DeltaNeTS+

• DeltaNeTS+ is a major improvement to our previous method DeltaNet.
• DeltaNeTS+ is a method for inferring direct gene targets of drug compounds and diseases from steady-state and/or time-series transcriptional profiles.
• DeltaNeTS+ incorporates gene regulatory information (if avaialbe) during the inference.
• DeltaNeTS+ generates a perturbation score for each gene in every sample. The score magnitude reflects the confidence that the transcription process of this gene was directly affected by the external stimuli.
• The score sign indicates the nature of the perturbation: positive for gene induction, negative for gene repression.

Please refer to DeltaNeTS+ manuscript for more detailed information.

DeltaNeTS+ installation

To install DeltaNeTS+ directly from github repository, devtools R package is required.

1. Install and load devtools package in R.
2. Install the package called deltanetsPlus, using devtools::install_github("CABSEL/DeltaNeTSplus/deltanetsPlus"). Your package will e inatalled in R library directory.
3. Load the package, using library(deltanetsPlus), and now you're ready to use!

DeltaNeTS+ example codes using C.elegans expression data

deltanetsPlus package includes example data of lfc, pval, tp,and experiment, which were processed from C .elegans data of Baugh et al. 2005 (GSE2180), as well as grn (tf-gene interactions) of C.elegans. This data set consists of 30 time-series samples of three gene perturbation experiments in C. elegans (10 time points in each experiment), and the perturbed targets were mex-3 for Experiment A, pie-1 for Experiment B, and pie-1 & pal-1 for Experiment C.

• lfc: an nx30 matrix of log base2 Fold Change values of differential gene expressions. Rows are genes (n) and Columna are time-series samples from three different experiments (10 time points per each experiment).
• pval: an nx30 matrix of statistical p-values of the differential gene expressions.
• tp: a 1X30 vector of sample time points
• experiment: a 1x30 vector of group/experiment indication.
• grn: TF-gene interaction list.

1. Generate a deltanetsPlus object.

In this example, we are creating a deltanetsPlus object using createDeltanetsPlusObj(), which will filter out unsignificant gene expressions and compute slopes of gene expression changes, given pval and tp, repectively.

d.obj = createDeltanetsPlusObj(lfc=lfc, pval=pval, tp=tp, experiment=experiment, p.thres=0.05)

For multiple datasets, one can use combin2() to combine deltaentsPlus object (e.g. d.obj=cbind2(d.obj1,d.obj2)). In that case, the number of genes should be the same in two lfc data.

2. Compute gene perturbation scores using deltanetPlus().

In this example, we will compute the perturbations for only a small set of genes.

gset = c("mex-3","pie-1","pal-1",sample(rownames(lfc),10))

Now, gset includes the actual perturbation targets (mex-3, pie-1, and pal-1) as well as 10 random genes, which are not supposed to be perturbed. If gset is not provided, deltanetsPlus() will compute the perturbation scores for whole genes. The example below used parallel computing (par=TRUE) with 2 clusters, but one can also switch off parallel computing by par=FALSE.

dts.res <- deltanetsPlus(d.obj, 
                         grn=grn, 
                         kfolds=10,
                         cv.method="cv",
                         perturbation="group",
                         gset=gset,
                         group=NULL,
                         lambda=10^seq(-2,5,length.out=100),
                         cv.opt = "lambda.1se",
                         par=TRUE, numClusters=2)

Finally, we can check the perturbation scores for the given gene set. In the following example results, mex-3, pie-1, and pal-1 showed the lowest perturbation values for exp. A (1st Column) and exp. B (2nd Column), and exp. C (3rd column), respectively, suggesting that those could be the genes directly repressed during the experiments.

gset2 = intersect(gset, rownames(dts.res$P))
print(dts.res$P[gset2,])

Example results:

12 x 3 sparse Matrix of class "dgCMatrix"

genes	1	2	3
mex-3	-1.776621450	-0.59719729	-0.62503056
pie-1	-1.182894234	-2.05782894	-2.40294302
pal-1	.	-0.02866807	-3.01030870
ife-3	-0.002458885	-0.00233180	-0.00266895
frm-5.1	0.044570707	-0.08326815	.
dnj-17	-0.138966291	-0.30727911	-0.26977825
F29D10.2	-0.187006724	-0.17008810	-0.18143735
pgrn-1	-0.307675932	.	0.10154587
F25E5.3	-0.093054526	.	-0.12077273
Y69A2AR.23	0.058642704	.	.
ZK112.6	0.084273230	.	0.16039465
ncs-2	-0.104642724	.	.

Acknowledgements

This work has been supported by the ETH Zurich Research Grant.