DGE_Heatmap.pl - Creates a heatmap illustrating differences in gene (miRNA) expression between samples
# Minimal argument call specifying all required parameters. At least two different samples have to be specified
DGE_Heatmap.pl --output heatmap
Mapping_day0_1_i_Eland_against_mature_unambiguous.txt
Mapping_day6_1_i_Eland_against_mature_unambiguous.txt
# Maximum argument call specifying all possible parameters; Many different samples may be specified
DGE_Heatmap.pl --output heatmap.pdf --normalisation quantile --tempdir "/tmp"
--min_length 20 --max_length 25 --max_mm 2 --strand RF
--mode pdf
Mapping_day0_1_i_Eland_against_mature_unambiguous.txt
Mapping_day6_1_i_Eland_against_mature_unambiguous.txt
Mapping_day12_1_i_Eland_against_mature_unambiguous.txt
Mapping_day24_1_i_Eland_against_mature_unambiguous.txt
The output file; The script creates actually two files, one contains the heatmap and another one the legend. The legend file has the same name as the heatmap with the addition of the string "_legend.txt"; Mandatory parameter
Only reads mapping to the specified strand will be used for creating the heatmap. Possible values: R (reverse strand), F (forward strand), RF (both strands); default=RF
The minimum length of reads. Shorter reads will not enter the heatmap. default=15
The maximum length of reads. Longer reads will not enter the heatmap. default=100
The maximum number of mismatches. Reads having more mismatches will not enter the heatmap. default=2
Should the output be postscript or a pdf; [ps or pdf]; default=pdf
The temporary directory; default=/tmp
MIRO allows to use several normalisation methods to create the heatmap. default=scalelinear. At the moment the following normalisation methods are supported:
samples are not normalised, the actual observed read counts will be displayed
The total expression levels will be linearly scaled to constant level. The individual read counts will be adjusted accordingly. This is the most straight-forward normalisation method
The quantile-normalisation method
Examples: housekeep5, housekeep10, housekeep20;
This normalisation methods is a derivate of the scalelinear method. Instead of using all genes (miRNAs) for calculating the normalisation factor, only the genes having a medium expression levels will be used. Therefore the genes having the highest and the lowest expression levels will be ignored (of course only for calculating the normalisation factor, not for normalisation itself). The housekeep normalisation has to be called with the exact percentage of genes to be skipped. E.g.: housekeep20 ignores the 20% highest and the 20% lowest expressed genes. The genes (miRNAs) are weighted by the log2 of the expression level.
Display the help pages.
This script creates a heatmap illustrating differences in gene (miRNA) expression between samples.
The heatmap is calculated from run_Mapping.pl output files.
Additionally a legend is provided with which it is possible to identify the respective genes (miRNAs) in the heatmap.
A list of unambiguously mapped read files.
At least two different files have to be provided, whereas each file is assumed to represent one sample e.g.:
one tissue or one time point.
The file have to be unambiguously mapped reads using the script run_Mapping or run_Multimapper:
For example:
24688||Count=3 TACCCTGTAGATCCGAATTTGT hsa-miR-10a MIMAT0000253 Homo sapiens miR-10a 1 0 F 1 128318||Count=2 TACCCTGTAGATCCGAATTTGTG hsa-miR-10a MIMAT0000253 Homo sapiens miR-10a 1 0 F 1 150952||Count=1 TACCCTGTAGATCCTAATTTGTGT hsa-miR-10a MIMAT0000253 Homo sapiens miR-10a 1 2 R 1 212857||Count=1 TACCCTGTAGATCCAAATTTGT hsa-miR-10a MIMAT0000253 Homo sapiens miR-10a 1 1 F 1 317801||Count=1 TACCTTGTAGATCCGAATTTGTG hsa-miR-10a MIMAT0000253 Homo sapiens miR-10a 1 1 F 1 389805||Count=1 TACCCTGTATATCCGAATTTGTGG hsa-miR-10a MIMAT0000253 Homo sapiens miR-10a 1 2 F 1
The output will be a heatmap (.pdf or .ps) and the corresponding legend (.txt).
The legend file will have the same name as the heatmap file (specified in --output) with the addition of "_legend.txt".
Perl 5.8 or higher
R 2.7.0 or higher
R-library geneplotter
R-library gplots
Robert Kofler
Manuela Hummel
Lauro Sumoy
Heinz Himmelbauer
robert.kofler at crg.es