diff --git a/_pkgdown.yml b/_pkgdown.yml index efc4ce33..391a10b7 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -116,6 +116,37 @@ reference: contents: - runEgret +- title: BLOBFISH + desc: BLOBFISH functions + contents: + - RunBLOBFISH + - GenerateNullPANDADistribution + - BuildSubnetwork + - CalculatePValues + - FindSignificantEdgesForHop + - SignificantBreadthFirstSearch + - FindConnectionsForAllHopCounts + - PlotNetwork + +- title: YARN + desc: YARN functions + contents: + - checkMisAnnotation + - checkTissuesToMerge + - filterGenes + - filterLowGenes + - normalizeTissueAware + - annotateFromBiomart + - downloadGTEx + - extractMatrix + - filterMissingGenes + - filterSamples + - plotCMDS + - plotDensity + - plotHeatmap + - qsmooth + - qstats + - title: Plotting functions desc: function to plot the networks and community structures. contents: @@ -138,3 +169,5 @@ reference: - mut.ucec - exon.size - genes + - Bladder_data + - Skin_data diff --git a/docs/404.html b/docs/404.html index 2357cfd3..caf3eac4 100644 --- a/docs/404.html +++ b/docs/404.html @@ -7,8 +7,8 @@ Page not found (404) • netZooR - - + + NA • netZooRNA • netZooR @@ -10,7 +10,7 @@ netZooR - 1.5.0 + 1.5.17 + + + + + +
+
+
+

Bladder RNA-seq data from the GTEx consortium

+ Source: R/YARN.R +
Bladder_data.Rd
+
+ +
+

Bladder RNA-seq data from the GTEx consortium. V6 release.

+
+ +
+

Usage

+ 
data(bladder)
+
+ +
+

Format

+

An object of class "ExpressionSet"; see ExpressionSet.

+
+
+

Source

+

GTEx Portal

+
+
+

Value

+ + +

ExpressionSet object

+
+
+

References

+

GTEx Consortium, 2015. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science, 348(6235), pp.648-660. +(PubMed)

+
+ +
+

Examples

+ 
data(bladder);
+checkMissAnnotation(bladder);
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/BuildSubnetwork.html b/docs/reference/BuildSubnetwork.html new file mode 100644 index 00000000..fcd43129 --- /dev/null +++ b/docs/reference/BuildSubnetwork.html @@ -0,0 +1,163 @@ + +Find the subnetwork of significant edges connecting the genes. — BuildSubnetwork • netZooR + Skip to contents + + +
+
+
+

Find the subnetwork of significant edges connecting the genes.

+ Source: R/BLOBFISH.R +
BuildSubnetwork.Rd
+
+ +
+

Find the subnetwork of significant edges connecting the genes.

+
+ +
+

Usage

+ 
BuildSubnetwork(
+  geneSet,
+  networks,
+  alpha,
+  hopConstraint,
+  nullDistribution,
+  verbose = FALSE,
+  topX = NULL,
+  doFDRAdjustment = TRUE,
+  pValueChunks = 100,
+  loadPValues = FALSE,
+  pValueFile = "pvalues.RDS"
+)
+
+ +
+

Arguments

+
geneSet
+

A character vector of genes comprising the targets of interest.

+ + +
networks
+

A list of bipartite (PANDA-like) networks, where each network is a data frame with the following format: +tf,gene,score

+ + +
alpha
+

The significance cutoff for the statistical test.

+ + +
hopConstraint
+

The maximum number of hops to be considered between gene pairs. +Must be an even number.

+ + +
nullDistribution
+

The null distribution, specified as a vector of values.

+ + +
verbose
+

Whether or not to print detailed information about the run.

+ + +
topX
+

Select the X lowest significant p-values for each gene. NULL by default.

+ + +
doFDRAdjustment
+

Whether or not to perform FDR adjustment.

+ + +
pValueChunks
+

The number of chunks to split when calculating the p-value. This +parameter allows the edges to be split into chunks to prevent memory errors.

+ + +
loadPValues
+

Whether p-values should be loaded from pValueFile or re-generated. +Default is FALSE.

+ + +
pValueFile
+

The file where the p-values should be saved. If NULL, they are not +saved and need to be recalculated.

+ +
+
+

Value

+ + +

A bipartite subnetwork in the same format as the original networks.

+
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/CalculatePValues.html b/docs/reference/CalculatePValues.html new file mode 100644 index 00000000..40f6b09e --- /dev/null +++ b/docs/reference/CalculatePValues.html @@ -0,0 +1,143 @@ + +Calculate p-values for all edges in the network using a Wilcoxon two-sample test +for each edge. — CalculatePValues • netZooR + Skip to contents + + +
+
+
+

Calculate p-values for all edges in the network using a Wilcoxon two-sample test +for each edge.

+ Source: R/BLOBFISH.R +
CalculatePValues.Rd
+
+ +
+

Calculate p-values for all edges in the network using a Wilcoxon two-sample test +for each edge.

+
+ +
+

Usage

+ 
CalculatePValues(
+  network,
+  nullDistribution,
+  pValueChunks = 100,
+  doFDRAdjustment = TRUE,
+  pValueFile = "pvalues.RDS",
+  verbose = FALSE
+)
+
+ +
+

Arguments

+
network
+

A combination of PANDA-like networks, with the following format +(e.g., 3 networks), provided as a data frame: +tf,gene,score1,score2,score3

+ + +
nullDistribution
+

The null distribution, specified as a vector of values.

+ + +
pValueChunks
+

The number of chunks to split when calculating the p-value. This +parameter allows the edges to be split into chunks to prevent memory errors.

+ + +
doFDRAdjustment
+

Whether or not to perform FDR adjustment.

+ + +
pValueFile
+

The file where the p-values should be saved. If NULL, they are not +saved and need to be recalculated.

+ + +
verbose
+

Whether or not to print detailed information about the run.

+ +
+
+

Value

+ + +

A vector of p-values, one for each edge.

+
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/FindConnectionsForAllHopCounts.html b/docs/reference/FindConnectionsForAllHopCounts.html new file mode 100644 index 00000000..7b0e2b01 --- /dev/null +++ b/docs/reference/FindConnectionsForAllHopCounts.html @@ -0,0 +1,119 @@ + +For all hop counts up to the maximum, find subnetworks connecting each pair of +genes by exactly that number of hops. For instance, find each — FindConnectionsForAllHopCounts • netZooR + Skip to contents + + +
+
+
+

For all hop counts up to the maximum, find subnetworks connecting each pair of +genes by exactly that number of hops. For instance, find each

+ Source: R/BLOBFISH.R +
FindConnectionsForAllHopCounts.Rd
+
+ +
+

For all hop counts up to the maximum, find subnetworks connecting each pair of +genes by exactly that number of hops. For instance, find each

+
+ +
+

Usage

+ 
FindConnectionsForAllHopCounts(subnetworks, verbose = FALSE)
+
+ +
+

Arguments

+
subnetworks
+

A list of bipartite (PANDA-like) subnetworks for each gene, +containing only the significant edges meeting the hop count criteria and +where each network is a data frame with the following format: +tf,gene

+ + +
verbose
+

Whether or not to print detailed information about the run.

+ +
+
+

Value

+ + +

A bipartite subnetwork in the same format as the original networks.

+
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/FindSignificantEdgesForHop.html b/docs/reference/FindSignificantEdgesForHop.html new file mode 100644 index 00000000..6e5857e3 --- /dev/null +++ b/docs/reference/FindSignificantEdgesForHop.html @@ -0,0 +1,134 @@ + +Find the subnetwork of significant edges n / 2 hops away from each gene. — FindSignificantEdgesForHop • netZooR + Skip to contents + + +
+
+
+

Find the subnetwork of significant edges n / 2 hops away from each gene.

+ Source: R/BLOBFISH.R +
FindSignificantEdgesForHop.Rd
+
+ +
+

Find the subnetwork of significant edges n / 2 hops away from each gene.

+
+ +
+

Usage

+ 
FindSignificantEdgesForHop(
+  geneSet,
+  combinedNetwork,
+  hopConstraint,
+  pValues,
+  verbose = FALSE,
+  topX = NULL
+)
+
+ +
+

Arguments

+
geneSet
+

A character vector of genes comprising the targets of interest.

+ + +
combinedNetwork
+

A concatenation of n PANDA-like networks with the following format: +tf,gene,score_net1, score_net2, ... , score_netn

+ + +
hopConstraint
+

The maximum number of hops to be considered for a gene.

+ + +
pValues
+

The p-values for all edges.

+ + +
verbose
+

Whether or not to print detailed information about the run.

+ + +
topX
+

Select the X lowest significant p-values for each gene. NULL by default.

+ +
+
+

Value

+ + +

A bipartite subnetwork in the same format as the original networks.

+
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/GenerateNullPANDADistribution.html b/docs/reference/GenerateNullPANDADistribution.html new file mode 100644 index 00000000..c215bd38 --- /dev/null +++ b/docs/reference/GenerateNullPANDADistribution.html @@ -0,0 +1,152 @@ + +Generate a null distribution of edge scores for PANDA-like networks; that is, +the set of edges where (1) the TF does not have a binding motif in the gene region, +(2) the TF does not form a complex with any other TF that has a binding motif in +the gene region, and (3) the genes regulated by the TF are not coexpressed with the +gene in question. We obtain this by inputting an empty prior and an identity coexpression +matrix. — GenerateNullPANDADistribution • netZooR + Skip to contents + + +
+
+
+

Generate a null distribution of edge scores for PANDA-like networks; that is, +the set of edges where (1) the TF does not have a binding motif in the gene region, +(2) the TF does not form a complex with any other TF that has a binding motif in +the gene region, and (3) the genes regulated by the TF are not coexpressed with the +gene in question. We obtain this by inputting an empty prior and an identity coexpression +matrix.

+ Source: R/BLOBFISH.R +
GenerateNullPANDADistribution.Rd
+
+ +
+

Generate a null distribution of edge scores for PANDA-like networks; that is, +the set of edges where (1) the TF does not have a binding motif in the gene region, +(2) the TF does not form a complex with any other TF that has a binding motif in +the gene region, and (3) the genes regulated by the TF are not coexpressed with the +gene in question. We obtain this by inputting an empty prior and an identity coexpression +matrix.

+
+ +
+

Usage

+ 
GenerateNullPANDADistribution(
+  ppiFile,
+  motifFile,
+  sampSize = 20,
+  numberOfPandas = 10
+)
+
+ +
+

Arguments

+
ppiFile
+

The location of the protein-protein interaction network between transcription factors. +This should be a TSV file where the first two columns are the transcription +factors and the third is whether there is a PPI between them.

+ + +
motifFile
+

The location of the motif prior from genes to transcription factors. This should +be a TSV file where the first column is the transcription factors, the +second is the genes, and the third is whether the transcription factor's +binding motif is in the gene promoter region.

+ + +
sampSize
+

Number of samples to simulate

+ + +
numberOfPandas
+

Number of null PANDA networks to generate

+ +
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/PlotNetwork.html b/docs/reference/PlotNetwork.html new file mode 100644 index 00000000..b5eaa70f --- /dev/null +++ b/docs/reference/PlotNetwork.html @@ -0,0 +1,164 @@ + +Plot the networks, using different colors for transcription factors, genes of interest, +and additional genes. — PlotNetwork • netZooR + Skip to contents + + +
+
+
+

Plot the networks, using different colors for transcription factors, genes of interest, +and additional genes.

+ Source: R/BLOBFISH.R +
PlotNetwork.Rd
+
+ +
+

Plot the networks, using different colors for transcription factors, genes of interest, +and additional genes.

+
+ +
+

Usage

+ 
PlotNetwork(
+  network,
+  genesOfInterest,
+  tfColor = "blue",
+  nodeSize = 1,
+  edgeWidth = 0.5,
+  vertexLabels = NA,
+  vertexLabelSize = 0.7,
+  vertexLabelOffset = 0.5,
+  layoutBipartite = TRUE,
+  geneColorMapping = NULL
+)
+
+ +
+

Arguments

+
network
+

A data frame with the following format: +tf,gene

+ + +
genesOfInterest
+

Which genes of interest to highlight

+ + +
tfColor
+

Color for the transcription factors

+ + +
nodeSize
+

Size of node

+ + +
edgeWidth
+

Width of edges

+ + +
vertexLabels
+

Which vertex labels to include. By default, none are included.

+ + +
vertexLabelSize
+

The size of label to use for the vertex, as a fraction of the default.

+ + +
vertexLabelOffset
+

Number of pixels in the offset when plotting labels. +Default is TRUE.

+ + +
layoutBipartite
+

Whether or not to layout as a bipartite graph.

+ + +
geneColorMapping
+

Color mapping from a set of genes to a color. The +nodes and edges connected to them will be this color. If NULL, all genes and +their edges will be gray. The format is a data frame, where the first column ("gene") +is the name of the gene and the second ("color") is the color.

+ +
+
+

Value

+ + +

A bipartite plot of the network

+
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/RunBLOBFISH.html b/docs/reference/RunBLOBFISH.html new file mode 100644 index 00000000..487eb857 --- /dev/null +++ b/docs/reference/RunBLOBFISH.html @@ -0,0 +1,175 @@ + +Given a set of genes of interest, full bipartite networks with scores (one network for each sample), a significance +cutoff for statistical testing, and a hop constraint, BLOBFISH finds a subnetwork of +significant edges connecting the genes. — RunBLOBFISH • netZooR + Skip to contents + + +
+
+
+

Given a set of genes of interest, full bipartite networks with scores (one network for each sample), a significance +cutoff for statistical testing, and a hop constraint, BLOBFISH finds a subnetwork of +significant edges connecting the genes.

+ Source: R/BLOBFISH.R +
RunBLOBFISH.Rd
+
+ +
+

Given a set of genes of interest, full bipartite networks with scores (one network for each sample), a significance +cutoff for statistical testing, and a hop constraint, BLOBFISH finds a subnetwork of +significant edges connecting the genes.

+
+ +
+

Usage

+ 
RunBLOBFISH(
+  geneSet,
+  networks,
+  alpha,
+  hopConstraint,
+  nullDistribution,
+  verbose = FALSE,
+  topX = NULL,
+  doFDRAdjustment = TRUE,
+  pValueChunks = 100,
+  loadPValues = FALSE,
+  pValueFile = "pvalues.RDS"
+)
+
+ +
+

Arguments

+
geneSet
+

A character vector of genes comprising the targets of interest.

+ + +
networks
+

A list of bipartite (PANDA-like) networks, where each network is a data frame with the following format: +tf,gene,score

+ + +
alpha
+

The significance cutoff for the statistical test.

+ + +
hopConstraint
+

The maximum number of hops to be considered between gene pairs. +Must be an even number.

+ + +
nullDistribution
+

The null distribution, specified as a vector of values.

+ + +
verbose
+

Whether or not to print detailed information about the run.

+ + +
topX
+

Select the X lowest significant p-values for each gene. NULL by default.

+ + +
doFDRAdjustment
+

Whether or not to perform FDR adjustment.

+ + +
pValueChunks
+

The number of chunks to split when calculating the p-value. This +parameter allows the edges to be split into chunks to prevent memory errors.

+ + +
loadPValues
+

Whether p-values should be loaded from pValueFile or re-generated. +Default is FALSE.

+ + +
pValueFile
+

The file where the p-values should be saved. If NULL, they are not +saved and need to be recalculated.

+ +
+
+

Value

+ + +

A bipartite subnetwork in the same format as the original networks.

+
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/SignificantBreadthFirstSearch.html b/docs/reference/SignificantBreadthFirstSearch.html new file mode 100644 index 00000000..3cc41b03 --- /dev/null +++ b/docs/reference/SignificantBreadthFirstSearch.html @@ -0,0 +1,146 @@ + +Find all significant edges adjacent to the starting nodes, excluding the nodes +specified. — SignificantBreadthFirstSearch • netZooR + Skip to contents + + +
+
+
+

Find all significant edges adjacent to the starting nodes, excluding the nodes +specified.

+ Source: R/BLOBFISH.R +
SignificantBreadthFirstSearch.Rd
+
+ +
+

Find all significant edges adjacent to the starting nodes, excluding the nodes +specified.

+
+ +
+

Usage

+ 
SignificantBreadthFirstSearch(
+  networks,
+  pValues,
+  startingNodes,
+  nodesToExclude,
+  startFromTF,
+  verbose = FALSE,
+  topX = NULL
+)
+
+ +
+

Arguments

+
networks
+

A concatenation of n PANDA-like networks with the following format: +tf,gene,score_net1, score_net2, ... , score_netn +Edges must be specified as "tf__gene".

+ + +
pValues
+

The p-values from the original network.

+ + +
startingNodes
+

The list of nodes from which to start.

+ + +
nodesToExclude
+

The list of nodes to exclude from the search.

+ + +
startFromTF
+

Whether to start from transcription factors (TRUE) or genes (FALSE).

+ + +
verbose
+

Whether or not to print detailed information about the run.

+ + +
topX
+

Select the X lowest significant p-values for each gene. NULL by default.

+ +
+
+

Value

+ + +

A bipartite subnetwork in the same format as the original networks.

+
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/Skin_data.html b/docs/reference/Skin_data.html new file mode 100644 index 00000000..f8378334 --- /dev/null +++ b/docs/reference/Skin_data.html @@ -0,0 +1,121 @@ + +Skin RNA-seq data from the GTEx consortium — Skin_data • netZooR + Skip to contents + + +
+
+
+

Skin RNA-seq data from the GTEx consortium

+ Source: R/YARN.R +
Skin_data.Rd
+
+ +
+

Skin RNA-seq data from the GTEx consortium. V6 release. Random selection of 20 skin samples. +13 of the samples are fibroblast cells, 5 Skin sun exposed, 2 sun unexposed.

+
+ +
+

Usage

+ 
data(skin)
+
+ +
+

Format

+

An object of class "ExpressionSet"; see ExpressionSet.

+
+
+

Source

+

GTEx Portal

+
+
+

Value

+ + +

ExpressionSet object

+
+
+

References

+

GTEx Consortium, 2015. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science, 348(6235), pp.648-660. +(PubMed)

+
+ +
+

Examples

+ 
data(skin);
+checkMissAnnotation(skin,"GENDER");
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/TIGER.html b/docs/reference/TIGER.html index fe96aa68..a2ddf668 100644 --- a/docs/reference/TIGER.html +++ b/docs/reference/TIGER.html @@ -1,5 +1,5 @@ -TIGER main function — tiger • netZooRTIGER main function — tiger • netZooR @@ -10,7 +10,7 @@ netZooR - 1.5.0 + 1.5.17 + + + + + +
+
+
+

Annotate your Expression Set with biomaRt

+ Source: R/YARN.R +
annotateFromBiomart.Rd
+
+ +
+

Annotate your Expression Set with biomaRt

+
+ +
+

Usage

+ 
annotateFromBiomart(
+  obj,
+  genes = featureNames(obj),
+  filters = "ensembl_gene_id",
+  attributes = c("ensembl_gene_id", "hgnc_symbol", "chromosome_name", "start_position",
+    "end_position"),
+  biomart = "ensembl",
+  dataset = "hsapiens_gene_ensembl",
+  ...
+)
+
+ +
+

Arguments

+
obj
+

ExpressionSet object.

+ + +
genes
+

Genes or rownames of the ExpressionSet.

+ + +
filters
+

getBM filter value, see getBM help file.

+ + +
attributes
+

getBM attributes value, see getBM help file.

+ + +
biomart
+

BioMart database name you want to connect to. Possible database names can be retrieved with teh function listMarts.

+ + +
dataset
+

Dataset you want to use. To see the different datasets available within a biomaRt you can e.g. do: mart = useMart('ensembl'), followed by listDatasets(mart).

+ + +
...
+

Values for useMart, see useMart help file.

+ +
+
+

Value

+ + +

ExpressionSet object with a fuller featureData.

+
+ +
+

Examples

+ 
download.file('https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/yarn/bladder.rdata',destfile='netZooR/data/bladder.rdata')
+download.file('https://netzoo.s3.us-east-2.amazonaws.com/netZooR/unittest_datasets/yarn/skin.rdata',destfile='netZooR/data/skin.rdata')
+data(skin)
+# subsetting and changing column name just for a silly example
+skin <- skin[1:10,]
+colnames(fData(skin)) = paste("names",1:6)
+biomart<-"ENSEMBL_MART_ENSEMBL";
+genes <- sapply(strsplit(rownames(skin),split="\\."),function(i)i[1])
+newskin <-annotateFromBiomart(skin,genes=genes,biomart=biomart)
+head(fData(newskin)[,7:11])
+
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/checkMisAnnotation.html b/docs/reference/checkMisAnnotation.html new file mode 100644 index 00000000..07f2d07f --- /dev/null +++ b/docs/reference/checkMisAnnotation.html @@ -0,0 +1,144 @@ + +Check for wrong annotation of a sample using classical MDS and control genes. — checkMisAnnotation • netZooR + Skip to contents + + +
+
+
+

Check for wrong annotation of a sample using classical MDS and control genes.

+ Source: R/YARN.R +
checkMisAnnotation.Rd
+
+ +
+

Check for wrong annotation of a sample using classical MDS and control genes.

+
+ +
+

Usage

+ 
checkMisAnnotation(
+  obj,
+  phenotype,
+  controlGenes = "all",
+  columnID = "chromosome_name",
+  plotFlag = TRUE,
+  legendPosition = NULL,
+  ...
+)
+
+ +
+

Arguments

+
obj
+

ExpressionSet object.

+ + +
phenotype
+

phenotype column name in the phenoData slot to check.

+ + +
controlGenes
+

Name of controlGenes, ie. 'Y' chromosome. Can specify 'all'.

+ + +
columnID
+

Column name where controlGenes is defined in the featureData slot if other than 'all'.

+ + +
plotFlag
+

TRUE/FALSE Whether to plot or not

+ + +
legendPosition
+

Location for the legend.

+ + +
...
+

Extra parameters for plotCMDS function.

+ +
+
+

Value

+ + +

Plots a classical multi-dimensional scaling of the 'controlGenes'. Optionally returns co-ordinates.

+
+ +
+

Examples

+ 
data(bladder)
+checkMisAnnotation(bladder,'GENDER',controlGenes='Y',legendPosition='topleft')
+
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/checkTissuesToMerge.html b/docs/reference/checkTissuesToMerge.html new file mode 100644 index 00000000..9c94c06d --- /dev/null +++ b/docs/reference/checkTissuesToMerge.html @@ -0,0 +1,143 @@ + +Check tissues to merge based on gene expression profile — checkTissuesToMerge • netZooR + Skip to contents + + +
+
+
+

Check tissues to merge based on gene expression profile

+ Source: R/YARN.R +
checkTissuesToMerge.Rd
+
+ +
+

Check tissues to merge based on gene expression profile

+
+ +
+

Usage

+ 
checkTissuesToMerge(
+  obj,
+  majorGroups,
+  minorGroups,
+  filterFun = NULL,
+  plotFlag = TRUE,
+  ...
+)
+
+ +
+

Arguments

+
obj
+

ExpressionSet object.

+ + +
majorGroups
+

Column name in the phenoData slot that describes the general body region or site of the sample.

+ + +
minorGroups
+

Column name in the phenoData slot that describes the specific body region or site of the sample.

+ + +
filterFun
+

Filter group specific genes that might disrupt PCoA analysis.

+ + +
plotFlag
+

TRUE/FALSE whether to plot or not

+ + +
...
+

Parameters that can go to checkMisAnnotation

+ +
+
+

Value

+ + +

CMDS Plots of the majorGroupss colored by the minorGroupss. Optional matrix of CMDS loadings for each comparison.

+
+
+

See also

+

checkTissuesToMerge

+
+ +
+

Examples

+ 
data(skin)
+checkTissuesToMerge(skin,'SMTS','SMTSD')
+
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/cobra.html b/docs/reference/cobra.html index 1080aa54..c8225091 100644 --- a/docs/reference/cobra.html +++ b/docs/reference/cobra.html @@ -4,7 +4,7 @@ linear combination of covariate-specific components. It can be applied for batch correction, differential co-expression analysis controlling for variables, and to understand the impact of - variables of interest to the observed co-expression.">Run COBRA in R — cobra • netZooRRun COBRA in R — cobra • netZooRnetZooR - 1.5.0 + 1.5.17 + + + + + +
+
+
+

Download GTEx files and turn them into ExpressionSet object

+ Source: R/YARN.R +
downloadGTEx.Rd
+
+ +
+

Downloads the V6 GTEx release and turns it into an ExpressionSet object.

+
+ +
+

Usage

+ 
downloadGTEx(type = "genes", file = NULL, ...)
+
+ +
+

Arguments

+
type
+

Type of counts to download - default genes.

+ + +
file
+

File path and name to automatically save the downloaded GTEx expression set. Saves as a RDS file.

+ + +
...
+

Does nothing currently.

+ +
+
+

Value

+ + +

Organized ExpressionSet set.

+
+ +
+

Examples

+ 
# obj <- downloadGTEx(type='genes',file='~/Desktop/gtex.rds')
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/dragon.html b/docs/reference/dragon.html index 54a0f210..da36b95a 100644 --- a/docs/reference/dragon.html +++ b/docs/reference/dragon.html @@ -1,5 +1,5 @@ -Run DRAGON in R. — dragon • netZooRRun DRAGON in R. — dragon • netZooR @@ -10,7 +10,7 @@ netZooR - 1.5.0 + 1.5.17 + + + + + +
+
+
+

Extract the appropriate matrix

+ Source: R/YARN.R +
extractMatrix.Rd
+
+ +
+

This returns the raw counts, log2-transformed raw counts, or normalized expression. +If normalized = TRUE then the log paramater is ignored.

+
+ +
+

Usage

+ 
extractMatrix(obj, normalized = FALSE, log = TRUE)
+
+ +
+

Arguments

+
obj
+

ExpressionSet object or objrix.

+ + +
normalized
+

TRUE / FALSE, use the normalized matrix or raw counts

+ + +
log
+

TRUE/FALSE log2-transform.

+ +
+
+

Value

+ + +

matrix

+
+ +
+

Examples

+ 

+data(skin)
+head(netZooR:::extractMatrix(skin,normalized=FALSE,log=TRUE))
+head(netZooR:::extractMatrix(skin,normalized=FALSE,log=FALSE))
+
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/filterGenes.html b/docs/reference/filterGenes.html new file mode 100644 index 00000000..43ce8ed4 --- /dev/null +++ b/docs/reference/filterGenes.html @@ -0,0 +1,133 @@ + +Filter specific genes — filterGenes • netZooR + Skip to contents + + +
+
+
+

Filter specific genes

+ Source: R/YARN.R +
filterGenes.Rd
+
+ +
+

The main use case for this function is the removal of sex-chromosome genes. +Alternatively, filter genes that are not protein-coding.

+
+ +
+

Usage

+ 
filterGenes(
+  obj,
+  labels = c("X", "Y", "MT"),
+  featureName = "chromosome_name",
+  keepOnly = FALSE
+)
+
+ +
+

Arguments

+
obj
+

ExpressionSet object.

+ + +
labels
+

Labels of genes to filter or keep, eg. X, Y, and MT

+ + +
featureName
+

FeatureData column name, eg. chr

+ + +
keepOnly
+

Filter or keep only the genes with those labels

+ +
+
+

Value

+ + +

Filtered ExpressionSet object

+
+ +
+

Examples

+ 
data(skin)
+filterGenes(skin,labels = c('X','Y','MT'),featureName='chromosome_name')
+filterGenes(skin,labels = 'protein_coding',featureName='gene_biotype',keepOnly=TRUE)
+
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/filterLowGenes.html b/docs/reference/filterLowGenes.html new file mode 100644 index 00000000..17bd31cc --- /dev/null +++ b/docs/reference/filterLowGenes.html @@ -0,0 +1,133 @@ + +Filter genes that have less than a minimum threshold CPM for a given group/tissue — filterLowGenes • netZooR + Skip to contents + + +
+
+
+

Filter genes that have less than a minimum threshold CPM for a given group/tissue

+ Source: R/YARN.R +
filterLowGenes.Rd
+
+ +
+

Filter genes that have less than a minimum threshold CPM for a given group/tissue

+
+ +
+

Usage

+ 
filterLowGenes(obj, groups, threshold = 1, minSamples = NULL, ...)
+
+ +
+

Arguments

+
obj
+

ExpressionSet object.

+ + +
groups
+

Vector of labels for each sample or a column name of the phenoData slot. +for the ids to filter. Default is the column names.

+ + +
threshold
+

The minimum threshold for calling presence of a gene in a sample.

+ + +
minSamples
+

Minimum number of samples - defaults to half the minimum group size.

+ + +
...
+

Options for cpm.

+ +
+
+

Value

+ + +

Filtered ExpressionSet object

+
+
+

See also

+

cpm function defined in the edgeR package.

+
+ +
+

Examples

+ 
data(skin)
+filterLowGenes(skin,'SMTSD')
+
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/filterMissingGenes.html b/docs/reference/filterMissingGenes.html new file mode 100644 index 00000000..e77baf5e --- /dev/null +++ b/docs/reference/filterMissingGenes.html @@ -0,0 +1,116 @@ + +Filter genes not expressed in any sample — filterMissingGenes • netZooR + Skip to contents + + +
+
+
+

Filter genes not expressed in any sample

+ Source: R/YARN.R +
filterMissingGenes.Rd
+
+ +
+

The main use case for this function is the removal of missing genes.

+
+ +
+

Usage

+ 
filterMissingGenes(obj, threshold = 0)
+
+ +
+

Arguments

+
obj
+

ExpressionSet object.

+ + +
threshold
+

Minimum sum of gene counts across samples -- defaults to zero.

+ +
+
+

Value

+ + +

Filtered ExpressionSet object

+
+ +
+

Examples

+ 
data(skin)
+filterMissingGenes(skin)
+
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/filterSamples.html b/docs/reference/filterSamples.html new file mode 100644 index 00000000..5997a598 --- /dev/null +++ b/docs/reference/filterSamples.html @@ -0,0 +1,126 @@ + +Filter samples — filterSamples • netZooR + Skip to contents + + +
+
+
+

Filter samples

+ Source: R/YARN.R +
filterSamples.Rd
+
+ +
+

Filter samples

+
+ +
+

Usage

+ 
filterSamples(obj, ids, groups = colnames(obj), keepOnly = FALSE)
+
+ +
+

Arguments

+
obj
+

ExpressionSet object.

+ + +
ids
+

Names found within the groups labels corresponding to samples to be removed

+ + +
groups
+

Vector of labels for each sample or a column name of the phenoData slot +for the ids to filter. Default is the column names.

+ + +
keepOnly
+

Filter or keep only the samples with those labels.

+ +
+
+

Value

+ + +

Filtered ExpressionSet object

+
+ +
+

Examples

+ 
data(skin)
+filterSamples(skin,ids = "Skin - Not Sun Exposed (Suprapubic)",groups="SMTSD")
+filterSamples(skin,ids=c("GTEX-OHPL-0008-SM-4E3I9","GTEX-145MN-1526-SM-5SI9T"))
+
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/genes.html b/docs/reference/genes.html index 2bd3c5ca..6dc8e4a9 100644 --- a/docs/reference/genes.html +++ b/docs/reference/genes.html @@ -1,6 +1,6 @@ Example of a gene list — genes • netZooRExample of a gene list — genes • netZooRFunction reference • netZooRFunction reference • netZooR @@ -10,7 +10,7 @@ netZooR - 1.5.0 + 1.5.17 + + + + + +
+
+
+

Normalize in a tissue aware context

+ Source: R/YARN.R +
normalizeTissueAware.Rd
+
+ +
+

This function provides a wrapper to various normalization methods developed. +Currently it only wraps qsmooth and quantile normalization returning a log-transformed +normalized matrix. qsmooth is a normalization approach that normalizes samples in +a condition aware manner.

+
+ +
+

Usage

+ 
normalizeTissueAware(
+  obj,
+  groups,
+  normalizationMethod = c("qsmooth", "quantile"),
+  ...
+)
+
+ +
+

Source

+

The function qsmooth comes from the qsmooth packages +currently available on github under user 'kokrah'.

+
+
+

Arguments

+
obj
+

ExpressionSet object

+ + +
groups
+

Vector of labels for each sample or a column name of the phenoData slot +for the ids to filter. Default is the column names

+ + +
normalizationMethod
+

Choice of 'qsmooth' or 'quantile'

+ + +
...
+

Options for qsmooth function or normalizeQuantiles

+ +
+
+

Value

+ + +

ExpressionSet object with an assayData called normalizedMatrix

+
+ +
+

Examples

+ 
data(skin)
+normalizeTissueAware(skin,"SMTSD")
+
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/otter.html b/docs/reference/otter.html index 522ad566..8363fe4c 100644 --- a/docs/reference/otter.html +++ b/docs/reference/otter.html @@ -4,7 +4,7 @@ motif (W), TF PPI (P), and gene coexpression (C) through minimzing the following objective: min f(W) - with f(W) = (1-lambda)*||WW' - P||^2 + lambda*||W'W - C||^2 + (gamma/2)*||W||^2">Run OTTER in R — otter • netZooRRun OTTER in R — otter • netZooRnetZooR - 1.5.0 + 1.5.17 + + + + + +
+
+
+

Plot classical MDS of dataset

+ Source: R/YARN.R +
plotCMDS.Rd
+
+ +
+

This function plots the MDS coordinates for the "n" features of interest. Potentially uncovering batch +effects or feature relationships.

+
+ +
+

Usage

+ 
plotCMDS(
+  obj,
+  comp = 1:2,
+  normalized = FALSE,
+  distFun = dist,
+  distMethod = "euclidian",
+  n = NULL,
+  samples = TRUE,
+  log = TRUE,
+  plotFlag = TRUE,
+  ...
+)
+
+ +
+

Arguments

+
obj
+

ExpressionSet object or objrix.

+ + +
comp
+

Which components to display.

+ + +
normalized
+

TRUE / FALSE, use the normalized matrix or raw counts.

+ + +
distFun
+

Distance function, default is dist.

+ + +
distMethod
+

The distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Any unambiguous substring can be given.

+ + +
n
+

Number of features to make use of in calculating your distances.

+ + +
samples
+

Perform on samples or genes.

+ + +
log
+

TRUE/FALSE log2-transform raw counts.

+ + +
plotFlag
+

TRUE/FALSE whether to plot or not.

+ + +
...
+

Additional plot arguments.

+ +
+
+

Value

+ + +

coordinates

+
+ +
+

Examples

+ 
data(skin)
+res <- plotCMDS(skin,pch=21,bg=factor(pData(skin)$SMTSD))
+# \donttest{
+# library(calibrate)
+# textxy(X=res[,1],Y=res[,2],labs=rownames(res))
+# }
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/plotDensity.html b/docs/reference/plotDensity.html new file mode 100644 index 00000000..8e40a232 --- /dev/null +++ b/docs/reference/plotDensity.html @@ -0,0 +1,132 @@ + +Density plots of columns in a matrix — plotDensity • netZooR + Skip to contents + + +
+
+
+

Density plots of columns in a matrix

+ Source: R/YARN.R +
plotDensity.Rd
+
+ +
+

Plots the density of the columns of a matrix. Wrapper for matdensity.

+
+ +
+

Usage

+ 
plotDensity(obj, groups = NULL, normalized = FALSE, legendPos = NULL, ...)
+
+ +
+

Arguments

+
obj
+

ExpressionSet object

+ + +
groups
+

Vector of labels for each sample or a column name of the phenoData slot +for the ids to filter. Default is the column names.

+ + +
normalized
+

TRUE / FALSE, use the normalized matrix or log2-transformed raw counts

+ + +
legendPos
+

Legend title position. If null, does not create legend by default.

+ + +
...
+

Extra parameters for matdensity.

+ +
+
+

Value

+ + +

A density plot for each column in the ExpressionSet object colored by groups

+
+ +
+

Examples

+ 
data(skin)
+filtData <- filterLowGenes(skin,"SMTSD")
+plotDensity(filtData,groups="SMTSD",legendPos="topleft")
+# to remove the legend
+plotDensity(filtData,groups="SMTSD")
+
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/plotHeatmap.html b/docs/reference/plotHeatmap.html new file mode 100644 index 00000000..b896a5ad --- /dev/null +++ b/docs/reference/plotHeatmap.html @@ -0,0 +1,142 @@ + +Plot heatmap of most variable genes — plotHeatmap • netZooR + Skip to contents + + +
+
+
+

Plot heatmap of most variable genes

+ Source: R/YARN.R +
plotHeatmap.Rd
+
+ +
+

This function plots a heatmap of the gene expressions forthe "n" features of interest.

+
+ +
+

Usage

+ 
plotHeatmap(obj, n = NULL, fun = stats::sd, normalized = TRUE, log = TRUE, ...)
+
+ +
+

Arguments

+
obj
+

ExpressionSet object or objrix.

+ + +
n
+

Number of features to make use of in plotting heatmap.

+ + +
fun
+

Function to sort genes by, default sd.

+ + +
normalized
+

TRUE / FALSE, use the normalized matrix or raw counts.

+ + +
log
+

TRUE/FALSE log2-transform raw counts.

+ + +
...
+

Additional plot arguments for heatmap.2.

+ +
+
+

Value

+ + +

coordinates

+
+ +
+

Examples

+ 
data(skin)
+tissues <- pData(skin)$SMTSD
+plotHeatmap(skin,normalized=FALSE,log=TRUE,trace="none",n=10)
+# Even prettier
+# \donttest{
+# library(RColorBrewer)
+data(skin)
+tissues <- pData(skin)$SMTSD
+heatmapColColors <- brewer.pal(12,"Set3")[as.integer(factor(tissues))]
+heatmapCols <- colorRampPalette(brewer.pal(9, "RdBu"))(50)
+plotHeatmap(skin,normalized=FALSE,log=TRUE,trace="none",n=10,
+ col = heatmapCols,ColSideColors = heatmapColColors,cexRow = 0.6,cexCol = 0.6)
+# }
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/priorPp.html b/docs/reference/priorPp.html index d05b38d2..3d6c8b7c 100644 --- a/docs/reference/priorPp.html +++ b/docs/reference/priorPp.html @@ -1,5 +1,5 @@ -Filter low confident edge signs in the prior network using GeneNet — priorPp • netZooRFilter low confident edge signs in the prior network using GeneNet — priorPp • netZooR @@ -10,7 +10,7 @@ netZooR - 1.5.0 + 1.5.17 + + + + + +
+
+
+

Quantile shrinkage normalization

+ Source: R/YARN.R +
qsmooth.Rd
+
+ +
+

This function was modified from github user kokrah.

+
+ +
+

Usage

+ 
qsmooth(
+  obj,
+  groups,
+  norm.factors = NULL,
+  plot = FALSE,
+  window = 0.05,
+  log = TRUE
+)
+
+ +
+

Source

+

Kwame Okrah's qsmooth R package

+
+
+

Arguments

+
obj
+

for counts use log2(raw counts + 1)), for MA use log2(raw intensities)

+ + +
groups
+

groups to which samples belong (character vector)

+ + +
norm.factors
+

scaling normalization factors

+ + +
plot
+

plot weights? (default=FALSE)

+ + +
window
+

window size for running median (a fraction of the number of rows of exprs)

+ + +
log
+

Whether or not the data should be log transformed before normalization, TRUE = YES.

+ +
+
+

Value

+ + +

Normalized expression

+
+ +
+

Examples

+ 
data(skin)
+head(netZooR:::qsmooth(skin,groups=pData(skin)$SMTSD))
+
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/qstats.html b/docs/reference/qstats.html new file mode 100644 index 00000000..cbdc61c2 --- /dev/null +++ b/docs/reference/qstats.html @@ -0,0 +1,119 @@ + +Compute quantile statistics — qstats • netZooR + Skip to contents + + +
+
+
+

Compute quantile statistics

+ Source: R/YARN.R +
qstats.Rd
+
+ +
+

This function was directly borrowed from github user kokrah.

+
+ +
+

Usage

+ 
qstats(exprs, groups, window)
+
+ +
+

Source

+

Kwame Okrah's qsmooth R package +Compute quantile statistics

+
+
+

Arguments

+
exprs
+

for counts use log2(raw counts + 1)), for MA use log2(raw intensities)

+ + +
groups
+

groups to which samples belong (character vector)

+ + +
window
+

window size for running median as a fraction on the number of rows of exprs

+ +
+
+

Value

+ + +

list of statistics

+
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/runEgret.html b/docs/reference/runEgret.html index 86336954..d8ad10b8 100644 --- a/docs/reference/runEgret.html +++ b/docs/reference/runEgret.html @@ -3,7 +3,7 @@ NOTE: Beta version. EGRET infers individual-specific gene regulatory networks using inidividual level data - a genotype vcf file (v) and QBiC binding predictions (q) - as well as population/reference level data - eQTLs (b), a motif-gene prior (m), PPI network (p), and gene expression (e). An annotation file g is also used to - map TF names to their corresponding ensemble ids.">Run EGRET in R — runEgret • netZooRRun EGRET in R — runEgret • netZooRnetZooR - 1.5.0 + 1.5.17