+
pandaToyData is a list containing a regulatory structure derived from sequence motif analysis, protein-protein interaction data and a gene expression. The primary function in pandaR is called with
-
+
pandaResult <- panda(pandaToyData$motif, pandaToyData$expression, pandaToyData$ppi)
pandaResult
## PANDA network for1000genes and87transcription factors.
@@ -197,19 +198,19 @@
## Regulatory graph contains 87000 edges.
## Regulatory graph is complete.
Where pandaResult is a ‘panda’ object which contains data.frames describing the complete bipartite gene regulatory network as well as complete networks for gene coregulation and transcription factor cooperation. Due to completeness, edgeweights for the regulatory network are reported for all mxn possible TF-gene edges. The distribution of these edge weights for these networks has approximate mean 0 and standard deviation 1. The edges are therefore best interpreted in a relative sense. Strongly positive values indicative of relatively larger amounts of evidence in favor a regulatory mechanism and conversely, smaller or negative values can be interpreted as lacking evidence of a shared biological role. It is naturally of interest to specify a high edge weight subset of the complete network to investigate as a set of present/absent edges. This is easily performed by using the topedges function. A network containing the top 1000 edge scores as binary edges can be obtained by:
-
+
Users may then examine the genes targeted by a transcription factor of interest.
-
+
## [1] "AKAP10" "CNDP2" "CRHR1" "HNRNPD" "KIAA0652"
## [6] "LOC100093631" "LOC100128811" "PRR15" "TCF4" "TCP11L2"
## [11] "TMPRSS11B" "VCX3B" "WDR4"
The network can be further simplified by focusing only on transcription factors on interest and the genes that they are found to regulate. The subnetwork method serves this function
-
+
topSubnet <- subnetwork(topNet, c("AR","ARID3A","ELK1"))
Existing R packages, such as igraph, can be used to visualize the results
-
+
plotZbyTF - Plot Z by TF outdegree quartiles
We can compare how parameter choices effect the Z-score estimation between two PANDA runs. Additionally, we can compare two phenotypes:
-
+
panda.res1 <- with(pandaToyData, panda(motif, expression, ppi, hamming=1))
panda.res2 <- with(pandaToyData, panda(motif, expression +
rnorm(prod(dim(expression)),sd=5), ppi, hamming=1))
@@ -264,7 +265,7 @@
@@ -451,7 +451,7 @@
-The master branch on github should always be in good shape, so please to pull request to the devel branch. If the contribution is specific to pandaR, please contribute to its seperate GitHub page by pull request.
+The master branch on github should always be in good shape, so please to pull request to the devel branch. If the contribution is specific to pandaR, please contribute to its separate GitHub page by pull request.
@@ -291,7 +291,7 @@ Developers
Tian Wang
Author
John Platig
Author
Marieke Kuijjer
Author
-
Magha Padi
Author
+
Megha Padi
Author
Rebekka Burkholz
Author
Deborah Weighill
Author
@@ -315,7 +315,7 @@
Dev status
@@ -198,7 +198,7 @@ Contents
lioness(motif, expr, ppi = NULL, network.inference.method, ...)
+ lioness(
+ expr,
+ motif = NULL,
+ ppi = NULL,
+ network.inference.method = "panda",
+ ...
+)
Arguments
+
+ | expr |
+ A mandatory expression dataset, as a genes (rows) by samples (columns) data.frame |
+
| motif |
A motif dataset, a data.frame, matrix or exprSet containing 3 columns.
Each row describes an motif associated with a transcription factor (column 1) a
gene (column 2) and a score (column 3) for the motif. |
-
- | expr |
- A mandatory expression dataset, as a genes (rows) by samples (columns) data.frame |
-
| ppi |
A Protein-Protein interaction dataset, a data.frame containing 3 columns.
@@ -208,8 +214,7 @@ R
Examples
data(pandaToyData)
-linonessRes <- lioness(pandaToyData$motif,
- pandaToyData$expression[,1:3],pandaToyData$ppi,hamming=1,progress=FALSE)
+lionessRes <- lioness(expr = pandaToyData$expression[,1:3], motif = pandaToyData$motif, ppi = pandaToyData$ppi,hamming=1,progress=FALSE)
|