Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

5.11 MicroRNA Target Prediction Analysis Methods
· The Rb1 pathway "RB tumour suppressor/checkpoint signalling in re-
sponse to DNA damage" from the Biocarta pathway archive [60]
· The Pten pathway "PTEN dependent cell cycle arrest and apoptosis"
from the Biocarta pathway archive [59]
The complete pathway has a total of 238 nodes and the colour mapping
is based on fold change absolute values and directions taken from the dif-
ferential gene expression analysis (see Results section 6.4).The colour map-
ping was performed using the VistaClara Cytoscape plug-in [234] that assigns
colours to nodes matching expression data imported with the "Import At-
tribute/Expression Matrix File" function of Cytoscape. The assignment is
done through the sigmoid function:
y =
2
− 1 (5.10)
1 + e-sx where the value of the constant "s" determines the rate of change in the colour
gradient, with smaller values allowing for the colour to ramp very gradually,
and larger values bringing the sigmoid function closer to a step function with a
very abrupt colour change around the values −1 < y < 1. The log 2(F C) values
range between −11 < F C < 11 and a value of s=1 was applied, with darker
reds and greens indicating smaller absolute values of log 2(F C), and brighter
colours indicating greater ones. A pathway image with the same colour coding
and gradient used in the tumour correlation heatmap (Fig 7.4), has also been
generated (yellow and blue shades) to correlate the expression values of the
differentially expressed genes between the two analyses.
5.11 MicroRNA Target Prediction Analysis
In trying to answer the question "Is a subset of prediction algorithms better
at predicting than the single?" we used exon array data and microRNA mi-
croarray data from a cohort of GNS cell lines that was made available to us.
The candidate did not perform this analysis but rather used the data gath-
ered from it in the ensemble analysis of microRNA target predictions. The
exon microarray data was analysed in R using software packages from the
Bioconductor project [465]. Background correction, quantile normalization
and calculation of probe-set expression values from fluorescence data was per-
formed using the Robust Multi-chip Average (RMA) method as implemented
in the affy package [161]. We used the xmapcore system, based on the earlier
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