Remove artifacts from tracking to stop repo bloat.

.gitignore

@@ -0,0 +1 @@
+/out/

readme.md

@@ -4,7 +4,7 @@
 ## ABOUT
 
 This program simulates BiGpairSEQ (Bipartite Graph pairSEQ), a graph theory-based adaptation
-of the pairSEQ algorithm (Howie et al. 2015) for pairing T cell receptor sequences.
+of the pairSEQ algorithm (Howie, et al. 2015) for pairing T cell receptor sequences.
 
 ## THEORY
 
@@ -15,19 +15,18 @@ directly.
 BiGpairSEQ creates a [simple bipartite weighted graph](https://en.wikipedia.org/wiki/Bipartite_graph) representing the sample plate.
 The distinct TCRA and TCRB sequences form the two sets of vertices. Every TCRA/TCRB pair that share a well
 are connected by an edge, with the edge weight set to the number of wells in which both sequences appear.
-(Sequences in all wells are filtered out prior to creating the graph, as there is no signal in their occupancy pattern.)
+(Sequences present in *all* wells are filtered out prior to creating the graph, as there is no signal in their occupancy pattern.)
 The problem of pairing TCRA/TCRB sequences thus reduces to the "assignment problem" of finding a maximum weight
 matching on a bipartite graph--the subset of vertex-disjoint edges whose weights sum to the maximum possible value.
 
 This is a well-studied combinatorial optimization problem, with many known solutions.
-The best currently-known algorithm for bipartite graphs with integer weights--which is what BiGpairSEQ uses--
+The most efficient algorithm known to the author for maximum weight matching of a bipartite graph with strictly integral weights
 is from Duan and Su (2012). For a graph with m edges, n vertices per side, and maximum integer edge weight N, 
-their algorithm runs in **O(m sqrt(n) log(N))** time. This is the best known efficiency for finding a maximum weight
+their algorithm runs in **O(m sqrt(n) log(N))** time. As the graph representation of a pairSEQ experiment is 
-matching on a bipartite graph, and the integer edge weight requirement makes it ideal for BiGpairSEQ.
+bipartite with integer weights, this algorithm is ideal for BiGpairSEQ.
 
-Unfortunately, it's a fairly new algorithm, and the integer edge weight requirement makes it less generically useful.
+Unfortunately, it's a fairly new algorithm, and not yet implemented by the graph theory library used in this simulator.
-It is not implemented by the graph theory library used in this simulator. So this program
+So this program instead uses the Fibonacci heap-based algorithm of Fredman and Tarjan (1987), which has a worst-case
-instead uses the Fibonacci heap-based algorithm of Fredman and Tarjan (1987), which has a worst-case
 runtime of **O(n (n log(n) + m))**. The algorithm is implemented as described in Melhorn and Näher (1999).
 
 The current version of the program uses a pairing heap instead of a Fibonacci heap for its priority queue,
@@ -56,7 +55,7 @@ main menu looks like this:
 
 ```
 --------BiGPairSEQ SIMULATOR--------
-ALPHA/BETA T-CELL RECEPTOR MATCHING
+ALPHA/BETA T CELL RECEPTOR MATCHING
   USING WEIGHTED BIPARTITE GRAPHS
 ------------------------------------
 Please select an option:
@@ -81,9 +80,9 @@ writing files, the program will automatically add the correct extension to any f
 
 #### Cell Sample Files
 Cell Sample files consist of any number of distinct "T cells." Every cell contains 
-four sequences: Alpha CDR3, Beta CDR, Alpha CDR1, Beta CDR1. The sequences are represented by
+four sequences: Alpha CDR3, Beta CDR3, Alpha CDR1, Beta CDR1. The sequences are represented by
-random integers. CDR3 Alpha and Beta sequences are all unique. CDR1 Alpha and Beta sequences
+random integers. CDR3 Alpha and Beta sequences are all unique within a given Cell Sample file. CDR1 Alpha and Beta sequences
-are not necessarily unique; the relative diversity can be set when making a Cell Sample file.
+are not necessarily unique; the relative diversity can be set when making the file.
 
 (Note: though cells still have CDR1 sequences, matching of CDR1s is currently awaiting re-implementation.)
 
@@ -94,7 +93,7 @@ Options when making a Cell Sample file:
 Files are in CSV format. Rows are distinct T cells, columns are sequences within the cells.
 Comments are preceded by `#`
 
-Structure example:
+Structure:
 
 ---
     # Sample contains 1 unique CDR1 for every 4 unique CDR3s.
@@ -108,9 +107,9 @@ Structure example:
 Sample Plate files consist of any number of "wells" containing any number of T cells (as 
 described above). The wells are filled randomly from a Cell Sample file, according to a selected
 frequency distribution. Additionally, every individual sequence within each cell may, with some
-given dropout probability, be omitted from the file. This simulates the effect of amplification errors
+given dropout probability, be omitted from the file; this simulates the effect of amplification errors
-prior to sequencing. Plates can also be partitioned into any number of (approximately) evenly-sized
+prior to sequencing. Plates can also be partitioned into any number of sections, each of which can have a 
-sections, each of which can have a different number of T cells per well.
+different concentration of T cells per well.
 
 Options when making a Sample Plate file:
 * Cell Sample file to use
@@ -120,7 +119,7 @@ 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 exponential with a lambda of approximately 0.6. (Howie, et al. 2015))
 * Total number of wells on the plate
 * Number of sections on plate
 * Number of T cells per well
@@ -128,42 +127,42 @@ Options when making a Sample Plate file:
 * Dropout rate
 
 Files are in CSV format. There are no header labels. Every row represents a well. 
-Every column represents an individual cell, containing four sequences, represented by an array string:
+Every column represents an individual cell, containing four sequences, depicted as an array string:
 `[CDR3A, CDR3B, CDR1A, CDR1B]`. So a representative cell might look like this: 
 
 `[525902, 791533, -1, 866282]`
 
-Notice that the Alpha CDR1 is missing in the cell above, due to sequence dropout.
+Notice that the CDR1 Alpha is missing in the cell above--sequence dropout from simulated amplification error.
-Dropouts are represented by replacing sequences with the value `-1`. Comments are preceded by `#`
+Dropout sequences are replaced with the value `-1`. Comments are preceded by `#`
 
-Structure Example:
+Structure:
 
 ---
 ```
-# Cell source file name: 4MilCells.csv
+# Cell source file name:
-# Plate size: 96
+# Each row represents one well on the plate
-# Error rate: 0.1
+# Plate size:
-# Concentrations: 10000 5000 500
+# Concentrations:
-# Lambda: 0.6
+# Lambda -or- StdDev: 
 ```
-| well 1 | well 2 | well 3| ... |
+| Well 1, cell 1 | Well 1, cell 2 | Well 1, cell 3| ... |
 |---|---|---|---|
-| [105383, 786528, 959247, 925928] | [525902, 791533, -1, 866282] | [409236, 132303, 804465, 942261]| ... |
+| **Well 2, cell 1** | **Well 2, cell 2** | **Well 2, cell 3**| **...** |
-| [249930, 301502, 970003, 881099] | [523787, 552952, 997194, 970507]| [425363, 417411, 845399, -1]| ... |
+| **Well 3, cell 1** | **Well 3, cell 2** | **Well 3, cell 3**| **...** |
-| ... | ... | ... | ... |
+| **...** | **...** | **...** | **...** |
 
 ---
 
 #### Graph and Data Files
-Graph and Data files are serialized binaries of a Java object containing the graph representation of a
+Graph and Data files are serialized binaries of a Java object containing the weigthed bipartite graph representation of a
-Sample Plate and necessary metadata for matching and results output. Making them requires a Cell Sample file (to construct a list of correct sequence pairs for checking
+Sample Plate, along with the necessary metadata for matching and results output. Making them requires a Cell Sample file 
-the accuracy of BiGpairSEQ simulations) and a Sample Plate file (to construct the associated
+(to construct a list of correct sequence pairs for checking the accuracy of BiGpairSEQ simulations) and a 
-occupancy graph). These files can be several gigabytes in size. Writing them to a file lets us generate a graph and 
+Sample Plate file (to construct the associated occupancy graph). These files can be several gigabytes in size.
-its metadata once, then use it for multiple different BiGpairSEQ simulations.
+Writing them to a file lets us generate a graph and its metadata once, then use it for multiple different BiGpairSEQ simulations.
 
 Options for creating a Graph and Data file:
 * The Cell Sample file to use
-* The Sample Plate file (generated from the given Cell Sample file) to use.
+* The Sample Plate file to use. (This must have been generated from the selected Cell Sample file.)
 
 These files do not have a human-readable structure, and are not portable to other programs. (Export of graphs in a 
 portable data format may be implemented in the future. The tricky part is encoding the necessary metadata.)
@@ -181,10 +180,9 @@ Options when running a BiGpairSEQ simulation of CDR3 alpha/beta matching:
 * The maximum number of alpha/beta overlap wells to attempt to match
   * (must be <= the number of wells on the plate - 1)
 * The maximum difference in alpha/beta occupancy to attempt to match
-  * (To skip using this filter, enter a value >= the number of wells on the plate)
+  * (Optional. To skip using this filter, enter a value >= the number of wells on the plate)
-* The minimum percentage of a sequence's occupied wells shared by another sequence to attempt to match
+* The minimum overlap percentage--the percentage of a sequence's occupied wells shared by another sequence--to attempt to match. Given as value in range 0 - 100.
-  * given value from 0 to 100
+  * (Optional. To skip using this filter, enter 0)
-  * (To skip using this filter, enter 0)
 
 Example output:
 
@@ -217,34 +215,54 @@ Example output:
 ---
 
 **NOTE: The p-values in the output are not used for matching**—they aren't part of the BiGpairSEQ algorithm at all. 
-P-values are calculated *after* BiGpairSEQ matching is completed, for purposes of comparison, 
+P-values are calculated *after* BiGpairSEQ matching is completed, for purposes of comparison only, 
 using the (2021 corrected) formula from the original pairSEQ paper. (Howie, et al. 2015)
 
+### PERFORMANCE
+Performance details of the example excerpted above:
+
+On a home computer with a Ryzen 5600X CPU, 64GB of 3200MHz DDR4 RAM (half of which was allocated to the Java Virtual Machine), and a PCIe 3.0 SSD, running Linux Mint 20.3 Edge (5.13 kernel), 
+the author ran a BiGpairSEQ simulation of a 96-well sample plate with 30,000 T cells/well comprising ~11,800 alphas and betas,
+taken from a sample of 4,000,000 distinct cells with an exponential frequency distribution.
+
+With min/max occupancy threshold of 3 and 94 wells for matching, and no other pre-filtering, BiGpairSEQ identified 5,151 
+correct pairings and 18 incorrect pairings, for an accuracy of 99.652%.
+
+The simulation time was 14'22". If intermediate results were held in memory, this would be equivalent to the total elapsed time.
+
+Since this implementation of BiGpairSEQ writes intermediate results to disk (to improve the efficiency of *repeated* simulations
+with different filtering options), the actual elapsed time was greater. File I/O time was not measured, but took 
+slightly less time than the simulation itself. Real elapsed time from start to finish was under 30 minutes.
+
 ## TODO
 
 * ~~Try invoking GC at end of workloads to reduce paging to disk~~ DONE
-* ~~Hold graph data in memory until another graph is read-in?~~
+* ~~Hold graph data in memory until another graph is read-in?~~ ABANDONED
-  * No, this won't work, because BiGpairSEQ simulations alter the underlying graph based on filtering constraints. Changes would cascade with multiple experiments.
+  * *No, this won't work, because BiGpairSEQ simulations alter the underlying graph based on filtering constraints. Changes would cascade with multiple experiments.*
-* ~~See if there's a reasonable way to reformat Sample Plate files so that wells are columns instead of rows~~ DONE
+* See if there's a reasonable way to reformat Sample Plate files so that wells are columns instead of rows. 
+  * ~~Problem is variable number of cells in a well~~
+  * ~~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.
+  * 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
   * Custom vertex type with attribute for sequence occupancy?
 * Re-implement CDR1 matching method
-* Re-implement command line arguments, to enable scripting and statistical simulation studies
+* Implement Duan and Su's maximum weight matching algorithm
-* Implement Duan and Su's maximum weight matching algorithms
   * Add controllable algorithm-type parameter?
-* Test whether pairing heap (currently used) or Fibonacci heap is more efficient for current matching algorithm
+* Test whether pairing heap (currently used) or Fibonacci heap is more efficient for priority queue in current matching algorithm
   * in theory Fibonacci heap should be more efficient, but complexity overhead may eliminate theoretical advantage
   * Add controllable heap-type parameter?
-* Implement sample plates with random numbers of T cells per well
+
-  * Possible BiGpairSEQ advantage over pairSEQ: BiGpairSEQ is resilient to variations in well populations; 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.
 
   
 ## CITATIONS
 * Howie, B., Sherwood, A. M., et al. ["High-throughput pairing of T cell receptor alpha and beta sequences."](https://pubmed.ncbi.nlm.nih.gov/26290413/) Sci. Transl. Med. 7, 301ra131 (2015)
 * Duan, R., Su H. ["A Scaling Algorithm for Maximum Weight Matching in Bipartite Graphs."](https://web.eecs.umich.edu/~pettie/matching/Duan-Su-scaling-bipartite-matching.pdf) Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms, p. 1413-1424. (2012)
-* K. Melhorn, St. Näher. [The LEDA Platform of Combinatorial and Geometric Computing.](https://people.mpi-inf.mpg.de/~mehlhorn/LEDAbook.html) Cambridge University Press. Chapter 7, Graph Algorithms; p. 132-162 (1999)
+* Melhorn, K., Näher, St. [The LEDA Platform of Combinatorial and Geometric Computing.](https://people.mpi-inf.mpg.de/~mehlhorn/LEDAbook.html) Cambridge University Press. Chapter 7, Graph Algorithms; p. 132-162 (1999)
-* M. Fredman, R. Tarjan. ["Fibonacci heaps and their uses in improved network optimization algorithms."](https://www.cl.cam.ac.uk/teaching/1011/AlgorithII/1987-FredmanTar-fibonacci.pdf) J. ACM, 34(3):596–615 (1987))
+* Fredman, M., Tarjan, R. ["Fibonacci heaps and their uses in improved network optimization algorithms."](https://www.cl.cam.ac.uk/teaching/1011/AlgorithII/1987-FredmanTar-fibonacci.pdf) J. ACM, 34(3):596–615 (1987))
 
 ## EXTERNAL LIBRARIES USED
 * [JGraphT](https://jgrapht.org) -- Graph theory data structures and algorithms
@@ -257,4 +275,4 @@ BiGpairSEQ was conceived in collaboration with Dr. Alice MacQueen, who brought t
 pairSEQ paper to the author's attention and explained all the biology terms he didn't know.
 
 ## AUTHOR
-Eugene Fischer, 2021. UI improvements and documentation, 2022.
+BiGpairSEQ algorithm and simulation by Eugene Fischer, 2021. UI improvements and documentation, 2022.

src/main/java/Equations.java

@@ -8,6 +8,9 @@ public abstract class Equations {
         return (int) ((Math.random() * (max - min)) + min);
     }
 
+    //pValue calculation as described in original pairSEQ paper.
+    //Included for comparison with original results.
+    //Not used by BiGpairSEQ for matching.
     public static double pValue(Integer w, Integer w_a, Integer w_b, double w_ab_d) {
         int w_ab = (int) w_ab_d;
         double pv = 0.0;
@@ -18,6 +21,9 @@ public abstract class Equations {
         return pv;
     }
 
+    //Implementation of the (corrected) probability equation from pairSEQ paper.
+    //Included for comparison with original results.
+    //Not used by BiGpairSEQ for matching.
     private static double probPairedByChance(Integer w, Integer w_a, Integer w_b, Integer w_ab){
         BigInteger numer1 = choose(w, w_ab);
         BigInteger numer2 = choose(w - w_ab, w_a - w_ab);
@@ -30,10 +36,9 @@ public abstract class Equations {
         return prob.doubleValue();
     }
 
-    /*
+
-     * This works because nC(k+1) = nCk * (n-k)/(k+1)
+     //This works because nC(k+1) = nCk * (n-k)/(k+1)
-     * Since nC0 = 1, can start there and generate all the rest.
+     //Since nC0 = 1, can start there and generate all the rest.
-     */
      public static BigInteger choose(final int N, final int K) {
          BigInteger nCk = BigInteger.ONE;
          for (int k = 0; k < K; k++) {

src/main/java/Plate.java

@@ -68,7 +68,7 @@ public class Plate {
                     }
                     Integer[] cellToAdd = cells.get(n).clone();
                     for(int k = 0; k < cellToAdd.length; k++){
-                        if(Math.abs(rand.nextDouble()) < error){//error applied to each peptide
+                        if(Math.abs(rand.nextDouble()) < error){//error applied to each seqeunce
                             cellToAdd[k] = -1;
                         }
                     }
@@ -98,7 +98,7 @@ public class Plate {
                     n = (int) Math.floor(m);
                     Integer[] cellToAdd = cells.get(n).clone();
                     for(int k = 0; k < cellToAdd.length; k++){
-                        if(Math.abs(rand.nextDouble()) < error){//error applied to each peptide
+                        if(Math.abs(rand.nextDouble()) < error){//error applied to each sequence
                             cellToAdd[k] = -1;
                         }
                     }
@@ -134,30 +134,30 @@ public class Plate {
         return wells;
     }
 
-    //returns a map of the counts of the peptide at cell index pIndex, in all wells
+    //returns a map of the counts of the sequence at cell index sIndex, in all wells
-    public Map<Integer, Integer> assayWellsPeptideP(int... pIndices){
+    public Map<Integer, Integer> assayWellsSequenceS(int... sIndices){
-        return this.assayWellsPeptideP(0, size, pIndices);
+        return this.assayWellsSequenceS(0, size, sIndices);
     }
 
-    //returns a map of the counts of the peptide at cell index pIndex, in a specific well
+    //returns a map of the counts of the sequence at cell index sIndex, in a specific well
-    public Map<Integer, Integer> assayWellsPeptideP(int n, int... pIndices) { return this.assayWellsPeptideP(n, n+1, pIndices);}
+    public Map<Integer, Integer> assayWellsSequenceS(int n, int... sIndices) { return this.assayWellsSequenceS(n, n+1, sIndices);}
 
-    //returns a map of the counts of the peptide at cell index pIndex, in a range of wells
+    //returns a map of the counts of the sequence at cell index sIndex, in a range of wells
-    public Map<Integer, Integer> assayWellsPeptideP(int start, int end, int... pIndices) {
+    public Map<Integer, Integer> assayWellsSequenceS(int start, int end, int... sIndices) {
         Map<Integer,Integer> assay = new HashMap<>();
-        for(int pIndex: pIndices){
+        for(int pIndex: sIndices){
             for(int i = start; i < end; i++){
-                countPeptides(assay, wells.get(i), pIndex);
+                countSequences(assay, wells.get(i), pIndex);
             }
         }
         return assay;
     }
-    //For the peptides at cell indices pIndices, counts number of unique peptides in the given well into the given map
+    //For the sequences at cell indices sIndices, counts number of unique sequences in the given well into the given map
-    private void countPeptides(Map<Integer, Integer> wellMap, List<Integer[]> well, int... pIndices) {
+    private void countSequences(Map<Integer, Integer> wellMap, List<Integer[]> well, int... sIndices) {
         for(Integer[] cell : well) {
-            for(int pIndex: pIndices){
+            for(int sIndex: sIndices){
-                if(cell[pIndex] != -1){
+                if(cell[sIndex] != -1){
-                    wellMap.merge(cell[pIndex], 1, (oldValue, newValue) -> oldValue + newValue);
+                    wellMap.merge(cell[sIndex], 1, (oldValue, newValue) -> oldValue + newValue);
                 }
             }
         }

src/main/java/PlateFileReader.java

@@ -31,54 +31,23 @@ public class PlateFileReader {
             BufferedReader reader = Files.newBufferedReader(Path.of(filename));
             CSVParser parser = new CSVParser(reader, plateFileFormat);
         ){
-            //old code for wells as rows
-//            for(CSVRecord record: parser.getRecords()) {
-//                List<Integer[]> well = new ArrayList<>();
-//                for(String s: record) {
-//                    if(!"".equals(s)) {
-//                        String[] intString = s.replaceAll("\\[", "")
-//                                .replaceAll("]", "")
-//                                .replaceAll(" ", "")
-//                                .split(",");
-//                        //System.out.println(intString);
-//                        Integer[] arr = new Integer[intString.length];
-//                        for (int i = 0; i < intString.length; i++) {
-//                            arr[i] = Integer.valueOf(intString[i]);
-//                        }
-//                        well.add(arr);
-//                    }
-//                }
-//                wells.add(well);
             for(CSVRecord record: parser.getRecords()) {
-                if (wells.size() == 0) {
+                List<Integer[]> well = new ArrayList<>();
-                    int num = 0;
                 for(String s: record) {
-                        num++;
+                    if(!"".equals(s)) {
-                    }
+                        String[] intString = s.replaceAll("\\[", "")
-                    for (int i = 0; i < num; i++) {
-                        wells.add(new ArrayList<>());
-                    }
-                } else {
-                    int i = 0;
-                    for (String s : record) {
-                        if (!"".equals(s)) { //if value isn't the empty string
-                            //get rid of brackets, split at commas into a string array
-                            String[] intsAsStrings = s.replaceAll("\\[", "")
                                 .replaceAll("]", "")
                                 .replaceAll(" ", "")
                                 .split(",");
-                            //Make Integer array with the same values
+                        //System.out.println(intString);
-                            Integer[] arr = new Integer[intsAsStrings.length];
+                        Integer[] arr = new Integer[intString.length];
-                            for (int j = 0; j < intsAsStrings.length; j++) {
+                        for (int i = 0; i < intString.length; i++) {
-                                arr[j] = Integer.valueOf(intsAsStrings[j]);
+                            arr[i] = Integer.valueOf(intString[i]);
                         }
-                            //Add Integer array to the correct well
+                        well.add(arr);
-                            wells.get(i).add(arr);
-                            i++;
                     }
                 }
-
+                wells.add(well);
-                }
             }
         } catch(IOException ex){
             System.out.println("plate file " + filename + " not found.");

src/main/java/PlateFileWriter.java

@@ -59,7 +59,7 @@ public class PlateFileWriter {
             }
         }
 
-        //this took forever, and I don't use it, because it makes reading data in a huge pain
+        //this took forever
         List<List<String>> rows = new ArrayList<>();
         List<String> tmp = new ArrayList<>();
         for(int i = 0; i < wellsAsStrings.size(); i++){//List<Integer[]> w: wells){
@@ -89,6 +89,7 @@ public class PlateFileWriter {
             CSVPrinter printer = new CSVPrinter(writer, plateFileFormat);
         ){
             printer.printComment("Cell source file name: " + sourceFileName);
+            printer.printComment("Each row represents one well on the plate.");
             printer.printComment("Plate size: " + size);
             printer.printComment("Error rate: " + error);
             printer.printComment("Concentrations: " + concenString);
@@ -98,7 +99,7 @@ public class PlateFileWriter {
             else {
                 printer.printComment("Std. dev.: " + stdDev);
             }
-            printer.printRecords(rows);
+            printer.printRecords(wellsAsStrings);
         } catch(IOException ex){
             System.out.println("Could not make new file named "+filename);
             System.err.println(ex);

src/main/java/Simulator.java

@@ -57,8 +57,8 @@ public class Simulator {
         if(verbose){System.out.println("Cell maps made");}
 
         if(verbose){System.out.println("Making well maps");}
-        Map<Integer, Integer> allAlphas = samplePlate.assayWellsPeptideP(alphaIndex);
+        Map<Integer, Integer> allAlphas = samplePlate.assayWellsSequenceS(alphaIndex);
-        Map<Integer, Integer> allBetas = samplePlate.assayWellsPeptideP(betaIndex);
+        Map<Integer, Integer> allBetas = samplePlate.assayWellsSequenceS(betaIndex);
         int alphaCount = allAlphas.size();
         if(verbose){System.out.println("All alphas count: " + alphaCount);}
         int betaCount = allBetas.size();
@@ -296,8 +296,8 @@ public class Simulator {
         System.out.println("Cell maps made");
 
         System.out.println("Making well maps");
-        Map<Integer, Integer> allCDR3s = samplePlate.assayWellsPeptideP(cdr3Indices);
+        Map<Integer, Integer> allCDR3s = samplePlate.assayWellsSequenceS(cdr3Indices);
-        Map<Integer, Integer> allCDR1s = samplePlate.assayWellsPeptideP(cdr1Indices);
+        Map<Integer, Integer> allCDR1s = samplePlate.assayWellsSequenceS(cdr1Indices);
         int CDR3Count = allCDR3s.size();
         System.out.println("all CDR3s count: " + CDR3Count);
         int CDR1Count = allCDR1s.size();
@@ -591,26 +591,26 @@ public class Simulator {
                                                     Map<Integer, Integer> rowSequenceCounts,
                                                     Map<Integer,Integer> columnSequenceCounts,
                                                     double[][] weights){
-        Map<Integer, Integer> wellNRowPeptides = null;
+        Map<Integer, Integer> wellNRowSequences = null;
-        Map<Integer, Integer> wellNColumnPeptides = null;
+        Map<Integer, Integer> wellNColumnSequences = null;
         int vertexStartValue = rowSequenceToVertexMap.size();
         int numWells = samplePlate.getSize();
         for (int n = 0; n < numWells; n++) {
-            wellNRowPeptides = samplePlate.assayWellsPeptideP(n, rowSequenceIndices);
+            wellNRowSequences = samplePlate.assayWellsSequenceS(n, rowSequenceIndices);
-            for (Integer a : wellNRowPeptides.keySet()) {
+            for (Integer a : wellNRowSequences.keySet()) {
                 if(allRowSequences.containsKey(a)){
                     rowSequenceCounts.merge(a, 1, (oldValue, newValue) -> oldValue + newValue);
                 }
             }
-            wellNColumnPeptides = samplePlate.assayWellsPeptideP(n, colSequenceIndices);
+            wellNColumnSequences = samplePlate.assayWellsSequenceS(n, colSequenceIndices);
-            for (Integer b : wellNColumnPeptides.keySet()) {
+            for (Integer b : wellNColumnSequences.keySet()) {
                 if(allColumnSequences.containsKey(b)){
                     columnSequenceCounts.merge(b, 1, (oldValue, newValue) -> oldValue + newValue);
                 }
             }
-            for (Integer i : wellNRowPeptides.keySet()) {
+            for (Integer i : wellNRowSequences.keySet()) {
                 if(allRowSequences.containsKey(i)){
-                    for (Integer j : wellNColumnPeptides.keySet()) {
+                    for (Integer j : wellNColumnSequences.keySet()) {
                         if(allColumnSequences.containsKey(j)){
                             weights[rowSequenceToVertexMap.get(i)][columnSequenceToVertexMap.get(j) - vertexStartValue] += 1.0;
                         }

src/main/java/UserInterface.java

@@ -258,7 +258,7 @@ public class UserInterface {
             while (!quit) {
                 System.out.println();
                 System.out.println("--------BiGPairSEQ SIMULATOR--------");
-                System.out.println("ALPHA/BETA T-CELL RECEPTOR MATCHING");
+                System.out.println("ALPHA/BETA T CELL RECEPTOR MATCHING");
                 System.out.println("  USING WEIGHTED BIPARTITE GRAPHS  ");
                 System.out.println("------------------------------------");
                 System.out.println("Please select an option:");
@@ -681,18 +681,8 @@ public class UserInterface {
         System.out.println("This program simulates BiGpairSEQ, a graph theory based adaptation");
         System.out.println("of the pairSEQ algorithm for pairing T cell receptor sequences.");
         System.out.println();
-        System.out.println("Unlike pairSEQ, which calculates p-values for every TCR alpha/beta overlap and compares");
+        System.out.println("For full documentation, view readme.md file distributed with this code");
-        System.out.println("against a null distribution, BiGpairSEQ does not do any statistical calculations");
+        System.out.println("or visit https://gitea.ejsf.synology.me/efischer/BiGpairSEQ.");
-        System.out.println("directly. Instead, BiGpairSEQ creates a simple bipartite weighted graph representing");
-        System.out.println("the sample plate. The distinct TCRA and TCRB sequences form the two sets of vertices.");
-        System.out.println("Every TCRA/TCRB pair that share a well are connected by an edge, with the edge weight");
-        System.out.println("set to the number of wells in which both sequences appear. (Sequences in all wells are");
-        System.out.println("filtered out prior to creating the graph, as there is no signal in their occupancy");
-        System.out.println("pattern.) The problem of pairing TCRA/TCRB sequences thus reduces to the \"assignment");
-        System.out.println("problem\" of finding a maximum weight matching on a bipartite graph--the subset of");
-        System.out.println("vertex-disjoint edges whose weights sum to the maximum possible value.");
-        System.out.println();
-        System.out.println("For full documentation, see: https://gitea.ejsf.synology.me/efischer/BiGpairSEQ");
         System.out.println();
         System.out.println("pairSEQ citation:");
         System.out.println("Howie, B., Sherwood, A. M., et. al.");