| Breast Cancer Gene-Expression Miner v4.0
(bc-GenExMiner v4.0) | |  |
Tutorial - Gene correlation targeted analysis
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Gene correlation targeted analysis permits to know the linear dependence between each pair of the 2 to 10 submitted genes.
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| Step one |
Fill the textbox with at least two and up to ten actualised* gene symbols.
Gene symbols must be separated by ";" (e.g.: SERPINE1;
UBE2C; TACC3;...).
Click then on the "Submit" button.
*: see actualised web databases
(e.g.: GeneCards,
Source,
Unigene...)
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Step two |
After submission, a validation page shows detailed information about:
- tested gene(s)
- patients from original studies tested,
1 complete data before filtering,
- 2 number of genes found,
- 3 patients finally analysed
(if no missing genomic data).
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At this step, you can validate or cancel your submission according
to these intermediate descriptive data summarized at the bottom of the page.
"Start analysis" will launch
correlation analysis
with the chosen genes and direct you to corresponding result page.
"Cancel" will redirect you back to previous screen and offer you to choose new genes.
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Results page displays first
correlation map corresponding
to analysis with all patients.
Help to interpret correlation coefficient value is available by clicking on the "?" symbol below the colour scale.
Correlation maps corresponding to analysis with patients with positive or negative oestrogen receptor status and those corresponding
to analysis with patients from the four molecular subtypes, as determined by the
RMSPC,
and as determined by basal-like PAM50 subtyped and/or triple negative breast cancer typed by immunohistochemistry
can be displayed by clicking on the corresponding button in the left menu.
For each pair of genes,
correlation plot
can be displayed, along with correlation coefficient value,
corresponding p-value and number of patients involved, by clicking on the corresponding cell in the correlation map.
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Correlation maps can be saved in png format.
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Tables with all detailed values (correlation coefficient, p-value, number of patients) can be saved in csv format.
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