A tool to find genes which might predict differential survival when patients from a group are divided in up/downregulated subgroups.
Assume your hybridisations are from a set of
patients with defined survival and censoring state.
Survival scan calculates Mean and Standard Deviation of regulation from all
selected patients.
Next the group is subdivided in two arms:
Up: Patients with significant up regulation compared to whole consortium
Down: Patients with significant down regulation compared to whole consortium
for all three subgroups (up/down/all) KaplanMeier curves and Median survival are computed.
Now you can filter those genes with predict differential survival in groupings
up <=> all
down <=> all
up <=> down
Click Survival button and Select
Survival filter from dropdown menu:
The Group selection dialog pops up.
Here you can define two groups:
Group 1 : The diagnostic group, i.e. those hybridisations (patients) which will be analysed and assigned to the two subclasses
Group 2 : Additional hybridisations used to calculate reference survival data
On the parameters tabsheet define required
values:
Survival time row, Censoring row like for calculation of KaplanMeier curves
Threshold: Algorithm how average expression for a single gene is calculates: Mean=arithmetic mean, Median= Median mean
Min.Group size: Minimum number of hybs (patients in the two subclasses (up / downregulated)
Confidence interval: Number of standard deviations for thresholding
For each single gene the following steps are performed:
1. From all members of Group1 calculate:
Average (either Mean or Median)
Standard Deviation
2. Assign members from group 1 to:
Class1: Upregulated = Respective Hyb (patient) has expression value >= Average + (Conf.Interval * StandardDeviation)
Class2: Downregulated = Respective Hyb (patient) has expression value <= Average  (Conf.Interval * StandardDeviation)
Noclass: Not regulated = all other
3. Filter on Classsize: If (membernumber from Class1 < Min.GroupSize) or (membernumber from Class2 <Min.GroupSize) => ignore this gene
4. Compute median survival for members from Class1 (=MS1), Class2 (=MS2), Group1+Group2 (=MSR)
5. Compute survivalgain / loss (SGL) factor:
SGL1 = MS1 / MSR, if SGL1>1 => SGL1 = 1/SGL1
SGL2 = MS2 / MSR, if SGL2>1 => SGL2 = 1/SGL2
SGL12 = MS1 / MS2, if SGL12>1 => SGL12 = 1/SGL12
These values are later on shown in the pgraph. As smaller the SGxx values, as larger the gain or loss of survival.
Open the analysis in the analysis tree.
On the pgraph adjust the gain / loss
factor (i.e. adjust the pvalue slider).
For technical reasons the the pgraph was "misused and not correctly adapted.
Instead o the normal statistical pvalue a gain loss factor is displayed (as
computed by SUMO, see above)
In the example: p=0.4 => onyl genes with loss or gain of survival >= 60%
compared to the reference group will be selected.
Select the SGLclass by selecting the respective field in the result table:
down<=>ref, up<=>ref, down<=>up (here: downref)
Click KaplanMeier survival curves in
the analysistree to view KaplanMeier curves from the filtered gene for the
three classes:
Use the navigation buttons (blue arrow buttons) to cycle through the filtered
genes.
Click Population map to visualise
which hybs (patients) are members of the Down/Upregulated class for the
individual filtered gene.
The population may be clustered like a regular heat map:
Colours indicate membership of a single hyb (patient) in a particular gene to
the two (up / down
regulated versus average) class.
Also all other functionality available for handling and analysing of heat maps
can be used.
Applied to 3 groups.
Find genes differentially regulated in patient group 2 <=> 3.
Try to find genes in group1 which might predict differential survival when
patients from group1 are divided in Group2/3 similar patients.
Assume your hybridisations are from a set of
patients with defined survival and censoring state.
Survival scan calculates Mean and Standard Deviation of regulation from all
selected patients.
Two patient groups can be defined with the
aim to find genes which are differentially regulated under patient parameters
which were used to subdivide the two arms:
E.g. you have hybridisation from livertumour samples. Your patient annotations
contain information about Grading (=differntiation of tumour tissue:
1=differntiated=looks like normal liver ,...,3,4=undifferentiated no similarity
to normal liver)
A 2class ttest without multiple testing correction is used to find differentially expressed genes at the defined critical pvalue (G1G3alpha) between Grade1 / Grade34 patients.
Next, the patients in group 2 (e.g. in our
example patients with Grade 2) are divided in those being more similar to the
Grade1 / Grade34 patients.
This is done by computing Mean and SDev between G1 / G34. Now, all patients
> M+Confidence range*SDev => Subgroup1
< MConfidence range*SDev => Subgroup2
As above KaplanMeier, median surviaval and LogRank test are calculated for
Subgroup1/2 and Group1.
Survival time row  Sample annotation row containing survival data Days, weeks, month, years wahtever. The row shold only contain NUMBERS (5 weeks is not a number and will be interpreted as 0) 
Censored time row  Sample annotation row containing survival data for censored samples. Instead you may use the censoring row. 
Censoring row  Sample annotation row containing consoring nformation, i.e. defines whether the individual died at that time point (Survival time point) or was lost from the study without further knowledge about survival. Use "yes", "ja", "c", "censored","1" to define a sampel as censored, anything else is interpreted as not censored. 
Threshold  Define how to group the samples:

G2/G3.alpha  not used 
Min group size  The minimal number of samples per group (down/up regulated samples in a particular gene) 
Name  Value (exsample)  Meaning 

ReporterID  ebvmiRBART115p  Name of gene/.... 
Source line #  66  GeneID in original data matrix 
# analysed  495  Number of samples analyzed; shoud be identical for all genes 
Down threshold  0.008  Threshold for downregulated samples 
# down  29  Number of downregulated samples 
% down  0.06  Number of downregulated samples in % 
Members  12:2361:0,20:430:0,22:326:0,...  A data triplet for each sample within the group: SampleID:Survivaltime:CensorState e.g. "12:2361:0"  Sample 12, SUrvival time=2361 (days), not cnesored (0) 
Up threshold  0.010  Threshold for upregulated samples 
# up  38  Number of upregulated samples 
% up  0.08  Number of upregulated samples in % 
Members  1:358:0,5:705:0,6:322:0,39:167:0,...  SampleIDs,Survival,Censorstate for all members 
G1Mean  0.011  Mean (arithmetic) regulation in Downregulated samples (Group1) 
G1SDev  0.002  Standard deviation from regulation in Group1 
G2Mean  0.019  Mean (arithmetic) regulation in Upregulated samples (Group2) 
G2SDev  0.016  Standard deviation from regulation in Group2 
G1G2  0.030  Differential regulation (MeanG1  MeanG2) 
tvalue  11.538  tvalue (Studentsttest) with Welsh approximation) 
pvalue  1.91E014  pvalue from tdistribution with Sattertwaithe approximation) 
#reference  383  Number of samples used to compute reference Kaplan Meier Curve (KM) Sample numbers used for KMdata may differ from groupsize. Samples, for which there were NO survaval data in sample annotation, can not be used for survival data (obvious) 
Members1  12 20 22 ...  List of sampleID for G1; may be used to identidfy samples in Heatmap/ Group Selector 
Members2  1 5 6 39 ...  List of sampleID for G2 
DownMedian survival  482.000  Median survival in G1 
UpMedian survival  350.000  Median survival in G2 
ReferenceMedian survival  377.000  Median survival in ALL selectged samples (Ref) 
Down/Reference log2survivalgain  0.782  Compute: Ratio=MediaSurvialG1 / Media_SurvivalRef This cell contains = absolute (log2 (Ratio)) Cell value=1 => The mediansurvival betwen the groups differs by factor 2x 
Up/Reference log2survivalgain  0.928  
Down/Up log2survivalgain  0.726  
DownReference Log rank test  1.037  Log Rank Test (LRT) between G1<=>Ref (DownRegulated<=>Ref) 
UpReference Log rank test  4.254  Rank Test between G2<=>Ref (UpRegulated<=>Ref) 
DownUp Log rank test  5.952  Rank Test between G1<=>G2 (Down<=>upRegulated) 
DownReference pLRT  1.00E+000  pvalue from LRT G1<=>Ref 
UpReference pLRT  1.00E+000  pvalue from LRT G2<=>Ref 
DownUp pLRT  1.47E002  pvalue from LRT G1<=>G2 