[BioC] flowCore: inverse logicle transformation of flow cytometry data

[Chao-Jen Wong]

Hi, Pyne,

I agree with you that it is good to have an inverse function. Thanks for
your suggestion and tips. We will try to implement it next week.

Thanks,
Chao-Jen

spyne at broadinstitute.org wrote:
>
> Hi,
>
> The reason I need the inverse function for logicle is because
> after I have computationally identified the cluster of events in
> logicle-transformed marker space, now I want to use the knowledge
> of that range of events in the original, untransformed scale for
> sorting out similar events in the subsequent experiments.
>
> My guess is that this may not be a very far-fetched scenario,
> and since the transformation is deterministic and bijective
> anyway, an inverse function would be good to have, at least for
> the default argument settings. One option is of course a slow
> numerical computation method.
>
> However, since the transformation is monotonic, for a fixed setting
> of arguments (e.g. the default setting), doing a simple binary search
> over a reasonable range is a cheap way to approximate the inverse
> within a desirable accuracy.
>
> Thanks!
> -Pyne
>
>
> Quoting Chao-Jen Wong <cwon2 at fhcrc.org>:
>
>> Hi, Pyne
>>
>> That is an interesting question. flowCore does not have an inverse
>> function for the logicle transformation. Since the logicle
>> transformation is an one-to-one and onto function, it is possible to
>> implement an inverse function. It is, however, not straightforward.  Do
>> you really really need such a function?
>>
>> spyne at broadinstitute.org wrote:
>>>
>>> Hi,
>>>
>>> I applied logicle transformation (with default arguments)
>>> to my data points, then detected the subpopulations of
>>> interest in the transformed data, and now I want to
>>> revert the subpopulations back to the original scale of
>>> the untransformed state.
>>>
>>> In other words, if I want to apply the inverse of the logicle
>>> transformtion (applied with default arguments, which I do not
>>> know) to my data, is that possible?
>>>
>>> Thanks.
>>> -Pyne
>>>
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>>
>>
>> -- 
>> Chao-Jen Wong
>> Program in Computational Biology
>> Division of Public Health Sciences
>> Fred Hutchinson Cancer Research Center
>> 1100 Fairview Avenue N., M2-B876
>> PO Box 19024
>> Seattle, WA 98109
>> 206.667.4485
>> cwon2 at fhcrc.org
>>
>>
>
>


-- 
Chao-Jen Wong
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
1100 Fairview Avenue N., M2-B876
PO Box 19024
Seattle, WA 98109
206.667.4485
cwon2 at fhcrc.org