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@@ -131,11 +131,14 @@ Options when making a Sample Plate file:
* Standard deviation size
* Exponential
* Lambda value
* *(Based on the slope of the graph in Figure 4C of the pairSEQ paper, the distribution of the original experiment was exponential with a lambda of approximately 0.6. (Howie, et al. 2015))*
* *(Based on the slope of the graph in Figure 4C of the pairSEQ paper, the distribution of the original experiment was approximately exponential with a lambda ~0.6. (Howie, et al. 2015))*
* Total number of wells on the plate
* Number of sections on plate
* Number of T cells per well
* per section, if more than one section
* Well populations random or fixed
* If random, minimum and maximum population sizes
* If fixed
* Number of sections on plate
* Number of T cells per well
* per section, if more than one section
* Dropout rate
Files are in CSV format. There are no header labels. Every row represents a well.
@@ -260,7 +263,7 @@ slightly less time than the simulation itself. Real elapsed time from start to f
* ~~Apache Commons CSV library writes entries a row at a time~~
* _Got this working, but at the cost of a profoundly strange bug in graph occupancy filtering. Have reverted the repo until I can figure out what caused that. Given how easily Thingiverse transposes CSV matrices in R, might not even be worth fixing._
* Re-implement command line arguments, to enable scripting and statistical simulation studies
* Implement sample plates with random numbers of T cells per well.
* ~~Implement sample plates with random numbers of T cells per well.~~ DONE
* Possible BiGpairSEQ advantage over pairSEQ: BiGpairSEQ is resilient to variations in well population sizes on a sample plate; pairSEQ is not.
* preliminary data suggests that BiGpairSEQ behaves roughly as though the whole plate had whatever the *average* well concentration is, but that's still speculative.
* Enable GraphML output in addition to serialized object binaries, for data portability