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eugenefischer
2025-04-09 11:52:03 -05:00
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@@ -77,7 +77,7 @@ author--has not yet been necessary.
There have been some studies which show that [auction algorithms](https://en.wikipedia.org/wiki/Auction_algorithm) for the assignment problem can have superior performance in There have been some studies which show that [auction algorithms](https://en.wikipedia.org/wiki/Auction_algorithm) for the assignment problem can have superior performance in
real-world implementations, due to their simplicity, than more complex algorithms with better theoretical asymptotic real-world implementations, due to their simplicity, than more complex algorithms with better theoretical asymptotic
performance. The author has implemented a basic forward auction algorithm, which produces optimal assignment for unbalanced bipartite graphs with performance. The author has implemented a basic forward auction algorithm, which produces optimal assignment for unbalanced bipartite graphs with
integer weights. To allow for unbalanced assignment, this algorithim eschews epsilon-scaling, integer weights. To allow for unbalanced assignment, this algorithm eschews epsilon-scaling,
and as a result is prone to "bidding-wars" which increase run time, making it less efficient than the implementation of and as a result is prone to "bidding-wars" which increase run time, making it less efficient than the implementation of
the Fredman-Tarjan algorithm in JGraphT. A forward/reverse auction algorithm as developed by Bertsekas and Castañon the Fredman-Tarjan algorithm in JGraphT. A forward/reverse auction algorithm as developed by Bertsekas and Castañon
should be able to handle unbalanced (or, as they call it, asymmetric) assignment much more efficiently, but has yet to be should be able to handle unbalanced (or, as they call it, asymmetric) assignment much more efficiently, but has yet to be
@@ -483,8 +483,9 @@ Several BiGpairSEQ simulations were performed on a home computer with the follow
* Linux Mint 21 (5.15 kernel) * Linux Mint 21 (5.15 kernel)
### SAMPLE PLATES WITH VARYING NUMBERS OF CELLS PER WELL ### SAMPLE PLATES WITH VARYING NUMBERS OF CELLS PER WELL
NOTE: these results were obtained with an earlier version of BiGpairSEQ_Sim, and should be re-run with the current version.
The observed behavior is not believed to be likely to change, however. The probability calculations used by pairSEQ require that every well on the sample plate contain the same number of T cells.
BiGpairSEQ does not share this limitation; it is robust to variations in the number of cells per well.
A series of BiGpairSEQ simulations were conducted using a cell sample file of 3.5 million unique T cells. From these cells, A series of BiGpairSEQ simulations were conducted using a cell sample file of 3.5 million unique T cells. From these cells,
10 sample plate files were created. All of these sample plates had 96 wells, used an exponential distribution with a lambda of 0.6, and 10 sample plate files were created. All of these sample plates had 96 wells, used an exponential distribution with a lambda of 0.6, and
@@ -501,6 +502,9 @@ The well populations of the plates were:
All BiGpairSEQ simulations were run with a low overlap threshold of 3 and a high overlap threshold of 94. All BiGpairSEQ simulations were run with a low overlap threshold of 3 and a high overlap threshold of 94.
No optional filters were used, so pairing was attempted for all sequences with overlaps within the threshold values. No optional filters were used, so pairing was attempted for all sequences with overlaps within the threshold values.
NOTE: these results were obtained with an earlier version of BiGpairSEQ_Sim, and should be re-run with the current version.
The observed behavior is not believed to be likely to change, however.
Constant well population plate results: Constant well population plate results:
| |1000 Cell/Well Plate|2000 Cell/Well Plate|3000 Cell/Well Plate|4000 Cell/Well Plate|5000 Cell/Well Plate | |1000 Cell/Well Plate|2000 Cell/Well Plate|3000 Cell/Well Plate|4000 Cell/Well Plate|5000 Cell/Well Plate