Package 'Hiiragi2013'

Title: Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages
Description: This package contains the experimental data and a complete executable transcript (vignette) of the statistical analysis presented in the paper "Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages" by Y. Ohnishi, W. Huber, A. Tsumura, M. Kang, P. Xenopoulos, K. Kurimoto, A. K. Oles, M. J. Arauzo-Bravo, M. Saitou, A.-K. Hadjantonakis and T. Hiiragi; Nature Cell Biology (2014) 16(1): 27-37. doi: 10.1038/ncb2881."
Authors: Andrzej Oles [aut], Wolfgang Huber [aut, cre]
Maintainer: Wolfgang Huber <[email protected]>
License: Artistic-2.0
Version: 1.48.1
Built: 2026-10-10 11:09:20 UTC
Source: https://github.com/bioc/Hiiragi2013

Help Index

Find Differentially Expressed Genes

Description

Returns differentially expressed genes between two conditions.

Usage

getDifferentialExpressedGenes(x, groups, g1, g2, theta = 0.5, FDRcutoff = 0.05)

Arguments

x

an ExpressionSet containing a matrix of expression values with rows representing features and columns samples
groups

list of integer vectors specifying the grouping of samples
g1

character string specifying the name of the first tested group from groups
g2

character string specifying the name of the second tested group from groups
theta

numeric, probability with values in [0,1] used for quantile filtering of variance
FDRcutoff

numeric, p-value cutoff

Details

Differentially expressed features are selected based on a t-test with the adjusted p-value cutoff specified by FDRcutoff.

The filtering selects only features whose variance is greater than the quantile defined by the probability theta.

Value

Integer vector containing indices of differentially expressed features from x.

Author(s)

Wolfgang Huber, 2013

Examples

data(x)
groups = with(pData(x), list(
  "E3.25"       = which(genotype=="WT" & Embryonic.day=="E3.25"),
  "E3.5 (EPI)"  = which(genotype=="WT" & Embryonic.day=="E3.5" & lineage=="EPI"),
  "E3.5 (PE)"   = which(genotype=="WT" & Embryonic.day=="E3.5" & lineage=="PE")))
  
# get a list of differentially expressed genes along the transition from E3.25 to E3.5
de = union(
  getDifferentialExpressedGenes(x, groups, "E3.25", "E3.5 (EPI)"),
  getDifferentialExpressedGenes(x, groups, "E3.25", "E3.5 (PE)"))
fData(x[de,])$symbol

Multidimensional Scaling Plots

Description

Visualization of data similarity using non-metric multidimensional scaling.

Usage

MDSplot(
  x, 
  mask, 
  flip = integer(0), 
  rotation = 0, 
  cex = 2, 
  col = x$sampleColour, 
  panellabel, 
  pointlabel
)

Arguments

x

an ExpressionSet containing a matrix of expression values with rows representing features and columns samples
mask

a logical vector specifying the masking of data points; if missing all points are drawn
flip

an integer vector containing indices of columns of the distance matrix whose sign should be flipped
rotation

numeric, angle in radians by which the plot should be rotated
cex

numeric, the value giving the amount by which text and symbols should be scaled relative to the current setting
col

character vector specifing the colors of points
panellabel

character, the label of the figure panel
pointlabel

character vector containing labels corresponding to samples; if specified labels are drawn instead of points

Value

The result of the function is a plot.

Author(s)

Wolfgang Huber, 2013

Examples

## data preparation
data(x)
y = x[, with(pData(x), Embryonic.day=="E3.25")]

## define colours for plotting
colours = setNames(RColorBrewer::brewer.pal(10, "Paired")[c(9, 7)], c("WT", "FGF4-KO"))[y$genotype]
stopifnot(!any(is.na(colours)))

## select 100 most variable genes for plotting
selMDS = order(genefilter::rowVars(exprs(y)), decreasing=TRUE)[seq_len(100)]
MDSplot(y[selMDS,], col=colours)

Plot Heatmaps

Description

Plots a heatmap with features (rows) and samples (columns) reordered according to hierarchical clustering.

Usage

myHeatmap(x, collapseDuplicateFeatures = FALSE, haveColDend = FALSE)

Arguments

x

an ExpressionSet containing a matrix of expression values with rows representing features and columns samples
collapseDuplicateFeatures

logical, if TRUE multiple features per gene will be averaged
haveColDend

logical, if TRUE displays the grouping of samples using a dendrogram

Value

The result of the function is a plot.

Author(s)

Wolfgang Huber, 2013

See Also

myHeatmap2

Examples

data(x)
groups = with(pData(x), list(
  "E3.25"       = which(genotype=="WT" & Embryonic.day=="E3.25"),
  "E3.5 (EPI)"  = which(genotype=="WT" & Embryonic.day=="E3.5" & lineage=="EPI"),
  "E3.5 (PE)"   = which(genotype=="WT" & Embryonic.day=="E3.5" & lineage=="PE")))
samples = unlist(groups)

# heatmap of differentially expressed genes along the transition from E3.25 to E3.5
de = union(
  getDifferentialExpressedGenes(x, groups, "E3.25", "E3.5 (EPI)"),
  getDifferentialExpressedGenes(x, groups, "E3.25", "E3.5 (PE)"))
myHeatmap(x[de, samples], collapseDuplicateFeatures=TRUE)

Plot Heatmaps

Description

Plots a heatmap with clustering of rows and columns specified by the rowGroups and colGroups, respectively.

Usage

myHeatmap2(
  x, 
  rowGroups = factor(rep(1, nrow(x))), 
  colGroups = factor(rep(1, ncol(x))), 
  keeprownames = TRUE, 
  colors = colorRampPalette(brewer.pal(9, "Blues")[-1])(100), 
  ...
)

Arguments

x

an ExpressionSet containing a matrix of expression values with rows representing features and columns samples
rowGroups

factor which length aligns with rows of x
colGroups

factor which length aligns with columns of x
keeprownames

logical, if TRUE displays row names
colors

color palette used to plot the heatmap
...

arguments passed to the internal ordermat function

Value

The result of the function is a plot.

Author(s)

Wolfgang Huber, 2013

See Also

myHeatmap

Examples

## The function is currently defined as
function (x, rowGroups = factor(rep(1, nrow(x))), colGroups = factor(rep(1, ncol(x))),
    keeprownames = TRUE, colors = colorRampPalette(brewer.pal(9, "Blues")[-1])(100), ...) {
    x = ordermat(x, rowGroups, ...)
    x = ordermat(t(x), colGroups, ...)
    if (!keeprownames) 
        colnames(x) = NULL
    print(levelplot(x, aspect = "fill", xlab = "", ylab = "", 
        scales = list(x = list(rot = 90), raster = TRUE), col.regions = colors, 
        colorkey = list(space = "left", height = 0.15, useRaster = TRUE)))
  }

Clustering of Most Variable Genes

Description

The function pamCluster selects the ngenes most variable genes and performs their clustering using the partitioning around medoids method pam.

Usage

pamCluster(ngenes, x, k = 2)

Arguments

ngenes

numeric, the number of most variable genes to select
x

ExpressionSet containing gene expression values
k

positive integer specifying the number of clusters

Value

Integer vector specifying the clustering.

Author(s)

Wolfgang Huber

See Also

pam

Examples

data("x")
y = x[, x$Embryonic.day=="E3.5"]

## perform the clustering 
pc = pamCluster(50, y, k=3)

## display clustering vs. sample lineage
plot(as.factor(pData(y)$lineage), pc, yaxt="n", xlab="lineage", ylab="cluster")

Overview of the Sample Expression

Description

Plots a projection of sample expression profiles on the differential expression signature.

Usage

plotProjection(projection, label, col, colourMap)

Arguments

projection

a vector of scalar products between each sample's expression profile and differential expression signature
label

character vector of sample names
col

sample colour palette
colourMap

sample colour map

Value

The result of the function is a plot.

Author(s)

Wolfgang Huber, 2013

Examples

## For illustration of use please see the package vignette

Normalized Microarray Data

Description

An ExpressionSet object containing the RMA normalized dataset in the assayData and annotation in the phenoData.

Usage

data(x)

Format

Formal class 'ExpressionSet' [package "Biobase"] with 7 slots
  ..@ experimentData   :Formal class 'MIAME' [package "Biobase"] with 13 slots
  .. .. ..@ name             : chr ""
  .. .. ..@ lab              : chr ""
  .. .. ..@ contact          : chr ""
  .. .. ..@ title            : chr ""
  .. .. ..@ abstract         : chr ""
  .. .. ..@ url              : chr ""
  .. .. ..@ pubMedIds        : chr ""
  .. .. ..@ samples          : list()
  .. .. ..@ hybridizations   : list()
  .. .. ..@ normControls     : list()
  .. .. ..@ preprocessing    :List of 2
  .. .. .. ..$ filenames  : chr [1:101] "/tmp/RtmpI5T6yy/1_C32_IN.CEL" "/tmp/RtmpI5T6yy/2_C32_IN.CEL" "/tmp/RtmpI5T6yy/3_C32_IN.CEL" "/tmp/RtmpI5T6yy/4_C32_IN.CEL" ...
  .. .. .. ..$ affyversion: chr NA
  .. .. ..@ other            :List of 1
  .. .. .. ..$ : chr ""
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. .. .. ..@ .Data:List of 2
  .. .. .. .. .. ..$ : int [1:3] 1 0 0
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ assayData        :<environment: 0x167aa270> 
  ..@ phenoData        :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots
  .. .. ..@ varMetadata      :'data.frame':  5 obs. of  1 variable:
  .. .. .. ..$ labelDescription: chr [1:5] NA NA NA NA ...
  .. .. ..@ data             :'data.frame':	101 obs. of  5 variables:
  .. .. .. ..$ File.name            : chr [1:101] "1_C32_IN" "2_C32_IN" "3_C32_IN" "4_C32_IN" ...
  .. .. .. ..$ Embryonic.day        : Factor w/ 3 levels "E3.25","E3.5",..: 1 1 1 1 1 1 1 1 1 1 ...
  .. .. .. ..$ Total.number.of.cells: Factor w/ 11 levels "32","33","34",..: 1 1 1 1 1 1 1 1 1 1 ...
  .. .. .. ..$ lineage              : chr [1:101] "" "" "" "" ...
  .. .. .. ..$ genotype             : Factor w/ 2 levels "FGF4-KO","WT": 2 2 2 2 2 2 2 2 2 2 ...
  .. .. ..@ dimLabels        : chr [1:2] "sampleNames" "sampleColumns"
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. .. .. ..@ .Data:List of 1
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ featureData      :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots
  .. .. ..@ varMetadata      :'data.frame':	3 obs. of  1 variable:
  .. .. .. ..$ labelDescription: chr [1:3] NA NA NA
  .. .. ..@ data             :'data.frame':	45101 obs. of  3 variables:
  .. .. .. ..$ symbol  : chr [1:45101] "Copg1" "Atp6v0d1" "Golga7" "Psph" ...
  .. .. .. ..$ genename: chr [1:45101] "coatomer protein complex, subunit gamma 1" "ATPase, H+ transporting, lysosomal V0 subunit D1" "golgi autoantigen, golgin subfamily a, 7" "phosphoserine phosphatase" ...
  .. .. .. ..$ ensembl : chr [1:45101] "ENSMUSG00000030058" "ENSMUSG00000013160" "ENSMUSG00000015341" "ENSMUSG00000029446" ...
  .. .. ..@ dimLabels        : chr [1:2] "featureNames" "featureColumns"
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. .. .. ..@ .Data:List of 1
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ annotation       : chr "mouse4302"
  ..@ protocolData     :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots
  .. .. ..@ varMetadata      :'data.frame':	1 obs. of  1 variable:
  .. .. .. ..$ labelDescription: chr NA
  .. .. ..@ data             :'data.frame':	101 obs. of  1 variable:
  .. .. .. ..$ ScanDate: chr [1:101] "2011-03-16T04:33:05Z" "2011-03-16T04:42:32Z" "2011-03-16T04:51:54Z" "2011-03-16T05:19:47Z" ...
  .. .. ..@ dimLabels        : chr [1:2] "sampleNames" "sampleColumns"
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. .. .. ..@ .Data:List of 1
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. ..@ .Data:List of 4
  .. .. .. ..$ : int [1:3] 3 0 2
  .. .. .. ..$ : int [1:3] 2 22 0
  .. .. .. ..$ : int [1:3] 1 3 0
  .. .. .. ..$ : int [1:3] 1 0 0

References

Ohnishi et al., 2014

See Also

xq, xql

Examples

data(x)
x
pData(x)

qPCR Gene Expression

Description

An ExpressionSet object containing single-cell gene expression levels measured by qPCR.

Usage

data(xq)

Format

Formal class 'ExpressionSet' [package "Biobase"] with 7 slots
  ..@ experimentData   :Formal class 'MIAME' [package "Biobase"] with 13 slots
  .. .. ..@ name             : chr ""
  .. .. ..@ lab              : chr ""
  .. .. ..@ contact          : chr ""
  .. .. ..@ title            : chr ""
  .. .. ..@ abstract         : chr ""
  .. .. ..@ url              : chr ""
  .. .. ..@ pubMedIds        : chr ""
  .. .. ..@ samples          : list()
  .. .. ..@ hybridizations   : list()
  .. .. ..@ normControls     : list()
  .. .. ..@ preprocessing    : list()
  .. .. ..@ other            : list()
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. .. .. ..@ .Data:List of 2
  .. .. .. .. .. ..$ : int [1:3] 1 0 0
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ assayData        :<environment: 0xb12e938> 
  ..@ phenoData        :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots
  .. .. ..@ varMetadata      :'data.frame':  4 obs. of  1 variable:
  .. .. .. ..$ labelDescription: chr [1:4] NA NA NA NA
  .. .. ..@ data             :'data.frame':	137 obs. of  4 variables:
  .. .. .. ..$ Sample.ID    : chr [1:137] "34c_2" "34c_3" "34c_4" "34c_5" ...
  .. .. .. ..$ Embryonic.day: chr [1:137] "E3.25" "E3.25" "E3.25" "E3.25" ...
  .. .. .. ..$ Cell.type    : chr [1:137] "ICM" "ICM" "ICM" "ICM" ...
  .. .. .. ..$ sampleGroup  : chr [1:137] "E3.25" "E3.25" "E3.25" "E3.25" ...
  .. .. ..@ dimLabels        : chr [1:2] "sampleNames" "sampleColumns"
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. .. .. ..@ .Data:List of 1
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ featureData      :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots
  .. .. ..@ varMetadata      :'data.frame':	1 obs. of  1 variable:
  .. .. .. ..$ labelDescription: chr NA
  .. .. ..@ data             :'data.frame':	38 obs. of  1 variable:
  .. .. .. ..$ symbol: chr [1:38] "Fgf4" "Tom1l1" "Tdgf1" "Cldn4" ...
  .. .. ..@ dimLabels        : chr [1:2] "featureNames" "featureColumns"
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. .. .. ..@ .Data:List of 1
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ annotation       : chr "single cell qPCR"
  ..@ protocolData     :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots
  .. .. ..@ varMetadata      :'data.frame':	0 obs. of  1 variable:
  .. .. .. ..$ labelDescription: chr(0) 
  .. .. ..@ data             :'data.frame':	137 obs. of  0 variables
  .. .. ..@ dimLabels        : chr [1:2] "sampleNames" "sampleColumns"
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. .. .. ..@ .Data:List of 1
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. ..@ .Data:List of 4
  .. .. .. ..$ : int [1:3] 2 15 0
  .. .. .. ..$ : int [1:3] 2 16 0
  .. .. .. ..$ : int [1:3] 1 3 0
  .. .. .. ..$ : int [1:3] 1 0 0

References

Ohnishi et al., 2014

See Also

x, xql

Examples

data(xq)
xq
pData(xq)

Position-dependent Gene Expression

Description

An ExpressionSet object containing single-cell gene expression measured by qPCR, with cells facing the blastocyst cavity labelled fluorescently.

Usage

data(xql)

Format

Formal class 'ExpressionSet' [package "Biobase"] with 7 slots
  ..@ experimentData   :Formal class 'MIAME' [package "Biobase"] with 13 slots
  .. .. ..@ name             : chr ""
  .. .. ..@ lab              : chr ""
  .. .. ..@ contact          : chr ""
  .. .. ..@ title            : chr ""
  .. .. ..@ abstract         : chr ""
  .. .. ..@ url              : chr ""
  .. .. ..@ pubMedIds        : chr ""
  .. .. ..@ samples          : list()
  .. .. ..@ hybridizations   : list()
  .. .. ..@ normControls     : list()
  .. .. ..@ preprocessing    : list()
  .. .. ..@ other            : list()
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. .. .. ..@ .Data:List of 2
  .. .. .. .. .. ..$ : int [1:3] 1 0 0
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ assayData        :<environment: 0x7fe9c10> 
  ..@ phenoData        :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots
  .. .. ..@ varMetadata      :'data.frame':  2 obs. of  1 variable:
  .. .. .. ..$ labelDescription: chr [1:2] NA NA
  .. .. ..@ data             :'data.frame':	43 obs. of  2 variables:
  .. .. .. ..$ Embryonic.day: chr [1:43] "E4.5" "E4.5" "E4.5" "E4.5" ...
  .. .. .. ..$ Label        : chr [1:43] "High" "High" "Low" "Low" ...
  .. .. ..@ dimLabels        : chr [1:2] "sampleNames" "sampleColumns"
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. .. .. ..@ .Data:List of 1
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ featureData      :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots
  .. .. ..@ varMetadata      :'data.frame':	1 obs. of  1 variable:
  .. .. .. ..$ labelDescription: chr NA
  .. .. ..@ data             :'data.frame':	10 obs. of  1 variable:
  .. .. .. ..$ symbol: chr [1:10] "Fgf4" "Cubn" "Sox17" "Lama1" ...
  .. .. ..@ dimLabels        : chr [1:2] "featureNames" "featureColumns"
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. .. .. ..@ .Data:List of 1
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ annotation       : chr "single cell qPCR"
  ..@ protocolData     :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots
  .. .. ..@ varMetadata      :'data.frame':	0 obs. of  1 variable:
  .. .. .. ..$ labelDescription: chr(0) 
  .. .. ..@ data             :'data.frame':	43 obs. of  0 variables
  .. .. ..@ dimLabels        : chr [1:2] "sampleNames" "sampleColumns"
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. .. .. ..@ .Data:List of 1
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots
  .. .. ..@ .Data:List of 4
  .. .. .. ..$ : int [1:3] 3 0 1
  .. .. .. ..$ : int [1:3] 2 20 1
  .. .. .. ..$ : int [1:3] 1 3 0
  .. .. .. ..$ : int [1:3] 1 0 0

References

Ohnishi et al., 2014

See Also

x, xq

Examples

data(xql)
xql
pData(xql)