Use the File menu to open a data file. See the file formats page for
supported formats. Example data files can be loaded by selecting
File>Open Example Data.
Annotate columns or rows based on entries provided in a tab delimited
text file or an Excel .xls or .xlsx file. A colored bar visually
identifies members of the same category. Annotatotions are primarily
used for visualization. In analyses such as marker selection, column
annotations can also be used to identify phenotypes. To
- Create a tab delimited text file or an Excel .xls or .xlsx
- Select File>Annotate Columns or File>Annotate Rows and
open the file created previously.
GENE-E displays color bars below the column names or to the right of
the row names to indicate the categories to which the columns/rows
belong. Select Edit>Column Annotations or Edit>Row
Annotations to edit the color for a category or to delete a category.
New Heat Map
To open a new heat map on a subset of your data:
- Select the desired columns and rows.
- Select Tools>New Heat Map.
Select GENE-E>Preferences (Mac) or View>Options (other
platforms) to modify the title, look and feel of the current
visualization tool. GENE-E displays a window which provides options
specific to the current visualization tool. Most options are self
explanatory. The color tab controls the colors used in the
- Relative: GENE-E converts values to heat map colors using
the mean and maximum values for each row or the standard deviations
from the row mean for each row (as determined by the settings on
- Global. GENE-E converts values to heat map colors using the
minimum and maximum values in the entire data set (as determined by
the settings on this tab).
To change the color of the heat map click a colored square above the
heat map legend and select a new color. Click and drag a colored
square to move a control point. Click the add button to add a new
control point. Click delete to delete an existing color.
You can sort columns by column name, category, or annotation. You can
sort rows by row name, category, annotation, or the values in a
particular column. To sort columns:
Select the field(s) to sort by. Each
drop-down list includes column (for column name) and all categories
and annotations that you have loaded (in this example, the Phenotype
To sort rows:
Select Tools>Sort Rows or
click on a row header (shift-click to add a secondary sort).
Select the field(s) to sort by. Each drop-down list includes row (for
row name), each column name, and all categories and annotations that
you have loaded.
Masking rows or columns temporarily hides them from many GENE-E
operations. For example, masked rows and columns can be omitted from
new heat maps (Tools>New Heat Map). Highlight one or more
rows or columns. Select Tools>Mask Rows or Tools>Mask
Columns. Alternatively, right-click and select Mask Rows or Mask
Columns from the context menu. You can clear masked
columns/rows by highlighting one or more columns/rows and selecting
Tools>Unmask Columns or Tools>Unmask Rows or to clear all masked
columns/rows select Tools>Clear Column Mask or Tools>Clear Row
Marker selection identifies objects that are differentially expressed
between two classes. For each object, the analysis uses a test
statistic to calculate the difference in expression between the
classes and then estimates the significance (p-value) of the test
score. It then corrects for multiple hypotheses testing (MHT) by
computing both the false discovery rate (FDR) and the family-wise
error rate (FWER). The output of marker selection consists of:
- Score: The calculated value of the test statistic.
- p-value: The estimated significance of the test statistic
for this row (not yet corrected for MHT).
- p-value low: The estimated lower bound for the p-value.
- p-value high: The estimated upper bound for the p-value.
- FDR(BH): The expected proportion of non-marker genes (false
positives) within the set of genes declared to be differentially
expressed. It is estimated using the Benjamini and Hochberg
procedure. (Benjamini, Y. and Hochberg, Y. Controlling the False
Discovery Rate: A Practical and Powerful Approach to Multiple
Testing. Journal of the Royal Statistical Society. Series B
(Methodological). 57(1): p. 289-300.1. 1995.)
- FWER: The probability of having any false positives.
Hierarchical clustering recursively merges objects based on their
pair-wise distance. Objects closest together are merged first, objects
furthest apart are merged last. The result is a tree structure,
referred to as a dendogram, where the leaf nodes represent the
original items and internal (higher) nodes represent the merges that