Direction Pathway Analysis Package

Description

The directPA-package is designed for analysing pathways in experiments with multiple perturbations. The combination effects of different treatments are tested by rotating polar coordinates in two-dimentional space when the experiment contains two perturbations and corresponding controls, or spherical coordinates in three-dimensional space when the experiment contains three perturbations and corresponding controls.

Details

Author(s)

Pengyi Yang <yangpy7@gmail.com> & Ellis Patrick <epatrick87@gmail.com>

References

Pengyi Yang, Ellis Patrick, Shi-Xiong Tan, Daniel J. Fazakerley, James Burchfield, Christopher Gribben, Matthew J. Prior, David E. James, Yee Hwa Yang, Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway, Bioinformatics, 30(6), 808-814, 2014.

Phosphoproteomics of HEK-293E

Description

The data object contains all quantified phosphoryaltion sites in HEK-293E cells with Rapamycin or Torin1 treatment vs insulin stimulation. See Hsu et al. Science, 332(6035), 1317-1322, 2011, for more details.

Plasma Membrame Protoemics Data

Description

The data object contains all quantified plasma membrame proteins with insulin simulation with and without prior treatments of MK or Wmn vs basal condition in mouse 3T3L1 cell lines. Please refer to the article: Yang et al. Bioinformatics, 30(6):808-14, 2014.

KEGG pathway annotations

Description

The data object contains the annotations of KEGG pathways.

Reactome pathway annotations

Description

The data object contains the annotations of reactome pathways.

PhosphoELM annotations for human

Description

The data object contains the annotations of kinases and their conrresponding substrates as phosphorylation sites in human. It is extracted from the PhosphoELM database. For details of PhosphoELM, please refer to the article: Phospho.ELM: a database of phosphorylation sites–update 2008. Diella, F., Gould, C.M., Chica, C., Via, A. & Gibson, T.J. Nucleic Acids Res. 2008 Jan;36(Database issue):D240-4.

PhosphoELM annotations for mouse

Description

The data object contains the annotations of kinases and their conrresponding substrates as phosphorylation sites in mouse. It is extracted from the PhosphoELM database. For details of PhosphoELM, please refer to the article: Phospho.ELM: a database of phosphorylation sites–update 2008. Diella, F., Gould, C.M., Chica, C., Via, A. & Gibson, T.J. Nucleic Acids Res. 2008 Jan;36(Database issue):D240-4.

PhosphoSitePlus annotations for human

Description

The data object contains the annotations of kinases and their conrresponding substrates as phosphorylation sites in human. It is extracted from the PhosphoSitePlus database. For details of PhosphoSitePlus, please refer to the article: Hornbeck et al. Nucleic Acids Res., 40:D261-70, 2012.

PhosphoSitePlus annotations for mouse

Description

The data object contains the annotations of kinases and their conrresponding substrates as phosphorylation sites in mouse. It is extracted from the PhosphoSitePlus database. For details of PhosphoSitePlus, please refer to the article: Hornbeck et al. Nucleic Acids Res., 40:D261-70, 2012.

Batch Direction Analysis in 2-dimentional space

Description

Rotate to the direction of interest in polar coordinates by degree (e.g. pi/4).

Usage

directExplorer2d(Tc, annotation=NULL, gene.method="OSP", 
path.method="Stouffer", top=10, nd=8, ...)

Arguments

Value

The the list of enrichment analysis in tables.

Examples

# load the phosphoproteomics dataset
data(HEK)

# load the kinase-substrate annoations
data(PhosphoSite)

# test enrichment on 8 directions in polar coordinate system.
bda <- directExplorer2d(Tc=HEK, annotation=PhosphoSite.mouse)

# the direction are denoted as follow for the two treatments vs control:
# ++: up-regulated in both treatments
# +*: up-regulated in the first treatment and unchanged in the second treatment
# +-: up-regulated in the first treatment and down-regulated in the second treatment
# *-: unchanged in the first treatment and down-regulated in the second treatment
# --: down-regulated in both treatments
# -*: down-regulated in the first treatment and unchanged in the second treatment
# -+: down-regulated in the first treatment and up-regulated in the second treatment
# *+: unchanged in the first treatment and up-regulated in the second treatment

# sort the most enriched phosphorylation sites and kinases on down-regulaiton from both 
# treatments (i.e. "--") and displa the top-10 entries
bda$gene.tab[order(bda$gene.tab[,"--"]),][1:10,]
bda$path.tab[order(bda$path.tab[,"--"]),][1:10,]

Direction Analysis for Pathways

Description

The main function of direction Analysis. This function takes in a matrix of test statistics with two (2-dimensional space) or three (3-dimensional space) columns, the direction of interests, and the annotation list such as pathway annotation, and test for enrichment of pathways on the specified direction.

Usage

directPA(Tc, direction, annotation, minSize=5, gene.method="OSP", 
path.method="Stouffer", visualize=TRUE, ...)

Arguments

Value

a list that contains directional p-values for each gene and directional enrichment for each pathway.

Examples

# load the proteomics dataset
data(PM)

# load pathway annotations
data(Pathways)

# display reactome pathways. Could be replaced by any other pathway databases
Pathways.reactome[1:5]

# direction pathway analysis in 3-dimensional space. Implemnted as rotating by contrast
# (1) test combined effect of all 3 treatments (stimulation and inhibitions) vs control (basal) 
# on the original direction.
dPA <- directPA(Tc=PM, direction=c(1,1,1), annotation=Pathways.reactome)
dPA$gst[order(unlist(dPA$gst[,1])),][1:20,]
# rank substrates on the direciton of interest
sort(dPA$gene.pvalues)[1:20]

# (2) test combined effect of all 3 treatments vs controls on direction c(1,-1, 0)
# this rotates Ins by 0 degree, Wmn by 90 degree, and MK by 45 degree.
dPA <- directPA(Tc=PM, direction=c(1,-1,0), annotation=Pathways.reactome)
dPA$gst[order(unlist(dPA$gst[,1])),][1:20,]

# (3) test combined effect of all 3 perturbations vs controls on direction c(1,-1, 1)
# this rotates Ins by 0 degree, Wmn by 90 degree, and MK by 0 degree.
dPA <- directPA(Tc=PM, direction=c(1,-1,1), annotation=Pathways.reactome)
dPA$gst[order(unlist(dPA$gst[,1])),][1:20,]

Molecule Level Statistics

Description

Takes a vector of statistics with each element corresponds to a treatment vs control comparison, and calculates a combined statistics accross multiple treatments.

Usage

geneStats(T, method="OSP")

Arguments

Value

a p-value after integration across treatments.

Examples

# load the example data
data(PM)

# convert statistics into z-scores
PM.zscores <- apply(PM, 2, function(x){qnorm(rank(x)/(nrow(PM)+1))})

# Rotate the matrix by contrast 1, -1, -1 (i.e. up-regulation, down-regulation, dow-regulation).
PM.rotated <- rotate3d(PM.zscores, contrast = c(1, -1, -1))

# combine rotated statistics across treatments
gene.pvalues <- apply(PM.rotated, 1, geneStats)

Direction Analysis for Kinases

Description

This is a wrapper for runing directPA for kinase perturbation analysis (kinasePA)

Usage

kinasePA(Tc, direction, annotation, minSize=5, substrate.method="OSP", 
kinase.method="Stouffer", visualize=TRUE, ...)

Arguments

Value

a list that contains directional p-values for each substrate and directional enrichment for each kinase.

Examples

# load the phosphoproteomics dataset
data(HEK)

# load the kinase-substrate annoations
data(PhosphoSite)

# direction pathway analysis in 2-dimensional space. Implemented as rotating by degree 
# (1) test combined effect of Torin1 and Rapamycin vs insul both on "down-regulation"
# (180 degree to original direction)
kPA <- kinasePA(Tc=HEK, direction=pi, annotation=PhosphoSite.mouse)
kPA$kinase[order(unlist(kPA$kinase[,1])),][1:20,]
# rank substrates on the direciton of interest
sort(kPA$substrate.pvalues)[1:20]

# (2) test combined effect of Torin1 and Rapamycin vs insul on "no change and down-regulation"
# (135 degree to the original direction) 
kPA <- kinasePA(Tc=HEK, direction=pi*3/4, annotation=PhosphoSite.mouse)
kPA$kinase[order(unlist(kPA$kinase[,1])),][1:20,]

# (3) test combined effect of Torin1 and Rapamycin vs insul on "down-regulation and no change"
# (225 degree to the original direction) 
kPA <- kinasePA(Tc=HEK, direction=pi*5/4, annotation=PhosphoSite.mouse)
kPA$kinase[order(unlist(kPA$kinase[,1])),][1:20,]

Pathway Level Statistics

Description

Takes a vector of statistics with each element corresponds to a gene or phosphorylation site, and calculates a combined statistics for those that belong to the same pathway or kinase.

Usage

pathwayStats(PGs, T, minSize=5, method="Stouffer")

Arguments

Value

a doublet corresponding to the enrichment after integration across all genes or substrates that belong to the same pathway or kinase, and the size of the mapped genes or substrates to that pathway or kinase.

Examples

# load the example data
data(PM)

# load pathway annotations
data(Pathways)

# convert statistics into z-scores
PM.zscores <- apply(PM, 2, function(x){qnorm(rank(x)/(nrow(PM)+1))})

# Rotate the matrix by contrast 1, -1, -1 (i.e. up-regulation, down-regulation, dow-regulation).
PM.rotated <- rotate3d(PM.zscores, contrast = c(1, -1, -1))

# combine rotated statistics across treatments
gene.pvalues <- apply(PM.rotated, 1, geneStats)

# compute statistics for all reactome pathways
gene.zscores <- qnorm(gene.pvalues, lower.tail = FALSE)
gst <- t(sapply(Pathways.reactome, pathwayStats, gene.zscores))

Perturbation Plot

Description

This function takes in a matrix of test statistics with two columns (2-dimensional space) and the annotation list such as pathway annotation or kinase-substrate annotation, and visualize the enrichment of pathways or kinases in direction specific manner.

Usage

perturbPlot2d(Tc, annotation, minSize=5, ...)

Arguments

Value

a list of coordinates for pathways or kinases

Examples

# load the phosphoproteomics dataset
data(HEK)

# load the kinase-substrate annoations
data(PhosphoSite)

perturbPlot2d(Tc=HEK, annotation=PhosphoSite.mouse, cex=3)

Perturbation Plot 3D

Description

This function takes in a matrix of test statistics with two columns (3-dimensional space) and the annotation list such as pathway annotation or kinase-substrate annotation, and visualize the enrichment of pathways or kinases in direction specific manner.

Usage

perturbPlot3d(Tc, annotation, minSize=5, ...)

Arguments

Value

a list of coordinates for pathways or kinases

Polar Coordinates Rotation

Description

Rotate to the direction of interest in polar coordinates by degree (e.g. pi/4).

Usage

rotate2d(T, degree = 0)

Arguments

Value

A rotated matrix with respect to the direction of interest.

Examples

# load the phosphoproteomics dataset
data(HEK)

# convert statistics into z-scores
HEK.zscores <- apply(HEK, 2, function(x){qnorm(rank(x)/(nrow(HEK)+1))})

# Rotate the matrix by 1/2 pi (i.e. down-regulation, dow-regulation).
HEK.rotated <- rotate2d(HEK.zscores, degree = pi/2)

Spherical Coordinates Rotation

Description

Rotate to the direction of interest in spherical coordinates by contrasts (e.g. 1, -1, -1).

Usage

rotate3d(T, contrast)

Arguments

Value

A rotated matrix with respect to the direction of interest.

Examples

# load the example data
data(PM)

# convert statistics into z-scores
PM.zscores <- apply(PM, 2, function(x){qnorm(rank(x)/(nrow(PM)+1))})

# Rotate the matrix by contrast 1, -1, -1 (i.e. up-regulation, down-regulation, dow-regulation).
PM.rotated <- rotate3d(PM.zscores, contrast = c(1, -1, -1))