Type:	Package
Title:	Entropy Based Method for the Detection of Significant Variation in Gene Expression Data
Version:	1.2.3
Depends:	R (≥ 2.10)
Date:	2022-04-05
Maintainer:	Federico Zambelli <federico.zambelli@unimi.it>
Description:	An implementation of a method based on information theory devised for the identification of genes showing a significant variation of expression across multiple conditions. Given expression estimates from any number of RNA-Seq samples and conditions it identifies genes or transcripts with a significant variation of expression across all the conditions studied, together with the samples in which they are over- or under-expressed. Zambelli et al. (2018) <doi:10.1093/nar/gky055>.
License:	GPL-3
RoxygenNote:	6.0.1
NeedsCompilation:	no
Packaged:	2022-04-13 08:21:36 UTC; federicozambelli
Author:	Federico Zambelli [cre], Giulio Pavesi [aut]
Repository:	CRAN
Date/Publication:	2022-04-13 09:52:35 UTC

Entropy Based Method for the Detection of Significant Variation in Gene Expression Data

Description

An implementation of a method based on information theory devised for the identification of genes showing a significant variation of expression across multiple conditions. Given expression estimates from any number of RNA-Seq samples and conditions it identifies genes or transcripts with a significant variation of expression across all the conditions studied, together with the samples in which they are over- or under-expressed. Zambelli et al. (2018) <doi:10.1093/nar/gky055>.

Author(s)

NA

Maintainer: Federico Zambelli <federico.zambelli@unimi.it>

References

doi = 10.1093/nar/gky055 doi = 10.1007/978-1-0716-1307-8_6

Examples

#load expression values and experiment design
data("RN_Brain_Example_tpm", "RN_Brain_Example_design")
#compute statistics and p-values (considering only a subset of genes due to
#examples running time limit of CRAN). 
Results <- RN_calc(RN_Brain_Example_tpm[1:10000,], RN_Brain_Example_design)
#select only genes with significant changes of expression
Results <- RN_select(Results)
#Compute the Point Mutual information Matrix
Results <- RN_pmi(Results)

#load expression values and experiment design
data("RN_BarresLab_FPKM", "RN_BarresLab_design")
#compute statistics and p-values (considering only a subset of genes due to
#examples running time limit of CRAN)
Results_B <- RN_calc(RN_BarresLab_FPKM[1:10000,], RN_BarresLab_design)
#select only genes with significant changes of expression
Results_B <- RN_select(Results_B)
#Compute the Point Mutual information matrix
Results_B <- RN_pmi(Results_B)

RN_BarresLab_FPKM

Description

Data expression values (mean FPKM) for different cerebral cortex cell from Zhang et al: "An RNA-Seq transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex", J Neurosci 2014 Sep. RN_BarresLab_design is the corresponding design matrix.

Usage

data("RN_BarresLab_FPKM")

Format

A data frame with 12978 observations on the following 7 variables.

Astrocyte

a numeric vector

Neuron

a numeric vector

OPC

a numeric vector

NFO

a numeric vector

MO

a numeric vector

Microglia

a numeric vector

Endothelial

a numeric vector

Source

Zhang Y, Chen K, Sloan SA, Bennett ML, Scholze AR, O'Keeffe S, Phatnani HP, Guarnieri P, Caneda C, Ruderisch N, Deng S, Liddelow SA, Zhang C, Daneman R, Maniatis T, Barres BA, Wu JQ. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J Neurosci. 2014 Sep 3;34(36):11929-47. doi: 10.1523/JNEUROSCI.1860-14.2014. Erratum in: J Neurosci. 2015 Jan 14;35(2):846-6. PubMed PMID: 25186741; PubMed Central PMCID: PMC4152602.

Data were downloaded from https://web.stanford.edu/group/barres_lab/brain_rnaseq.html

Examples

data(RN_BarresLab_FPKM)

RN_BarresLab_design

Description

The experiment design matrix for RN_BarresLab_design

Usage

data("RN_Brain_Example_design")

Format

The format is: num[1:9,1:3]

Details

A binary matrix where conditions correspond to columns and samples to rows. In this example there are seven conditions (cell types) and seven samples with the expression measured by mean FPKM.

Examples

data(RN_Brain_Example_design)

RN_Brain_Example_design

Description

The example design matrix for RN_Brain_Example_tpm

Usage

data("RN_Brain_Example_design")

Format

The format is: num[1:9,1:3]

Details

A binary matrix where conditions correspond to the columns and samples to the rows. In this example there are three conditions (individuals) and nine samples, with three replicates for each condition.

Examples

data(RN_Brain_Example_design)

RN_Brain_Example_tpm

Description

Example data expression values from brain of three different individuals, each with three replicates. RN_Brain_Example_design is the corresponding design matrix.

Usage

data("RN_Brain_Example_tpm")

Format

A data frame with 78699 observations on the following 9 variables.

GENE

a factor vector

BRAIN_2_1

a numeric vector

BRAIN_2_2

a numeric vector

BRAIN_2_3

a numeric vector

BRAIN_3_1

a numeric vector

BRAIN_3_2

a numeric vector

BRAIN_3_3

a numeric vector

BRAIN_1_1

a numeric vector

BRAIN_1_2

a numeric vector

BRAIN_1_3

a numeric vector

Examples

data(RN_Brain_Example_tpm)

RN_calc

Description

Computes both global and local p-values, and returns the results in a list containing for each gene the original expression values and the associated global and local p-values (as -log10(p-value)).

Usage

RN_calc(X, design)

Arguments

Value

Author(s)

Giulio Pavesi - Dep. of Biosciences, University of Milan

Federico Zambelli - Dep. of Biosciences, University of Milan

Examples

data("RN_Brain_Example_tpm", "RN_Brain_Example_design")
#compute statistics and p-values (considering only a subset of genes due to
#examples running time limit of CRAN)
Results <- RN_calc(RN_Brain_Example_tpm[1:10000,], RN_Brain_Example_design)

## The function is currently defined as

function(X, design = NULL)
{
  if(is.null(design))
  {
    design <- .RN_default_design(sum(sapply(X, is.numeric)))
  }
  
	Results <- list(expr = X, design = design)

	GPV <- RN_calc_GPV(X, bind = FALSE)
	LPV <- RN_calc_LPV(X, design = design, bind = FALSE)	

	TABLE = cbind(X,'---',GPV,'---',LPV)	

	Results$gpv <- GPV
	Results$lpv <- LPV
	Results$c_like <- TABLE
	Results$res <- cbind(X, GPV, LPV)
	
	return(Results)
}

RN_calc_GPV

Description

This function calculates global p-values from expression data, represented as -log10(p-values).

Usage

RN_calc_GPV(X, bind = TRUE)

Arguments

Value

If bind is TRUE the function returns a data.frame with the original expression values from 'X' and an attached column with the -log10() of the global p-value, otherwise only the numeric vector of -log10(p-values) is returned.

Author(s)

Giulio Pavesi - Dep. of Biosciences, University of Milan

Federico Zambelli - Dep. of Biosciences, University of Milan

Examples

data("RN_Brain_Example_tpm")
GPV <- RN_calc_GPV(RN_Brain_Example_tpm)

## The function is currently defined as
function (X, bind = TRUE) 
{
    rnums <- sapply(X, is.numeric)
    GL_LPV <- apply(X[rnums], 1, ".RN_calc_GPV_row")
    if (bind) {
        GPV <- cbind(X, GL_LPV)
        return(GPV)
    }
    else {
        return(GL_LPV)
    }
  }

RN_calc_LPV

Description

This function calculates local p-values from expression data, output as -log10(p-value).

Usage

RN_calc_LPV(X, design, bind = TRUE)

Arguments

Value

If bind is TRUE the function returns a data.frame with the original expression values from data.frame X and attached columns with the -log10(p-values) computed for each sample, otherwise only the matrix of -log10(p-values) is returned.

Author(s)

Giulio Pavesi - Dep. of Biosciences, University of Milan

Federico Zambelli - Dep. of Biosciences, University of Milan

Examples

data("RN_Brain_Example_tpm", "RN_Brain_Example_design")
LPV <- RN_calc_LPV(RN_Brain_Example_tpm, RN_Brain_Example_design)
## The function is currently defined as

function(X, design = NULL, bind = TRUE)
{
  if(is.null(design))
  {
    design <- .RN_default_design(sum(sapply(X, is.numeric)))
  }
  
	rnums <- sapply(X, is.numeric)

	.RN_design_check(X, design, rnums)
	RL <- .RN_get_replicate_list(design)
	
	l1 <- rep("LOC_LPV", length(X[rnums]))
	l2 <- colnames(X[rnums])
	PV <- apply(X[rnums],  1, '.RN_calc_LPV_row', RL = RL)
	PV <- t(PV)
	colnames(PV) <- paste(l1, l2, sep="_")

	if(bind)
	{
		return (cbind(X, PV))
	}
	else
		return (PV)
}

Compute point mutual information matrix for the experimental conditions.

Description

Compute point mutual information for experimental conditions from the overxexpressed genes identified by RN_select.

Usage

RN_pmi(Results)

Arguments

Value

The original input containing

And two new matrices:

Author(s)

Giulio Pavesi - Dep. of Biosciences, University of Milan

Federico Zambelli - Dep. of Biosciences, University of Milan

Examples

data("RN_Brain_Example_tpm", "RN_Brain_Example_design")
#compute statistics and p-values (considering only a subset of genes due to
#examples running time limit of CRAN)
Results <- RN_calc(RN_Brain_Example_tpm[1:10000,], RN_Brain_Example_design)
Results <- RN_select(Results)
Results <- RN_pmi(Results)

## The function is currently defined as
RN_pmi <- function(Results)
{
  if(is.null(Results$selected))
    Results <- RN_select(Results)
  
  Results$pmi <- matrix(nrow = ncol(Results$design), 
                        ncol = ncol(Results$design))
  
  colnames(Results$pmi) <- colnames(Results$design)
  rownames(Results$pmi) <- colnames(Results$design)
  
  Results$npmi <- Results$pmi
  
  colshift <- ncol(Results$selected) - ncol(Results$design) 
  
  for(x in 1:nrow(Results$pmi))
  {
    for(y in 1:nrow(Results$pmi))
    {
      if(x > y)
      {
        Results$pmi[x,y] <- Results$pmi[y,x]
        Results$npmi[x,y] <- Results$npmi[y,x]
        next
      } else
      {
        sum_x <- sum(Results$selected[,x+colshift] == 1, na.rm = TRUE)
        sum_y <- sum(Results$selected[,y+colshift] == 1, na.rm = TRUE)
        sum_xy <- sum(Results$selected[,x+colshift] == 1 & 
                        Results$selected[,y+colshift] == 1, na.rm = TRUE)
        freq_x <- sum_x / nrow(Results$selected)
        freq_y <- sum_y / nrow(Results$selected)
        freq_xy <- sum_xy / nrow(Results$selected)
        
        h_xy <- log2(1/freq_xy)
        
        Results$pmi[x,y] <- log2(freq_xy / (freq_x * freq_y))
        Results$npmi[x,y] <- Results$pmi[x,y] / h_xy
      }
    }
  }
  
  return (Results)
  }

Select transcripts/genes with significant p-values.

Description

Select transcripts with global p-value lower than an user defined threshold and provide a summary of over- or under-expression according to local p-values.

Usage

RN_select(Results, gpv_t = 0.01, lpv_t = 0.01, method = "BH")

Arguments

Value

The original input containing

and a new dataframe

Author(s)

Giulio Pavesi - Dep. of Biosciences, University of Milan

Federico Zambelli - Dep. of Biosciences, University of Milan

Examples

data("RN_Brain_Example_tpm", "RN_Brain_Example_design")
#compute statistics and p-values (considering only a subset of genes due to
#examples running time limit of CRAN)
Results <- RN_calc(RN_Brain_Example_tpm[1:10000,], RN_Brain_Example_design)
Results <- RN_select(Results)

## The function is currently defined as
function (Results, gpv_t = 0.01, lpv_t = 0.01, method = "BH") 
{
    lpv_t <- -log10(lpv_t)
    gpv_t <- -log10(gpv_t)
    Results$gpv_bh <- -log10(p.adjust(10^-Results$gpv, method = method))
    true_rows <- (Results$gpv_bh >= gpv_t)
    design_b <- t(Results$design > 0)
    Results$lpv_sel <- data.frame(row.names = rownames(Results$lpv)[true_rows])
    for (d in seq_along(design_b[, 1])) {
        col <- apply(Results$lpv[true_rows, ], 1, ".RN_select_lpv_row", 
            design_b[d, ], lpv_t)
        Results$lpv_sel <- cbind(Results$lpv_sel, col)
        colnames(Results$lpv_sel)[length(Results$lpv_sel)] <- paste("condition", 
            d, sep = "_")
    }
    lbl <- Results$res[, !sapply(Results$res, is.numeric)]
    Results$selected <- cbind(lbl[true_rows], Results$gpv[true_rows], 
	Results$gpv_bh[true_rows], Results$lpv_sel)
    colnames(Results$selected) <- c(names(which(!sapply(Results$res, 
	is.numeric))), "GL_LPV", "Corr. GL_LPV", colnames(Results$lpv_sel))
    Results$selected <- Results$selected[order(Results$selected[,3], decreasing=TRUE),]
    Results$lpv_sel <- NULL
    return(Results)
  }

RNentropy

Description

This function runs RNentropy on a file containing normalized expression values.

Usage

RNentropy(file, tr.col, design, header = TRUE, skip = 0, skip.col = NULL, 
col.names = NULL)

Arguments

Value

Refer to the help for RN_calc.

Author(s)

Giulio Pavesi - Dep. of Biosciences, University of Milan

Federico Zambelli - Dep. of Biosciences, University of Milan

Examples

#load expression values and experiment design
data("RN_Brain_Example_tpm", "RN_Brain_Example_design")
#compute statistics and p-values (considering only a subset of genes due to
#examples running time limit of CRAN) 
Results <- RN_calc(RN_Brain_Example_tpm[1:10000,], RN_Brain_Example_design)
#select only genes with significant changes of expression
Results <- RN_select(Results)


## The function is currently defined as
function (file, tr.col, design, header = TRUE, skip = 0, skip.col = NULL, 
    col.names = NULL) 
{
    TABLE <- read.table(file, row.names = tr.col, header = header, 
        skip = skip, blank.lines.skip = TRUE, comment.char = "#")
    if (!is.null(skip.col)) {
        TABLE <- .RN_delete_col(TABLE, tr.col, skip.col)
    }
    if (!is.null(col.names)) {
        colnames(TABLE) <- col.names
    }
    return(RN_calc(TABLE, design))
  }