |
Ntfs |
Total number of transcription factors used in the experiment. |
Ntargets |
Total number of target genes used in the experiment. |
Value
The function returns an adjacency matrix where the rownames correspond to all the Tfs and colnames correspond to all the target genes. Each column is a binary vector where all the values corresponding to the rectified Tfs active for that target are 1 while rest of the values are zeros.
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
See Also
Generate filepaths to maintain adjacency matrices and images
Description
This function generates a set of filepaths which are used to keep the adjacency matrix A obtained after the first_GBM_step + null_model_refinement_step. It also generates a path where an image of the variable importance curves for several target genes can be kept.
Usage
get_filepaths(A_prev, experimentid, outputpath, sample_type)
Arguments
A_prev |
Adjacency matrix A obtained after |
experimentid |
The id of the experiment being conducted. It takes natural numbers like 1,2,3 etc. By default it's 1. |
outputpath |
Location where the Adjacency_Matrix and Images folder will be created. |
sample_type |
String arguement representing a label for the experiment i.e. in case of DREAM3 challenge sample_type="DREAM3". |
Value
Returns a data frame where the first element in the data frame is the location where the Adjacency_Matrix folder is located in the filesystem, second element represents the location where the Images folder is located in the filesystem, third element represents the path to the file where the Adjacency_Matrix will be written.
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
Get indices of experiments where knockout or knockdown happened
Description
This function provides the indices of all those samples (out of N) where it is known apriori that a gene was either knocked-out or was knocked-down. This information is useful for the null_model_refinement_step which utilizes the z_score_effect technique (with the help of this information).
Usage
get_ko_experiments(K)
Arguments
K |
N-by-p initial perturbation matrix. It directly corresponds to E matrix, e.g. if K[i,j] is equal to 1, it means that gene j was knocked-out in experiment i. Single gene knock-out experiments are rows of K with only one value 1. Colnames of K is set to be the set of all genes. By default it's a matrix of zeros of the same size as E, e.g. unknown initial perturbation state of genes. |
Value
Return a vector containing the indices of all the samples where a gene was knocked-out/down.
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
See Also
null_model_refinement_step, z_score_effect
Get the indices of all the TFs from the data
Description
This function provides the indices of all the transcription factors which are present in the expression matrix. In case of DREAM Challenges it will return the indices as 1,...,p for all the p genes in the data as the transcription factors are not known beforehand.
Usage
get_tf_indices(E, tfs, Ntfs)
Arguments
E |
E is the expression matrix of size N x p where N is number of examples and p is the number of genes. Here the column names of expression matrix is the list of all the genes present in the E matrix. Colnames of E is the set of all genes. |
tfs |
List of names of transcription factors. |
Ntfs |
Total number of transcription factors used in the experiment. |
Value
Returns the indices of all the transcription factors present in E matrix.
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
See Also
Column normalize the obtained adjacency matrix
Description
We perform a column normalization on an adjacency matrix A equivalent to inferred GRN
Usage
normalize_matrix_colwise(A,Ntargets)
Arguments
A |
Inferred GRN in the form of Ntfs-by-Ntargets matrix |
Ntargets |
Total number of target genes used in the experiment |
Value
Column Normalized GRN of size Ntfs-by-Ntargets
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
Perform the null model refinement step
Description
We used this function for refining the edge-weights in an inferred GRN (A) by utilizing matrix (S2) obtained from null-mutant zscore effect (z_score_effect) as shown in Slawek J, Arodz T i.e. A = A x S2.
Usage
null_model_refinement_step(E, A, K,tfs, targets, Ntfs, Ntargets)
Arguments
E |
N-by-p expression matrix. Columns correspond to genes, rows correspond to experiments. E is expected to be already normalized using standard methods, for example RMA. Colnames of E is the set of all genes. |
A |
Intermediate GRN network in the form of a p-by-p adjacency matrix. |
K |
N-by-p initial perturbation matrix. It directly corresponds to E matrix, e.g. if K[i,j] is equal to 1, it means that gene j was knocked-out in experiment i. Single gene knock-out experiments are rows of K with only one value 1. Colnames of K is set to be the set of all genes. By default it's a matrix of zeros of the same size as E, e.g. unknown initial perturbation state of genes. |
tfs |
List of names of transcription factors |
targets |
List of names of target genes |
Ntfs |
Number of transcription factors used while building the GBM ( |
Ntargets |
Number of targets used while building the GBM ( |
Value
Returns a refined adjacency matrix A in the form of a Ntfs-by-Ntargets matrix.
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
References
Slawek J, Arodz T. ENNET: inferring large gene regulatory networks from expression data using gradient boosting. BMC systems biology. 2013 Oct 22;7(1):1.
See Also
Perform the regularized GBM modelling once the initial GRN is inferred
Description
This function undertakes all the proposed steps for regularizing the list of transcription factors for individual target gene followed by re-iterating through the core GBM model and the refinement step to produce the final reverse engineered GRN.
Usage
regularized_GBM_step(E, A_prev, K, tfs, targets, Ntfs, Ntargets, lf, M, nu, s_f,
experimentid, outputpath, sample_type, mink=0,real=0)
Arguments
E |
N-by-p expression matrix. Columns correspond to genes, rows correspond to experiments. E is expected to be already normalized using standard methods, for example RMA. Colnames of E is the set of all genes. |
A_prev |
An intermediate inferred GRN obtained from |
K |
N-by-p initial perturbation matrix. It directly corresponds to E matrix, e.g. if K[i,j] is equal to 1, it means that gene j was knocked-out in experiment i. Single gene knock-out experiments are rows of K with only one value 1. Colnames of K is set to be the set of all genes. By default it's a matrix of zeros of the same size as E, e.g. unknown initial perturbation state of genes. |
tfs |
List of names of transcription factors. |
targets |
List of names of target genes. |
Ntfs |
Total number of transcription factors used in the experiment. |
Ntargets |
Total number of target genes used in the experiment |
lf |
Loss Function: 1 -> Least Squares and 2 -> Least Absolute Deviation |
M |
Number of extensions in boosting model, e.g. number of iterations of the main loop of RGBM algorithm. By default it's 5000. |
nu |
Shrinkage factor, learning rate, 0<nu<=1. Each extension to boosting model will be multiplied by the learning rate. By default it's 0.001. |
s_f |
Sampling rate of transcription factors, 0<s_f<=1. Fraction of transcription factors from E, as indicated by |
experimentid |
The id of the experiment being conducted. It takes natural numbers like 1,2,3 etc. By default it's 1. |
outputpath |
Location where the Adjacency_Matrix and Images folder will be created. |
sample_type |
String arguement representing a label for the experiment i.e. in case of DREAM3 challenge sample_type="DREAM3". |
mink |
User specified threshold i.e. the minimum number of Tfs to be considered while optimizing the L-curve criterion. By default it's 0. |
real |
Numeric value 0 or 1 corresponding to simulated or real experiment respectively. |
Value
Returns the final inferred GRN in form of Ntfs-by-Ntargets matrix
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
See Also
Regulate the size of the regulon for each TF
Description
We control the size of the regulon for each TF by using a heuristic to remove the edges whose weights are small
Usage
regulate_regulon_size(A)
Arguments
A |
Inferred GRN in the form of Ntfs-by-Ntargets matrix |
Value
Refined adjacency matrix A in the form of Ntfs-by-Ntargets matrix
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
Re-iterate through the core GBM model building with optimal set of Tfs for each target gene
Description
This function re-performs the core GBM model building (only one time) using the optimal set of transcription factors obtained from select_ideal_k followed by get_colids for individual target gene to return a regularized GRN.
Usage
second_GBM_step(E, K, df_colids, tfs, targets, Ntfs, Ntargets, lf, M, nu, s_f)
Arguments
E |
N-by-p expression matrix. Columns correspond to genes, rows correspond to experiments. E is expected to be already normalized using standard methods, for example RMA. Colnames of E is the set of all genes. |
K |
N-by-p initial perturbation matrix. It directly corresponds to E matrix, e.g. if K[i,j] is equal to 1, it means that gene j was knocked-out in experiment i. Single gene knock-out experiments are rows of K with only one value 1. Colnames of K is set to be the set of all genes. By default it's a matrix of zeros of the same size as E, e.g. unknown initial perturbation state of genes. |
df_colids |
A matrix made up of column vectors where each column vector represents the optimal set of active Tfs which regulate each target gene and obtained from |
tfs |
List of names of transcription factors. |
targets |
List of names of target genes. |
Ntfs |
Total number of transcription factors used in the experiment. |
Ntargets |
Total number of target genes used in the experiment |
lf |
Loss Function: 1 -> Least Squares and 2 -> Least Absolute Deviation |
M |
Number of extensions in boosting model, e.g. number of iterations of the main loop of RGBM algorithm. By default it's 5000. |
nu |
Shrinkage factor, learning rate, 0<nu<=1. Each extension to boosting model will be multiplied by the learning rate. By default it's 0.001. |
s_f |
Sampling rate of transcription factors, 0<s_f<=1. Fraction of transcription factors from E, as indicated by |
Value
Returns a regularized GRN of the form Ntfs-by-Ntargets
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
See Also
Identifies the optimal value of k i.e. top k Tfs for each target gene
Description
This function detects the optimal number of transcription factors which are regulating each target gene. This number is different for different target genes. It utilizes a heuristic to also detect the isolated targets which are not regulated by any transcription factor. To the detect the optimal number of Tfs for each target gene, it uses a notion similar to that used for optimization of the L-curve criterion for Tikonov regularization by evaluating the variable importance curve for each target gene.
Usage
select_ideal_k(experimentid, mink, filepath, imagepath, adjacency_matrix_path)
Arguments
experimentid |
The id of the experiment being conducted. It takes natural numbers like 1,2,3 etc. By default it's 1. |
mink |
User specified threshold i.e. the minimum number of Tfs to be considered while optimizing the L-curve criterion. By default it's 0. |
filepath |
Path where some intermediate files will be written and provided by the function |
imagepath |
Path where an image of the variable importance curves for first 16 target genes will be written and provided by the function |
adjacency_matrix_path |
Path where an intermediate adjacency matrix will be written and provided by the function |
Value
Returns a vector where each element represents the optimal number of transcription factors for each target gene.
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
Test the regression model
Description
Test the regression model for each target gene
Format
The format is: List of 4 $ name : chr "test_regression_stump_R" $ address :Class 'RegisteredNativeSymbol' <externalptr> $ dll :List of 5 ..$ name : chr "RGBM" ..$ path : chr "/home/raghvendra/R/x86_64-pc-linux-gnu-library/3.3/RGBM/libs/RGBM.so" ..$ dynamicLookup: logi TRUE ..$ handle :Class 'DLLHandle' <externalptr> ..$ info :Class 'DLLInfoReference' <externalptr> ..- attr(*, "class")= chr "DLLInfo" $ numParameters: int 15 - attr(*, "class")= chr [1:2] "CRoutine" "NativeSymbolInfo"
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
Train the regression stump
Description
Train the regression stump for each target gene
Format
The format is: List of 4 $ name : chr "train_regression_stump_R" $ address :Class 'RegisteredNativeSymbol' <externalptr> $ dll :List of 5 ..$ name : chr "RGBM" ..$ path : chr "/home/raghvendra/R/x86_64-pc-linux-gnu-library/3.3/RGBM/libs/RGBM.so" ..$ dynamicLookup: logi TRUE ..$ handle :Class 'DLLHandle' <externalptr> ..$ info :Class 'DLLInfoReference' <externalptr> ..- attr(*, "class")= chr "DLLInfo" $ numParameters: int 15 - attr(*, "class")= chr [1:2] "CRoutine" "NativeSymbolInfo"
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
Log transforms the edge-weights in the inferred GRN
Description
This function performs an inverse absolute log-transformation of the non-zero edge weights in the final inferred GRN (A) to make the edge-weights more comprehensible and understandable.
Usage
transform_importance_to_weights(A)
Arguments
A |
Inferred GRN in the form of Ntfs-by-Ntargets matrix |
Value
Refined adjacency matrix A in the form of Ntfs-by-Ntargets matrix
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
Convert adjacency matrix to a list of edges
Description
This function converts adjacency matrix A to a sorted list of edges, e.g. a list in which edges are sorted by decreasing confidence.
Usage
v2l(A, max = 1e+05, check.names = TRUE)
Arguments
A |
Input adjacency matrix. |
max |
Maximal length of the resulting list. This number may be lower than the number of all the edges from adjacency matrix. Then only top |
check.names |
Checks name of the gene ids |
Value
A data frame of sorted edges: (1) list of sources (2) list of destinations (3) list of confidences. Elements in all the lists correspond to each other.
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
Generates a matrix S2 of size Ntfs x Ntargets using the null-mutant zscore algorithm Prill, Robert J., et al
Description
This function generates a matrix of the form Ntfs-by-Ntargets using the steps proposed in null-mutant zscore method and acts as a refinement step for the inferred GRN where this matrix is multiplied element by element with the inferred adjacency matrix A. However, this step is only effective in presence of additional source of information like knockout, knockdown or which genes are intially perturbed in time-series expression data.
Usage
z_score_effect(E, K, tfs, targets, Ntfs, Ntargets)
Arguments
E |
N-by-p expression matrix. Columns correspond to genes, rows correspond to experiments. E is expected to be already normalized using standard methods, for example RMA. Colnames of E is the set of all genes. |
K |
N-by-p initial perturbation matrix. It directly corresponds to E matrix, e.g. if K[i,j] is equal to 1, it means that gene j was knocked-out in experiment i. Single gene knock-out experiments are rows of K with only one value 1. Colnames of K is set to be the set of all genes. By default it's a matrix of zeros of the same size as E, e.g. unknown initial perturbation state of genes. |
tfs |
List of names of transcription factors |
targets |
List of names of target genes |
Ntfs |
Total number of transcription factors used in the experiment. |
Ntargets |
Total number of target genes used in the experiment. |
Value
Returns an S2 matrix of form Ntfs-by-Ntargets. In absence of any additional knockout/knockdown/perturbation information the S2 matrix is a matrix of ones.
Author(s)
Raghvendra Mall <rmall@hbku.edu.qa>
References
Prill, Robert J., et al. "Towards a rigorous assessment of systems biology models: the DREAM3 challenges." PloS one 5.2 (2010): e9202.