Plotting Graphcs and Heatmpas
=============================

Graphs
~~~~~~

It may be necessary to install first

.. code:: r

    install.packages("igraphdata", repos = "http://cran.r-project.org")

.. code:: python

    library(igraph)
    library(igraphdata)

.. code:: python

    data(macaque)

.. code:: python

    plot(macaque, layout=layout.auto, vertex.shape="circle",
         vertex.size=8, edge.arrow.size=0.5, vertex.label=NA)

.. image:: MiscGraphics_files/MiscGraphics_4_0.png
   :width: 600



Heatmap
~~~~~~~

.. code:: python

    heatmap(as.matrix(mtcars), Rowv = NA, Colv = NA, scale = "column")


.. image:: MiscGraphics_files/MiscGraphics_6_0.png
   :width: 600
	   
Heatmap with dendrogram
~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    heatmap(as.matrix(mtcars), scale = "column")

.. image:: MiscGraphics_files/MiscGraphics_8_0.png
   :width: 600
	   



Fancy heatmaps
~~~~~~~~~~~~~~

Also see `d3heatmaps <https://github.com/rstudio/d3heatmap>`__ if you
want an interactive heatmap. This does not seem to work within the
notebook but will work in RStudio or an R script.

.. code:: python

    library(pheatmap)

.. code:: python

    pheatmap(as.matrix(mtcars), scale = "column")



.. image:: MiscGraphics_files/MiscGraphics_11_0.png
   :width: 600


.. code:: python

    library(gplots)

.. code:: python

    heatmap.2(as.matrix(mtcars), scale = "column", col=redgreen)

.. image:: MiscGraphics_files/MiscGraphics_13_0.png
   :width: 600




Work!
^^^^^

Here we will try to replicate the noise discovery heatmap shown in the
statistics class.

.. code:: python

    suppressMessages(library(genefilter))

Perform noise discovery
~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    set.seed(123)
    
    n <- 20 # number of subjects
    m <- 20000 # number of genes 
    alpha <- 0.005 # significance level
    
    # create a matrix of gene expression values with m rows and 2*n columns
    M <- matrix(rnorm(2*n*m), m, 2*n)
    
    # give row and column names
    rownames(M) <- paste("G", 1:m, sep="")
    colnames(M) <- paste("id", 1:(2*n), sep="")
    
    # assign subjects inot equal sized groups
    grp <- factor(rep(0:1, c(n, n)))
    
    # calculate p-value using t-test for mean experession value of each gene
    pvals <- rowttests(M, grp)$p.value
    
    # extract the genes which meet the specified significance level
    hits <- M[pvals < alpha,]

-  Use pheatmap to plot a heatmap
-  Remove the row names (Google or use R's built-in help to figure out
   to do this)
-  Use this color palette to map expression values to a red-blakc-green
   scale

``colorRampPalette(c("red3", "black", "green3"))(50)``