Create significance versus gene expression ratio (fold change) scatter plot of microarray data
mavolcanoplot(
DataX, DataY,
PValues
)SigStructure
= mavolcanoplot(DataX, DataY, PValues
)... mavolcanoplot(..., 'Labels',
LabelsValue
,
...)... mavolcanoplot(..., 'LogTrans',
LogTransValue
,
...)... mavolcanoplot(..., 'PCutoff',
PCutoffValue
, ...)... mavolcanoplot(..., 'Foldchange',
FoldchangeValue
,
...)... mavolcanoplot(..., 'PlotOnly',
PlotOnlyValue
,
...)
DataX , DataY  DataMatrix object, matrix, or vector of gene expression values from a single experimental condition. If a DataMatrix object or a matrix, each row is a gene, each column is a sample, and an average expression value is calculated for each gene.
 
PValues  Either of the following:
 
LabelsValue  Cell array of labels (typically gene names or probe set
IDs) for the data. After creating the plot, you can click a data point
to display the label associated with it. If you do not provide a  
LogTransValue  Property to control the conversion of data in  
PCutoffValue  Lets you specify a cutoff pvalue to define data points
that are statistically significant. This value is displayed graphically
as a horizontal line on the plot. Default is
 
FoldchangeValue  Lets you specify a ratio fold change to define data points
that are differentially expressed. Default is
 
PlotOnlyValue  Controls the display of the volcano plot without user
interface components. Choices are

SigStructure  Structure containing information for genes that are considered to be both statistically significant (above the pvalue cutoff) and significantly differentially expressed (outside of the fold change values). The fields are listed below. 
creates a scatter plot
of gene expression data, plotting significance versus fold change
of gene expression ratios of two data sets, mavolcanoplot(
DataX, DataY,
PValues
)DataX
and DataY
.
It plots significance as the –log_{10} (pvalue)
from the input, PValues
. DataX
and DataY
can
be vectors, matrices, or DataMatrix objects. PValues
is
a clumn vector or DataMatrix
object.
returns a structure
containing information for genes that are considered to be both statistically
significant (above the pvalue cutoff) and significantly differentially
expressed (outside of the fold change values). The fields within SigStructure
= mavolcanoplot(DataX, DataY, PValues
)SigStructure
are
sorted by pvalue and include:
Name
PCutoff
FCThreshold
GeneLabels
PValues
FoldChanges
Note:
The fields 
... mavolcanoplot(..., '
defines optional properties that use property name/value
pairs in any order. These property name/value pairs are as follows:PropertyName
', PropertyValue
,
...)
lets you provide a cell array of labels
(typically gene names or probe set IDs) for the data. After creating
the plot, you can click a data point to display the label associated
with it. If you do not provide a ... mavolcanoplot(..., 'Labels',
LabelsValue
,
...)LabelsValue
,
data points are labeled with row numbers from DataX
and DataY
.
controls the conversion of data from ... mavolcanoplot(..., 'LogTrans',
LogTransValue
,
...)DataX
and DataY
to
log_{2} scale. When LogTransValue
is true
, mavolcanoplot
converts
data from natural to log_{2} scale. Default is false
,
which assumes the data is already log_{2} scale.
lets you specify a pvalue cutoff
to define data points that are statistically significant. This value
displays graphically as a horizontal line on the plot. Default is ... mavolcanoplot(..., 'PCutoff',
PCutoffValue
, ...)0.05
,
which is equivalent to 1.3010 on the –log_{10} (pvalue)
scale.
Note: You can also change the pvalue cutoff interactively after creating the plot. 
lets you specify a ratio fold change to
define data points that are differentially expressed. Fold changes
display graphically as two vertical lines on the plot. Default is ... mavolcanoplot(..., 'Foldchange',
FoldchangeValue
,
...)2
,
which corresponds to a ratio of 1 and –1 on a log_{2} (ratio)
scale.
Note: You can also change the fold change interactively after creating the plot. 
controls the display of the volcano plot
without user interface components. Choices are ... mavolcanoplot(..., 'PlotOnly',
PlotOnlyValue
,
...)true
or false
(default).
Note:
If you set the 
The volcano plot displays the following:
–log_{10} (pvalue) versus log_{2} (ratio) scatter plot of genes
Two vertical fold change lines at a fold change level
of 2, which corresponds to a ratio of 1 and –1 on a log_{2} (ratio)
scale. (Lines will be at different fold change levels, if you used
the 'Foldchange'
property.)
One horizontal line at the 0.05 pvalue level, which
is equivalent to 1.3010 on the –log_{10} (pvalue)
scale. (The line will be at a different pvalue level, if you used
the 'PCutoff'
property.)
Data points for genes that are considered both statistically significant (above the pvalue line) and differentially expressed (outside of the fold changes lines) appear in orange.
After you display the volcano scatter plot, you can interactively:
Adjust the vertical fold change lines by clickdragging one line or entering a value in the Fold Change text box.
Adjust the horizontal pvalue cutoff line by clickdragging or entering a value in the pvalue Cutoff text box.
Display labels for data points by clicking a data point.
Select a gene from the Up Regulated or Down Regulated list to highlight the corresponding data point in the plot. Press and hold Ctrl or Shift to select multiple genes.
Zoom the plot by selecting Tools > Zoom In or Tools > Zoom Out.
View lists of significantly upregulated and downregulated genes and their associated pvalues, and optionally, export the labels, pvalues, and fold changes to a structure in the MATLAB^{®} Workspace by clicking Export.
Load a MATfile, included with the Bioinformatics Toolbox™ software,
which contains Affymetrix^{®} data variables, including dependentData
and independentData
,
two matrices of gene expression values from two experimental conditions.
load prostatecancerexpdata
Use the mattest
function to calculate
pvalues for the gene expression values in the two matrices.
pvalues = mattest(dependentData, independentData);
Using the two matrices, the pvalues
calculated
by mattest
, and the probesetIDs
column
vector of labels provided, use mavolcanoplot
to
create a significance versus gene expression ratio scatter plot of
the microarray data from the two experimental conditions.
mavolcanoplot(dependentData, independentData, pvalues,... 'Labels', probesetIDs)
View the volcano plot without the user interface components.
mavolcanoplot(dependentData, independentData, pvalues,... 'Labels', probesetIDs,'Plotonly', true)
The prostatecancerexpdata.mat
file used in
the previous example contains data from Best et al., 2005.
[1] Cui, X., Churchill, G.A. (2003). Statistical tests for differential expression in cDNA microarray experiments. Genome Biology 4, 210.
[2] Best, C.J.M., Gillespie, J.W., Yi, Y., Chandramouli, G.V.R., Perlmutter, M.A., Gathright, Y., Erickson, H.S., Georgevich, L., Tangrea, M.A., Duray, P.H., Gonzalez, S., Velasco, A., Linehan, W.M., Matusik, R.J., Price, D.K., Figg, W.D., EmmertBuck, M.R., and Chuaqui, R.F. (2005). Molecular alterations in primary prostate cancer after androgen ablation therapy. Clinical Cancer Research 11, 6823–6834.