Package 'OMICsPCA'

Title: An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples
Description: OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals.
Authors: Subhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb]
Maintainer: Subhadeep Das <[email protected]>
License: GPL-3
Version: 1.25.0
Built: 2024-10-30 08:30:51 UTC
Source: https://github.com/bioc/OMICsPCA

Help Index

Quick analysis and visualization of the individuals/annotations/rows (e.g. Tss, gene) from integrated Assay.

Description

This function displays scatterplot of the individuals from integrated assays. This allows the display of scatterplots both in 2D and 3D.

Usage

analyse_individuals(name,Assay, choice, PC, group,
groupinfo = NULL, ...)

Arguments

name

Name of the "PCA" object containing the assay data

Assay

Name of an Assay

choice 
1 = 2D scatterplot on selected PCs through PC argument.
2 = 3D scatterplot on selected PCs through PC argument

for details of the choices see the vignette
PC

A vector of numbers corresponding to principal components

group

A vector of group names. Required only if choice = 2

groupinfo

output of create_group, or similar object containing group information.

...

additional arguments allowed to base function "plot3d" of package "rgl"

Value

Displays various plots and tables as per the combination of input arguments

Author(s)

Subhadeep Das <[email protected]>

Examples

PCAlist <- integrate_variables(Assays = c("H2az","H3k4me1",
"H3k9ac"), name = multi_assay,
groups = c("WE","RE"), groupinfo = groupinfo_ext,
scale.unit = FALSE, graph = FALSE)

analyse_individuals(name = PCAlist,
Assay = "H3k9ac", groupinfo = groupinfo_ext,
choice = 1, PC = c(1,2))

Quick analysis and visualization of the individuals/annotations/rows (e.g. Tss, gene)

Description

This function works similarly as "analyse_individuals()". The only extra argument needs to be supplied here is "start_end", (if choice = 1.) returned by "integrate_pca()"

Usage

analyse_integrated_individuals(
name, choice = 1, geom = "point",
PC = c(1,2), groupinfo = NULL, ...)

Arguments

name

Name of the "PCA" object containing the integrated PCA.

choice 
1 = 2D scatterplot on selected PCs through PC argument.
2 = 3D scatterplot on selected PCs through PC argument

for details of the choices see the vignette
geom

Used when choice = 1. Avalilabe options are : "point", "text", c("point","text"). default = "point"

PC

A vector of numbers corresponding to principal components

groupinfo

same as integrate_variables()

...

additional arguments allowed to base function "plot3d" of package "rgl"

Value

Displays various plots and tables as per the combination of input arguments

Author(s)

Subhadeep Das <[email protected]>

Examples

exclude <- list(0,c(1,9))

int_PCA <- integrate_pca(Assays = c("H2az",
"H3k9ac"), name = multi_assay, mergetype = 2,
exclude = exclude,
groupinfo = groupinfo_ext, graph = FALSE)


name = int_PCA$int_PCA

analyse_integrated_individuals(
name = name,
choice = 2, PC = c(1,2,3),
col = c("RED", "BLACK","GREEN"),
groupinfo = groupinfo_ext)

Quick analysis and visualization of the integrated Assays by integrate() function.

Description

This function is designed for a quick analysis and visualization of the data integrated by "integrateAssays()"" function. It works similarly as analyse_variables. In addition to that it takes an additinal argument "start_end" returned by "integrateAssays()".

Usage

analyse_integrated_variables(start_end = start_end, Assay = "all",
name, choice, title = NULL, PC = 1,
var_type = "contrib", ...)

Arguments

start_end

a list as returned by "integrate_pca()"

Assay

Name of an Assay. default = "all"

name

Name of the "PCA" object returned by "integrate_pca()"

choice 
1 = variance barplot
2 = Loadings of cell lines on selected PCs
3 = hCorrelation matrix
4 = Squarred loadings of Cell lines on a PC
5 = Contribution of Cell lines on a PC
for details of the choices see the vignette
title

title of plots

PC

The PC on which the plots will be drawn. used in choice 4 and 5. default = 1

var_type

Used when choice = 2. available options are: "coord", "cos2" and "contrib"

...

additional arguments allowed to base functions "used in various choices. See vignettes for details.

Value

Displays various plots and tables as per the combination of input arguments

Author(s)

Subhadeep Das <[email protected]>

Examples

exclude <- list(0,c(1,9))

int_PCA <- integrate_pca(Assays = c("H2az",
"H3k9ac"), name = multi_assay, mergetype = 2,
exclude = exclude, graph = FALSE)

start_end = int_PCA$start_end

name = int_PCA$int_PCA


analyse_integrated_variables(start_end = start_end, name = name,
choice = 1, title = "variance barplot", Assay = 1, addlabels = TRUE)

Quick analysis and visualization of the integrated Assays by integrate() function.

Description

This function is designed for a quick analysis and visualization of the data integrated by integrate() function. It takes 3 compulsory arguments name, Assay and choice. The type of analysis should be selected through the "choice" argument. This function acts as a wrapper around a collection of functions of package "factoextra" and "corrplot" and thus can take additional arguments specific and non-conflicting to such functions.

Usage

analyse_variables(name, Assay, choice, title = NULL, PC = 1,
var_type = "contrib", ...)

Arguments

name

Name of the "PCA" object

Assay

Name of an Assay.

choice 
1 = variance barplot
2 = Loadings of cell lines on selected PCs
3 = hCorrelation matrix
4 = Squarred loadings of Cell lines on a PC
5 = Contribution of Cell lines on a PC

for details of the choices see the vignette
title

Title of the plot. default = NULL

PC

used in choice = 4 and 5. Indicates the PC on which choice 4 and 5 will depend. default = 1

var_type

used in choice = 2. available options are: "coord", "cos2" and "contrib". default = "contrib"

...

extra arguments passed through the functions imported from various packages. See vignettes for details.

Value

Displays various plots and tables as per the combination of input arguments

Author(s)

Subhadeep Das <[email protected]>

Examples

PCAlist <- integrate_variables(Assays = c("H2az","H3k4me1",
"H3k9ac"), name = multi_assay,
groups = c("WE","RE"), groupinfo = groupinfo_ext,
scale.unit = FALSE, graph = FALSE)

analyse_variables(name = PCAlist, Assay = "H2az", choice = 1,
title = "variance barplot", addlabels = TRUE)

Pairwise correlation, scatter plot and histogram on selected groups.

Description

This function creates pairwise correlation plots and tables; scatter plots and histograms on selected groups. The type of results should be passed through the argument 'choice'. This is a wrapper on various functions and thus can take additional and non-conflicting arguments specific to them.

Usage

chart_correlation(name, Assay, groups, choice,
groupinfo = NULL,
...)

Arguments

name

Name of the "MultiAssayExperiment" object containing the assay data

Assay

Name of an assay

groups

Name of all or subset of groups

choice 

"table" = correlation table

"scatter" = scatterplot of each pair of columns/variables of
selected groups

"hist" = histogram of each column/variables

"all" = all of 1,2,3 together
groupinfo

output of create_group or similar object.

...

additional arguments for base functions "chart.Correlation" and "pairs"

Value

Displays various plots and tables as per the combination of input arguments

Author(s)

Subhadeep Das <[email protected]>

Examples

chart_correlation(name = multi_assay,
Assay = "H2az", groupinfo = groupinfo_ext,
groups = "WE", choice = "table")

Cluster data points

Description

Clustering of data points using various algorithms

Usage

cluster(name, n = NULL, graph = NULL, choice, title = NULL, ...)

Arguments

name

name of the dataframe or matrix object containing the coordinates of data points. The output of "extract()" may be directly put here.

n

Number of clusters

graph

logical. Plots the clusterplot on first 2 dimensions if set TRUE

choice

Clustering algorithm to use. Available choices are: "density", "kmeans", "pam"

title

Title of the plot

...

additional non-conflicting arguments to cluster functions

Value

returns a list containing the cluster and plot information

Author(s)

Subhadeep Das

References

Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Institute for Computer Science, University of Munich. Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96).

Forgy, E. W. (1965). Cluster analysis of multivariate data: efficiency vs interpretability of classifications. Biometrics, 21, 768–769.

Hartigan, J. A. and Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28, 100–108. doi: 10.2307/2346830.

Lloyd, S. P. (1957, 1982). Least squares quantization in PCM. Technical Note, Bell Laboratories. Published in 1982 in IEEE Transactions on Information Theory, 28, 128–137.

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam & J. Neyman, 1, pp. 281–297. Berkeley, CA: University of California Press.

Examples

exclude <- list(0,c(1,9))

int_PCA <- integrate_pca(Assays = c("H2az",
"H3k9ac"),
groupinfo = groupinfo,
name = multi_assay, mergetype = 2,
exclude = exclude, graph = FALSE)

name = int_PCA$int_PCA

data <- extract(name = name, PC = c(1:4),
groups = c("WE","RE"), integrated = TRUE,
rand = 300, groupinfo = groupinfo_ext)

clusters <- cluster(name = data, n = 2,
choice = "kmeans",
title = "kmeans on 2 clusters")

Comparison of clusters by boxplot

Description

boxplot of the assay values in each cluster

Usage

cluster_boxplot(name, Assay, clusterobject, clustercolumn, choice = NULL)

Arguments

name
Assay

name of the assay which is to be compared

clusterobject

a cluster object returned by the cluster() function

clustercolumn

the column in the clusterobject containing the group membership information

choice

type of graph

Value

a ggplot object

Author(s)

Subhadeep Das

Examples

bp <- cluster_boxplot(name = multi_assay,
Assay = "H2az", clusterobject = clustered_data,
clustercolumn = 5)


bp

Detection of algorithm and number of clusters

Description

Detection of appropriate clustering algorithm and cluster number for given data using "clvalid" and "NbClust" in background for cluster validation.

Usage

cluster_parameters(name, comparisonAlgorithm = "clValid",
optimal = FALSE, n = 2:6,
clusteringMethods = c("kmeans", "pam"),
validationMethods = c("internal", "stability"),
distance = "euclidean", ...)

Arguments

name

dataframe returned by "extract()".

optimal

logical. If TRUE, returns a dataframe of optimal results

n

a vector of numbers corresponding to the number of clusters to be tested or validated

comparisonAlgorithm

2 choices available: "clValid" or "NbClust"

clusteringMethods

a vector of single or multiple names of clustering algorithms. available choices are:

1) if comparisonAlgorithm = "clValid" : "hierarchical", "kmeans", "diana", "fanny", "som", "model", "sota", "pam", "clara" and "agnes"

2) if comparisonAlgorithm = "NbClust" : "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid", "kmeans".

validationMethods

name of the method to validate clusters. Available options (one or more):

1) if comparisonAlgorithm = "clValid" then one of:

"kl", "ch", "hartigan", "ccc", "scott", "marriot", "trcovw", "tracew", "friedman", "rubin", "cindex", "db", "silhouette", "duda", "pseudot2", "beale", "ratkowsky", "ball", "ptbiserial", "gap", "frey", "mcclain", "gamma", "gplus", "tau", "dunn", "hubert", "sdindex", "dindex", "sdbw", "all" (all indices except GAP, Gamma, Gplus and Tau), "alllong" (all indices with Gap, Gamma, Gplus and Tau included).

2) if comparisonAlgorithm = "NbClust" then one or more of:

"internal", "stability", and "biological"

distance

metric used to calculate distance matrix. options:

1) for "clValid" :

"euclidean", "correlation", and "manhattan".

2) for "NbClust" :

This must be one of: "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"

...

additional non-conflicting arguments to "clValid" or "Nbclust"

Value

1) for "clValid"

an object of class "clValid" (optimal = FALSE) or a dataframe of optimal values (optimal = TRUE)

2) for "NbClust"

a list of :

All.index, All.CriticalValues, Best.nc and Best.partition.

See the help pages of "clValid" (?clValid) and "NbClust" (?NbClust) for more details.

Author(s)

Subhadeep Das

References

Brock, G., Pihur, V., Datta, S. and Datta, S. (2008) clValid: An R Package for Cluster Validation Journal of Statistical Software 25(4) http://www.jstatsoft.org/v25/i04

Charrad M., Ghazzali N., Boiteau V., Niknafs A. (2014). "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set.", "Journal of Statistical Software, 61(6), 1-36.", "URL http://www.jstatsoft.org/v61/i06/".

Examples

exclude <- list(0,c(1,9))

int_PCA <- integrate_pca(Assays = c("H2az",
"H3k9ac"),
groupinfo = groupinfo,
name = multi_assay, mergetype = 2,
exclude = exclude, graph = FALSE)

name = int_PCA$int_PCA

data <- extract(name = name, PC = c(1:4),
groups = c("WE","RE"), integrated = TRUE, rand = 600,
groupinfo = groupinfo_ext)

#### Using "clValid" ####

clusterstats <- cluster_parameters(name = data,
optimal = FALSE, n = 2:4, comparisonAlgorithm = "clValid",
distance = "euclidean", clusteringMethods = c("kmeans"),
validationMethods = c("internal"))

Subsets an Assay dataframe into smaller groups

Description

This function subsests an user defined Assay into smaller groups according to user supplied instructions.

Usage

create_group(name, group_names = NULL,
                grouping_factor = NULL,
                comparison = NULL,
                condition = NULL
                )

Arguments

name

Name of the "MultiAssayExperiment"" object containing the assay data

group_names

A vector containing the user defined names of the groups to be created

grouping_factor

name of the dataframe on which grouping will be done

comparison

A vector of comparison symbols such as >, <, ==, >=, <=,%in%, etc

condition

A vector of conditions corresponding to 'comparison'. "condition"" should be a vector or range of digits (e.g. c(1,3,7,9) or 1:5) if%in% is chosen as comparison. Otherwise, a single digit should be chosen.

Value

returns the group membership of individuals as a dataframe

Author(s)

Subhadeep Das <[email protected]>

Examples

groupinfo <- create_group(name = multi_assay,
group_names = c("WE","RE","NE","IntE"),
grouping_factor = "CAGE",
comparison = c(">=","%in%","==","%in%"),
condition = c("25","1:5","0","6:24"))

head(groupinfo)

Distribution of Assays as percentage of cell lines

Description

descriptor() produces panels of boxplots displaying the distribution of various factors(e.g. ChIP-seq of H3k9ac histone modification) in percentage of cell lines.

choice = 1 output is collection of boxplots explaining the distribution of percentage of cell lines having or overlapping with the input factors (e.g. H3k9ac).

choice = 2 displays the distribution of percentage of cell lines having or overlapping with increasing number of factors for the selected group (e.g. WE or RE etc). If this option is selected, then a group name should be provided through choice2group.

Usage

descriptor(name, factors, groups,
choice = 1, choice2group = NULL, title = NULL,
groupinfo = NULL)

Arguments

name

Name of the "MultiAssayExperiment" object containing the assay data

factors

A vector containing the name of the Assays

groups

Name of all or subset of groups

choice

Numerical value between 1 and 2. See Description and vignettes for details of plots generated by this 2 choices

choice2group

Used when choice is 2. This argument allows a name of group.

title

Title of the plot

groupinfo

output of create_group() or similar object

Value

Displays various plots as per the combination of various input arguments.

Author(s)

Subhadeep Das <[email protected]>

Examples

descriptor(name = multi_assay,
factors = c(
"H2az","H3k9ac","H3k4me1"),
groups = c("WE","RE","IntE","NE"),
choice = 1,
title = "Distribution of percentage of cell types overlapping
with various factors",
groupinfo = groupinfo_ext)

extraction of projected cooordinates from "PCA" object.

Description

funtion to extract projected coordinates of individuals on principal components.

Usage

extract(name, groups, integrated = TRUE, Assay = NULL,
rand = NULL, PC = c(1,2,3,4), groupinfo = NULL)

Arguments

name

name of the OMICs object.

groups

one or more groups created by or supplied through "create_group()"

integrated

whether to extract coordinates from integrated or individual assay.

Assay

name of the assay if integrated = FALSE

rand

number of rows to be extracted randomly.

PC

principal component numbers. The projected informaton on this PCs will be extracted.

groupinfo

same as integrate_variables()

Value

a data frame of the extracted value.

Author(s)

Subhadeep Das

Examples

exclude <- list(0,c(1,9))

int_PCA <- integrate_pca(Assays = c("H2az",
"H3k9ac"),
groupinfo = groupinfo,
name = multi_assay, mergetype = 2,
exclude = exclude, graph = FALSE)

name = int_PCA$int_PCA

data <- extract(name = name, PC = c(1:4),
groups = c("WE","RE"), integrated = TRUE, rand = 600,
groupinfo = groupinfo_ext)

Data extraction from "MultiAssayExperiment" object

Description

Extraction of individuals according to user supplied group and Assay

Usage

extract_assay(name, Assay, groups, groupinfo = NULL,
addgroupnames = TRUE)

Arguments

name

name of the "OMICs" object from which data is to be retrieved.

Assay

name of the "Assay" from which data is to be retrieved.

groups

name of the "groups" from which data is to be retrieved.

groupinfo

output of create_group() or similar object

addgroupnames

logical argument. If "TRUE", adds the group membership info of returned individuals

Value

a data frame containing the intended information

Author(s)

Subhadeep Das

Examples

x <- extract_assay(name = multi_assay, Assay = "H2az",
groups = c("WE","RE"), groupinfo = groupinfo_ext)

x

Integration of multiple Assays into linear combinations by PCA.

Description

This function integrates multiple assays read by InputOMICs() into linear combinations using PCA. Following earlier analyses, user may want to exclude some variables/columns/cell lines which should be supplied through the "exclude" argument (see vignettes for more explanation)

PCA is done by the function PCA() from package "FactoMineR". Additional arguments may be supplied to PCA() through "..." .

Usage

integrate_pca(Assays, name, exclude, mergetype = 1,
groupinfo = NULL, ...)

Arguments

Assays

A vector of names of an Assays read by "InputOMICs()"

name

Name of the "MultiAssayExperiment" object containing the assay data

exclude

A list that indicates which columns to exclude. See vignettes for details

mergetype

2 options : 

1 = subset of groups should be entered by user and PCA is run
on this subset to integrate Assays into PCs

2 = PCA is run on all groups
groupinfo

same as plot_density()

...

aditional arguments supplied to function "PCA()" from package "FactoMineR"

Details

The "mergetype" argument lets user decide whether to combine all groups or a subset of groups created by "create_group". If mergetype is set to 1, "integrateAssays" asks for the name of groups which should be a charatcer or charagter vector (see vignettes for more details).

Value

"integrateAssays()" returns a list containing the start and end column of each Assay as ordered in the "Assays" argument.

Author(s)

Subhadeep Das <[email protected]>

Examples

int_PCA <- integrate_pca(Assays = c("H2az",
"H3k9ac"), name = multi_assay, mergetype = 2,
exclude = list(0,c(1,9)), graph = FALSE,
groupinfo = groupinfo_ext)

Integration of an experiment/ Assay done on many Cell lines/ time points into linear combinations by PCA.

Description

If an assay (e.g. ChIP-seq for the histone modification H3k9ac) is done on multiple cells/conditions/treatment/time, it might be, sometimes, necessary to integrate or combine them. Such combination may be done by several techniques like Principal Component Analysis (PCA) or Factor Analysis (FA). This function integrates an assay experimented on multiple cells or conditions into many linear combinations using PCA.

Usage

integrate_variables(Assays, name, groups, groupinfo = NULL, ...)

Arguments

Assays

A vector of names of an assays

name

Name of the "MultiAssayExperiment" object containing the assay data

groups

Name of all or subset of groups

groupinfo

output of create_group or similar object containing group information

...

Additional arguments supplied to "PCA()" imported from "FactoMineR"

Value

an object of class "PCA"

Author(s)

Subhadeep Das <[email protected]>

Examples

PCAlist <- integrate_variables(Assays = c("H2az","H3k4me1",
"H3k9ac"), name = multi_assay,
groups = c("WE","RE"), groupinfo = groupinfo_ext,
scale.unit = FALSE, graph = FALSE)

support function of prepareDataset

Description

intersects a factor file and an annotation file

Usage

intersect(fact, anno, ...)

Arguments

fact

Full path of the directory containing only the bed (.bed) files corresponding to several cell lines of the specified factor.

anno

Full path of the annotation file (e.g. gene, TSS, exon etc.)

...

additional arguments received from "prepareDataset()" and suppliet to "R_bedtools_intersect()"

Value

A list containing the intersections of the files (paths of which are supplied throgh the arguments).

Author(s)

Subhadeep Das

Examples

anno <- system.file("extdata/annotation2/TSS_groups.bed",
package = "OMICsPCAdata")

fact <- system.file("extdata/factors2/demofactor",
package = "OMICsPCAdata")

Cells <- intersect(fact = fact, anno = anno)

Cells

Storage of intersected factors from multiple cell types as columns into a dataframe

Description

This function takes the output list of the function "intersect" and stores the intersected value of the factor and annotation files into a dataframe. The values corrsponding to the entries in annotation file represents the corresponding value of in each cell line. If an entry is not found in a factor file, its corresponding value will be 0.

Usage

merge_cells(list, Cells)

Arguments

list

Full path of the file containing name of the Annotations (e.g. gene) in a single column

Cells

output list of function "integrate()"

Value

A dataframe wich rows corresponding to each entry in annotation file and columns corresponding to the intersected value with the corresponding annotation in each cell line.

Author(s)

Subhadeep Das

Examples

anno <- system.file("extdata/annotation2/TSS_groups.bed",
package = "OMICsPCAdata")

fact <- system.file("extdata/factors2/demofactor",
package = "OMICsPCAdata")

Cells <- intersect(fact = fact, anno = anno)

list <- system.file("extdata/annotation2/TSS_list",
package = "OMICsPCAdata")

merged_Cells <- merge_cells(list = list, Cells = Cells)

head(merged_Cells)

Visualization of Density of various groups on Principal components

Description

This function displays density of individuals

Usage

plot_density(name, Assay, PC = 1, groups,
groupinfo = NULL, ...)

Arguments

name

Name of the "PCA" object containing the assay data

Assay

Name of an Assay

PC

A number corresponding to the principal component on which density is to be calculated

groups

A vector of names or subset of names of groups

groupinfo

same as integrate_variables()

...

additional arguments of base function "geom_density" from package "ggplot2"

Value

a "gg" "ggplot" object

Author(s)

Subhadeep Das <[email protected]>

Examples

PCAlist <- integrate_variables(Assays = c("H2az","H3k4me1",
"H3k9ac"), name = multi_assay,
groups = c("WE","RE"), groupinfo = groupinfo_ext,
scale.unit = FALSE, graph = FALSE)

densityplot <- plot_density(name = PCAlist,
Assay = "H2az", groupinfo = groupinfo_ext,
PC = 1, groups = c("WE","RE"),
adjust = 1)

Visualization of 3D Density of various groups on Principal components

Description

This function displays density of individuals from various groups created by create_group(). This wraps "persp" from package "graphics" and thus takes additional graphical input shown in example.

Usage

plot_density_3D(name, Assay, group, PC1 = 1,
PC2 = 2, static = FALSE,
gridsize = 100, groupinfo = NULL, ...)

Arguments

name

Name of the "PCA" object containing the assay data

Assay

Name of an Assay

group

Names of a group

PC1, PC2

Numbers corresponding to the principal components on which density is to be calculated

static

Logical if TRUE a static plot is generated. default = FALSE

gridsize

A number used in kernel smoothing. default is 100

groupinfo

Same as integrate_variables()

...

additional arguments passed to base function "persp" from package "graphics"

Details

2D density is calculated using the "kde2d"" function from package "MASS"" which use kernel density estimation (KDE) to calculate density of 2D data. If the variance on either or both of the PCs are 0, the KDE can't be calculated.

Value

Displays 3D density plots of PCs

Author(s)

Subhadeep Das <[email protected]>

Examples

PCAlist <- integrate_variables(Assays = c("H2az"),
name = multi_assay,
groups = c("WE","RE"), groupinfo = groupinfo_ext,
scale.unit = FALSE, graph = FALSE)

plot_density_3D(name = PCAlist, Assay = "H2az",
group = "WE", PC1 = 1, PC2 = 2, grid_size = 100,
static = FALSE, groupinfo = groupinfo_ext)

Visualization of Density of various groups on Principal components of integrated assays

Description

This function works similarly as "plot_density()". See the man page of "plot_density()" for details.

Usage

plot_integrated_density(name, PC = 1, groups,
groupinfo = NULL, ...)

Arguments

name

Name of the integrated "PCA"" object

PC

A number corresponding to the principal component on which density is to be calculated. default = 1

groups

A vector of names or subset of names of groups

groupinfo

same as plot_density()

...

additional arguments allowed to base function "geom_density" of package "ggplot2"

Value

a "gg" "ggplot" object

Author(s)

Subhadeep Das <[email protected]>

Examples

exclude <- list(0,c(1,9))

int_PCA <- integrate_pca(Assays = c("H2az",
"H3k9ac"), name = multi_assay, mergetype = 2,
exclude = exclude, groupinfo = groupinfo_ext,
,graph = FALSE)

name = int_PCA$int_PCA

densityplot <- plot_integrated_density(name = name, PC = 1,
groups = c("WE","RE","IntE","NE"), groupinfo = groupinfo_ext)

# additional graphical functions (e.g. xlim, ylim, theme) may be
#added with densityplot (see section VIII. Density analysis)

densityplot

Visualization of 3D Density of various groups on Principal components of multiple Assays

Description

works similarly as "plot_density_3D" . See vignettes and man page of "plot_density_3D" for more details.

Usage

plot_integrated_density_3D(name, PC1 = 1, PC2 = 2, group,
gridsize = 100, static = FALSE, groupinfo = NULL, ...)

Arguments

name

Name of the integrated "PCA" object

PC1, PC2

Numbers corresponding to the principal components on which density is to be calculated

group

Names of a group

gridsize

A number used in kernel smoothing. default is 100

static

Logical if TRUE a static plot is generated. default = FALSE

groupinfo

same as integrate_variables()

...

additional arguments allowed to base function "persp" of package "graphics"

Details

2D density is calculated using the "kde2d"" function from package "MASS"" which use kernel density estimation (KDE) to calculate density of 2D data. If the variance on either or both of the PCs are 0, the KDE can't be calculated.

Value

Displays 3D density plots.

Author(s)

Subhadeep Das <[email protected]>

Examples

exclude <- list(0,c(1,9))

int_PCA <- integrate_pca(Assays = c("H2az",
"H3k9ac"), name = multi_assay, mergetype = 2,
exclude = exclude, groupinfo = groupinfo_ext,
,graph = FALSE)

name = int_PCA$int_PCA


plot_integrated_density_3D(name = name, PC1 = 1, PC2 = 2,
group = c("WE","RE"), gridsize = 100, static = FALSE,
groupinfo = groupinfo_ext)

Intersects and merge data from multiple experiments into a dataframe

Description

This function intersects feature files (e.g. ChIP-seq, RNA seq) with annotation files(e.g.exons, genes) and represents the intersected values of each annotation from all cell lines in a data frame.

Usage

prepare_dataset(factdir, annofile, annolist, ...)

Arguments

factdir

Full path of directory containing (only) feature (e.g ChIP-seq, CAGE) files in bed format

annofile

Full path of the annotation file (e.g. TSS, exon, gene) in bed format

annolist

Full path of the file containing name of the annotations (e.g. name of exons)

...

additional arguments to pass to "R_bedtools_intersect()" through "intersect()"

Details

If an annotation (e.g. gene) does not present in an assay file (e.g ChIP-seq of H3k9ac in the Cell Gm12878), this function puts a 0 as its value.

Value

a dataframe of intersected values.

Author(s)

Subhadeep Das <[email protected]>

Examples

anno <- system.file("extdata/annotation2/TSS_groups.bed",
                    package = "OMICsPCAdata")

list <- system.file("extdata/annotation2/TSS_list",
                    package = "OMICsPCAdata")

fact <- system.file("extdata/factors2/demofactor",
                    package = "OMICsPCAdata")

prepare_dataset(factdir = fact, annofile = anno, annolist = list)