**COURSE USER'S GUIDE**

Statistical Analysis of Microarray Data using The MSCL Analyst\'s
Toolbox and JMP

Course given by CIT on November 3, 2004

Peter J. Munson and Jennifer J. Barb

Contact Info:

Peter Munson, PhD. Jennifer Barb, M.S.

Bldg 12A Room 2039 Bldg 12A Room 2001C

301-496-2972 301-435-9232

<munson@helix.nih.gov> <barbj@mail.nih.gov>

**Part I.**

**Introduction To JMP: The Statistical Discovery Software**

# A. How to begin a Spreadsheet

1.  Create a Table

2.  Create a column

3.  Add Rows

**B. How to Import and Export Data**

1.  Exporting Data as text

2.  Export to Excel by copying and pasting

3.  Import as Text

4.  Import from Excel by copying and pasting

**C. Column properties and row properties**

1.  Column Properties

    a.  Data type

    b.  Modeling type

    c.  Formulas

    d.  Properties

2.  Row Properties

    a.  Selected

    b.  Color

    c.  Marker

    d.  Hidden/excluded

**Part II.**

# Introduction to the MSCL Analyst's Toolbox

I.  Prepare "Final" table using GCOS from Affymetrix

    a.  Export and pivot microarray data from GCOS -- this should get
        you a text file with rows as genes and columns as chips in the
        experiment; this text file can be imported into JMP

    b.  Import text data into JMP - using Text Import Preview as your
        option from the file type drop down box and make sure that data
        is read in correctly

![](media/image1.png)

c.  Delete unused columns

d.  Rearrange columns to have all column types next to each other, ie.
    all signal data next to one another and all present/absent data next
    to each other

e.  Rename columns to contain ShortNames and prefixes of data type

> Signal data - "\_Signal" suffix becomes "SG-" prefix
>
> Present absent call date - "\_Detection" suffix becomes "PA-" prefix

[Original long column names ShortNames column names]{.underline}

JJB_cell_N1_BMP2+TNF_Signal SG-t1

JJB_cell_N1_BMP2_Signal SG-c1

JJB_cell_N2_BMP2+TNF_Signal SG-t2

JJB_cell_N2_BMP2_Signal SG-c2

JJB_cell_N3_BMP2+TNF_Signal SG-t3

JJB_cell_N3_BMP2_Signal SG-c3

JJB_cell_N4_BMP2+TNF_Signal SG-t4

JJB_cell_N4_BMP2_Signal SG-c4

JJB_cell_N1_BMP2+TNF_Detection PA-t1

JJB_cell_N1_BMP2_Detection PA-c1

JJB_cell_N2_BMP2+TNF_Detection PA-t2

JJB_cell_N2_BMP2_Detection PA-c2

JJB_cell_N3_BMP2+TNF_Detection PA-t3

JJB_cell_N3_BMP2_Detection PA-c3

JJB_cell_N4_BMP2+TNF_Detection PA-t4

JJB_cell_N4_BMP2_Detection PA-c4

![](media/image2.png){width="4.750694444444444in" height="3.46875in"}

II. Transform Intensity data - Use 3-TransformDatav3.jsl script

    a.  Select columns to be transformed.

> ![](media/image3.png){width="3.2506944444444446in" height="2.03125in"}

b.  Lmed transform is recommended for Signal data

![](media/image4.png){width="3.4993055555555554in"
height="2.161111111111111in"}

c.  User directions box will appear when script is finished:

> ![](media/image5.png){width="1.25in" height="1.2090277777777778in"}

III. Principal Components Analysis and Master File Preparation using
     ComputePrincipalComponents.jsl script

     a.  Compute PCA on transformed data

> ![](media/image6.png){width="3.9993055555555554in"
> height="3.0319444444444446in"}

b.  Inspect output of PCA

> Output:

1.  pcatable.jmp

2.  two bivariate plots (pc1 vs. pc2, and pc2 vs. pc3)

> Inspect PCA for any outliers, block effect, clusters of data.

c.  Make master file from pcatable:

> Add new columns-

i.  Add treatment type column

ii. Add replicate number column if necessary for statistical test

iii. Add blocking factor column if necessary

iv. Add any clinical data or notes about each chip

> ![](media/image7.png){width="4.627083333333333in"
> height="1.8458333333333334in"}
>
> Save PCAtable as MasterFile
>
> ![](media/image8.png){width="3.125in" height="3.0694444444444446in"}
>
> Close masterfile table.

d.  Since we see a blocking effect, run ANOVA1block.jsl script

> Script will prompt to open MasterFile associated with expression data.
> Dialog box below will ask you to select which columns from the
> masterfile are associated with expression data and which contain the
> variables of interest.
>
> ![](media/image9.png){width="4.252083333333333in"
> height="3.1881944444444446in"}
>
> Quick View of the p-values to see if there is any significance. Run a
> distribution of the 2 p-values.
>
> ![](media/image10.png){width="2.875in" height="4.205555555555556in"}

e.  Compute False Discovery Rate (FDR) on p-values to correct for
    multiple comparisons. Run ComputeFDR.jsl script

![](media/image11.png){width="4.622222222222222in"
height="2.2006944444444443in"}

f.  Compute Log Fold Change -- Add new column, name column and add new
    column property, formula. Calculate the difference between treatment
    and control or difference between two groups of interest.

![](media/image12.png){width="2.6055555555555556in"
height="3.020138888888889in"}

g.  Create Indicator Column to select for important genes. All 1's will
    be genes that met filter criteria and all 0's will be genes that did
    not meet filter criteria. 10%FDR and 2 fold change (FC) filter

> Formula to use:
>
> FDR \>= 0.1 AND absolutevalue(LFC) \>= log10(2)

![](media/image13.png){width="3.0076388888888888in"
height="3.229861111111111in"}

h.. Inspect Genes and determine how many genes met criteria for gene
list:

run a summary of indicator column:

![](media/image14.png){width="2.9972222222222222in" height="2.09375in"}

![](media/image15.png){width="2.998611111111111in"
height="1.1965277777777779in"}

\*218 genes met filter criteria

IV. Annotating Data

    a.  Obtain Affymetrix annotation information for appropriate chip
        type from
        <http://affylims.cit.nih.gov/annotations/AffymetrixAnnotations.html>

![](media/image16.png){width="2.751388888888889in"
height="1.9944444444444445in"}

Join Annotation file with Final table.

![](media/image17.png){width="3.8722222222222222in"
height="2.495138888888889in"}

Join by matching columns on ProbeSetID

![](media/image18.png){width="3.3743055555555554in"
height="2.0069444444444446in"}

V.  GO-SCAN -- annotates gene list according to significant gene
    ontology annotations
    ([www.geneontology.org](http://www.geneontology.org/))

    a.  Run GO-SCAN2.1.jsl script

        i.  IMPORTANT to note that the current working directory to run
            the script must be that where the script is because there
            are many tables that the scripts needs to run

![](media/image19.png){width="4.503472222222222in"
height="3.5840277777777776in"}

Output files will be saved in the current directory as text files.

![](media/image20.png){width="3.863888888888889in"
height="1.3541666666666667in"}

b.  Read text output file into Excel and make some small formatting
    changes in order to get collapsible list.

> ![](media/image21.png){width="3.747916666666667in"
> height="2.6277777777777778in"}
>
> Data subtotaling, first subtotal on pvalue + name column:
>
> ![](media/image22.png){width="1.988888888888889in"
> height="2.2090277777777776in"}
>
> Second subtotal on flag column:
>
> ![](media/image23.png){width="1.9638888888888888in"
> height="2.091666666666667in"}
>
> Collapsible list:
>
> ![](media/image24.png){width="4.870833333333334in"
> height="2.1458333333333335in"}

NOTE:

Collapsible List can also be created at GO-SCAN online at

http://goscan.cit.nih.gov

VI. Plots

    a.  Volcano Plot- Pvalue by Log Fold Change

> Bivariate plot of p-value changed to log scale and on y-axis and log
> fold change on x-axis. Illustrates significant

![](media/image25.png){width="3.384027777777778in"
height="3.479861111111111in"}

![](media/image26.png){width="3.122916666666667in"
height="2.5069444444444446in"}

b.  Hierarchical Clustering

> Make a subset of gene list by first selecting all genes that passed
> the filter.

![](media/image27.png){width="3.875in" height="3.3895833333333334in"}

Make sure genes are selected and then make subset.

Select Cluster from Analyze tab

![](media/image28.png){width="4.371527777777778in"
height="2.1395833333333334in"}

Select columns of interest and uncheck "Standardize Data".

![](media/image29.png){width="2.588888888888889in"
height="2.8868055555555556in"}

Investigate areas of interest.

.![](media/image30.png){width="2.0854166666666667in"
height="2.6013888888888888in"}