├── LICENSE ├── README.md ├── condor_overview.png ├── data ├── CRC │ ├── Snakefile │ ├── config.yaml │ └── input │ │ ├── CRC2-L.csv │ │ ├── CRC2.csv │ │ ├── alt_readcounts.csv │ │ ├── character_matrix.csv │ │ └── total_readcounts.csv ├── PDAC │ ├── PDAC_panel.xlsx │ ├── Snakefile │ ├── config.yaml │ └── input │ │ ├── M04_regions.csv │ │ ├── M04_variants.csv │ │ ├── alt_readcounts.csv │ │ ├── amplicon_readcounts.csv │ │ ├── character_matrix.csv │ │ ├── snp_list.txt │ │ ├── sphyr_cell_names.txt │ │ ├── sphyr_input.txt │ │ ├── sphyr_mutation_names.txt │ │ └── total_readcounts.csv ├── sample │ ├── overview_B.csv │ ├── overview_character_matrix.csv │ ├── overview_character_matrix_without_noise.csv │ ├── overview_copynumbertree_scarlet.txt │ ├── overview_multi_state_character_matrix.csv │ ├── overview_multi_state_tree_node_character_matrix.csv │ ├── overview_phiscs.txt │ ├── overview_read_count.csv │ ├── overview_read_count_without_missing.csv │ ├── overview_readcounts_scarlet.csv │ ├── overview_scite.txt │ ├── overview_sifit.txt │ ├── overview_sphyr.txt │ ├── overview_tree.dot │ ├── overview_tree.newick │ ├── overview_tree_edgelist.csv │ ├── overview_tree_without_cells.dot │ ├── overview_variant_count.csv │ └── overview_variant_count_without_missing.csv └── simulation │ ├── Snakefile │ └── config.yaml ├── scripts ├── auxiliary.py └── fix_scarlet_output.py └── src ├── condor.py ├── evaluate_reads.py ├── simulation_reads.py └── solveConstrainedDollo.py /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 raphael-group 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ConDoR (constrained Dollo Reconstruction) 2 | 3 | ![Overview of ConDoR](condor_overview.png) 4 | Overview of the ConDoR algorithm. 5 | ConDoR takes as input: (a) A clustering of cells based on copy-number profiles and (b) variant and total read counts from scDNA-seq data. 6 | ConDoR employs the Constrained k-Dollo model to construct the (c) constrained k-Dollo phylogeny and the (d) mutation matrix. 7 | 8 | More details about the problem formulation and the algorithm can be found here: https://www.biorxiv.org/content/10.1101/2023.01.05.522408v1.abstract 9 | 10 | ## Contents 11 | 12 | 1. [Pre-requisites](#pre-requisites) 13 | 2. [Usage instcructions](#usage) 14 | * [I/O formats](#io) 15 | * [ConDoR](#condor) 16 | 17 | 18 | ## Pre-requisites 19 | + python3 (>=3.6) 20 | + [numpy](https://numpy.org/doc/) 21 | + [pandas](https://pandas.pydata.org/pandas-docs/stable/index.html) 22 | + [gurobipy](https://www.gurobi.com/documentation/9.0/quickstart_mac/py_python_interface.html) 23 | + [networkx](https://networkx.org/) 24 | + (optional for generating simulation instances) [snakemake (>=5.2.0)](https://snakemake.readthedocs.io) 25 | 26 | 27 | ## Usage instructions 28 | 29 | 30 | ### I/O formats 31 | The input for ConDoR are CSV files containing the total read counts, the variant read counts and the clustering (with or without the mutation matrix). 32 | It is important that the format matches the examples given in `data/sample` that were generated using the following commands. 33 | 34 | ```bash 35 | mkdir data/sample 36 | python src/simulation_reads.py -n 25 -m 25 -p 5 -k 1 -s 0 -d 0.1 -a 0.001 -b 0.001 -o data/sample/overview 37 | ``` 38 | 39 | 40 | ### ConDoR 41 | 42 | usage: condor.py [-h] [-i I] [-r R] [-v V] [-s S] [-a A] [-b B] [--ado ADO] [-k K] -o O [-t T] 43 | 44 | optional arguments: 45 | -h, --help show this help message and exit 46 | -i I csv file with mutation matrix and cluster id 47 | -r R csv file with total read count matrix 48 | -v V csv file with variant read count matrix 49 | -s S file containing list of SNPs 50 | -a A false positive error rate [0.001] 51 | -b B false negative error rate [0.001] 52 | --ado ADO precision parameter for ADO 53 | -k K maximum number of losses for an SNV 54 | -o O output prefix 55 | -t T time limit in seconds [1800] 56 | 57 | An example of usage is as follows. 58 | 59 | $ python src/condor.py -i data/sample/overview_character_matrix.csv -a 0.0018 -b 0.001 -k 1 -r data/sample/overview_read_count.csv -v data/sample/overview_variant_count.csv -o data/sample/overview 60 | -------------------------------------------------------------------------------- /condor_overview.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raphael-group/ConDoR/08c116181dc54d7d5a73f3bd4bbc3deca62bf769/condor_overview.png -------------------------------------------------------------------------------- /data/CRC/Snakefile: -------------------------------------------------------------------------------- 1 | # simulation snakefile 2 | configfile: "config.yaml" 3 | 4 | rule all: 5 | input: 6 | # condor 7 | #expand('condor/CRC2_k{k}_B.csv', filtered_product, seed=seeds, ncells=config['ncells'], ncharacters=config['ncharacters'], nclusters=config['nclusters'], missing=config['missing_rate'], k=config['k']), 8 | expand('condor_reads/CRC2_k{k}_B.csv', k=config['k']), 9 | expand('scarlet/CRC2.edgelist'), 10 | 11 | #rule condor: 12 | # output: 13 | # B='condor/CRC2_k{k}_B.csv', 14 | # input: 15 | # character_matrix='ground_truth/CRC2_k{k}_character_matrix.csv', 16 | # params: 17 | # prefix='condor/CRC2_k{k}', 18 | # fp=config['estimated_fp'], 19 | # fn=config['estimated_fn'], 20 | # log: 21 | # std='condor/CRC2_k{k}.log', 22 | # err='condor/CRC2_k{k}.err.log', 23 | # benchmark: 'condor/CRC2_k{k}.benchmark', 24 | # shell: 25 | # 'python ../../src/condor.py -i {input.character_matrix} -a {params.fp} -b {params.fn} -k {wildcards.k} ' 26 | # ' -o {params.prefix} ' 27 | # ' > {log.std} 2> {log.err} ' 28 | 29 | 30 | rule condor_reads: 31 | output: 32 | B='condor_reads/CRC2_k{k}_B.csv', 33 | input: 34 | character_matrix='input/character_matrix.csv', 35 | total_read_matrix='input/total_readcounts.csv', 36 | variant_read_matrix='input/alt_readcounts.csv', 37 | params: 38 | prefix='condor_reads/CRC2_k{k}', 39 | fp=config['fp'], 40 | fn=config['fn'], 41 | log: 42 | std='condor_reads/CRC2_k{k}.log', 43 | err='condor_reads/CRC2_k{k}.err.log', 44 | benchmark: 'condor_reads/CRC2_k{k}.benchmark', 45 | shell: 46 | 'python ../../src/condor.py -i {input.character_matrix} -a {params.fp} -b {params.fn} -k {wildcards.k} ' 47 | ' -r {input.total_read_matrix} -v {input.variant_read_matrix} ' 48 | ' -o {params.prefix} ' 49 | ' > {log.std} 2> {log.err} ' 50 | 51 | 52 | rule scarlet: 53 | output: 54 | Bmatrix= 'scarlet/CRC2.B_ancestor', 55 | input: 56 | readcount= 'input/CRC2.csv', 57 | copynumbertree= 'input/CRC2-L.csv', 58 | params: 59 | python_exec=config['python2_exec'], 60 | scarlet_exec=config['scarlet_exec'], 61 | prefix='scarlet/CRC2' 62 | log: 63 | std='scarlet/CRC2.log', 64 | err='scarlet/CRC2.err.log', 65 | benchmark: 'scarlet/CRC2.benchmark', 66 | shell: 67 | '{params.python_exec} {params.scarlet_exec} {input.readcount} {input.copynumbertree} {params.prefix} ' 68 | ' > {log.std} 2> {log.err} ;' 69 | 70 | rule fix_scarlet: 71 | output: 72 | Bmatrix_fixed= 'scarlet/CRC2.B_ancestor_fixed', 73 | input: 74 | Bmatrix= 'scarlet/CRC2.B_ancestor', 75 | character_matrix='input/character_matrix.csv', 76 | params: 77 | prefix='scarlet/CRC2' 78 | shell: 79 | ' python ../../scripts/fix_scarlet_output.py -d {input.character_matrix} -i {params.prefix}.B_ancestor -o {params.prefix}' 80 | 81 | rule scarlet_plot: 82 | input: 83 | Bmatrix='scarlet/CRC2.B_ancestor_fixed', 84 | copynumbertree= 'input/CRC2-L.csv', 85 | output: 86 | tree='scarlet/CRC2.edgelist', 87 | params: 88 | python_exec=config['python2_exec'], 89 | scarlet_plot_exec=config['scarlet_plot_exec'], 90 | prefix='scarlet/CRC2' 91 | log: 92 | std='scarlet/CRC2.plot.log', 93 | err='scarlet/CRC2.plot.err.log', 94 | benchmark: 'scarlet/CRC2.plot.benchmark', 95 | shell: 96 | '{params.python_exec} {params.scarlet_plot_exec} {input.Bmatrix} {input.copynumbertree} ALL {params.prefix} ' 97 | ' > {log.std} 2> {log.err} ' 98 | -------------------------------------------------------------------------------- /data/CRC/config.yaml: -------------------------------------------------------------------------------- 1 | # 2 | 3 | fp: 0.001 4 | fn: 0.001 5 | 6 | k: 7 | #- 0 8 | - 1 9 | #- 2 10 | 11 | vaf_threshold: 0.1 12 | read_threshold: 5 13 | 14 | # scarlet parameters 15 | python2_exec: '/n/fs/ragr-data/users/palash/anaconda3/envs/scarlet/bin/python2' 16 | scarlet_exec: '/n/fs/ragr-data/users/palash/scarlet/code/scarlet.py' 17 | scarlet_plot_exec: '/n/fs/ragr-data/users/palash/scarlet/code/plot_tree.py' 18 | 19 | -------------------------------------------------------------------------------- /data/CRC/input/CRC2-L.csv: -------------------------------------------------------------------------------- 1 | 3,2,FHIT-chr3-60412480,TOX-chr8-59851979,PTPRD-chr9-10074741 2 | 1,3,FHIT-chr3-60412480,LINGO2-chr9-29178757,LRP1B-chr2-141609298,LINGO2-chr9-29117698,STRN-chr2-37105068,NR3C2-chr4-149357051,CIITA-chr16-11002009,LINGO2-chr9-28064983,LINGO2-chr9-28778389,LINGO2-chr9-29123273,PTPRD-chr9-10074741,NR4A3-chr9-102595561 3 | 0,1, 4 | -------------------------------------------------------------------------------- /data/CRC/input/alt_readcounts.csv: -------------------------------------------------------------------------------- 1 | ,TSHZ3-chr19-31768568,TP53-chr17-7577548,TOX-chr8-59851979,STRN-chr2-37105068,SPEN-chr1-16258997,SPEN-chr1-16202934,PTPRD-chr9-10074741,PRKCB-chr16-24230186,PIK3CG-chr7-106508397,NRAS-chr1-115258747,NR4A3-chr9-102595561,NR3C2-chr4-149357051,MYH11-chr16-15917267,MN1-chr22-28193785,LRP1B-chr2-141609298,LINGO2-chr9-29178757,LINGO2-chr9-29123273,LINGO2-chr9-29117698,LINGO2-chr9-28778389,LINGO2-chr9-28064983,LAMB4-chr7-107732769,IL7R-chr5-35876645,IL21R-chr16-27448836,HELZ-chr17-65119071,FUS-chr16-31193877,FHIT-chr3-60412480,F8-chrX-154157378,EPHB6-chr7-142566125,CIITA-chr16-11002009,CHN1-chr2-175779934,CDK4-chr12-58144548,ATR-chr3-142269072,ATP7B-chr13-52534322,APC-chr5-112175328,APC-chr5-112164646,ALK-chr2-29416591 2 | MA-25,0,4,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,4,0 3 | MA-26,0,1,0,0,0,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,1,0 4 | MA-27,5,1440,532,191,5,0,0,15,6,65,1,0,10,534,1,0,22,33,2,0,0,0,36,58,0,0,5,0,0,0,1035,0,2,3,53,0 5 | MA-28,0,67,4,18,148,0,0,1,6,17,0,0,75,4,0,0,329,273,25,8,120,0,14,0,0,0,405,0,0,0,100,2,1,5,134,0 6 | MA-29,0,559,19,0,0,0,0,0,0,104,0,0,17,6,0,2,1,68,0,5,0,0,0,0,9,0,5,0,0,3,40,0,3,0,27,0 7 | MA-30,5,103,102,11,0,1,3,26,0,177,3,0,219,584,0,0,1,221,0,0,0,0,7,18,0,1,0,0,0,49,228,0,47,25,46,1 8 | MA-31,21,47,317,222,1,0,2,0,1,4132,13,1,4,3,0,1,1,34,0,0,0,0,217,0,99,0,2,0,0,25,2,1,555,52,8,2 9 | MA-32,49,24,316,2,3,0,0,0,0,769,21,0,24,1,0,1,0,31,2,0,0,0,6,0,0,2,5,0,0,1,51,0,105,9,18,1 10 | MA-33,0,334,0,4,95,0,6,0,0,474,0,0,0,1,0,0,8,58,189,0,0,2,0,0,5,0,211,0,0,0,1288,2,4,297,90,0 11 | MA-34,5,61,448,230,1,0,98,0,0,407,41,1,12,0,1,2,4,28,0,0,0,2,522,0,13,0,3,1,0,3,158,0,329,68,1477,0 12 | MA-35,1,119,2,32,5,0,187,0,0,1512,0,0,3,0,0,0,13,28,3,2,9,95,7,0,48,0,71,1,0,7,655,0,0,5,1508,0 13 | MA-36,748,71,613,25,0,0,2,19,1,895,0,1,143,7,0,0,42,114,0,1,0,12,46,4,68,151,0,0,0,40,16,2,67,324,24,0 14 | MA-37,0,2632,2,329,2,1,2,0,6,67,1,0,0,3,0,2,21,147,0,21,0,504,2,7,0,0,0,0,0,718,111,0,0,83,1,0 15 | MA-38,82,883,103,16,0,0,46,12,5,3638,4,5,3,118,0,1,0,35,0,0,0,2,5,73,3,0,0,1,0,0,2,0,3,0,34,0 16 | MA-39,0,267,310,3,1,1,60,0,0,78,3,3,38,0,0,0,3,12,0,1,0,0,5,192,2,1,0,0,0,3,48,0,40,1,663,0 17 | MA-40,2,19,18,124,0,1,0,0,29,23,0,0,59,3,0,0,79,1642,0,0,29,0,4,0,2,0,7,1,0,7,330,0,1,30,687,0 18 | MA-41,38,34,52,0,0,0,0,55,0,3,1,0,170,27,0,0,1,42,0,0,0,1,4,0,15,1,0,1,0,14,778,1,1,1,4,0 19 | MA-42,8,816,8,36,0,0,238,0,7,7642,2,1,9,1,0,0,0,645,0,0,0,0,217,9,93,3,0,0,0,0,6,0,13,2,103,0 20 | MA-43,136,8,304,0,2,0,3,97,0,377,9,0,0,0,0,1,4,21,0,0,0,0,7,264,113,173,0,0,0,1,18,2,571,4,20,0 21 | MA-44,1,581,0,0,6,0,0,0,3,1702,0,0,16,0,0,0,2,46,0,0,0,0,4,19,414,0,0,0,0,0,631,0,2,332,77,0 22 | MA-45,0,43,48,14,330,0,1,0,0,31,0,0,0,3,0,0,16,13,0,292,2,87,260,0,128,0,0,0,0,1,3330,0,8,1,65,0 23 | MA-46,5,41,39,33,0,0,92,64,6,416,1,0,2,128,0,0,380,386,0,0,0,0,79,13,109,14,0,0,0,9,5026,0,371,2,18,1 24 | MA-47,0,173,153,7,0,0,0,0,4,70,11,2,0,0,0,0,3,12,0,0,0,0,0,0,21,15,0,1,0,2,107,0,343,109,0,1 25 | MA-48,3,1,117,11,0,0,0,5,0,1338,162,1,17,1,0,2,22,42,0,0,0,0,355,0,38,0,0,0,0,0,111,0,1,904,101,0 26 | MA-84,2,248,90,130,0,0,69,89,0,52,0,0,186,12,0,0,12,3,0,0,0,0,186,0,0,15,0,0,0,0,233,0,3,1,25,0 27 | MA-85,0,9,26,110,7,0,1,0,12,382,0,0,3,102,0,0,4,4,22,0,11,1,214,0,0,0,55,0,0,56,443,0,2,0,1,1 28 | MA-86,2,12,118,0,325,0,5,3,13,383,0,0,15,25,1,0,8,293,142,19,0,2,18,0,0,2,4,2,0,51,562,2,3,183,11,0 29 | MA-87,2,120,908,5,0,0,533,290,0,22,18,0,1,2,0,1,6,6,0,0,0,0,28,0,44,21,0,1,0,225,3,0,2,5,185,0 30 | MA-88,0,617,218,9,0,0,1,0,0,263,1,0,0,0,0,2,3,23,0,0,0,0,3,0,4,0,0,0,0,0,155,0,19,101,1687,0 31 | MA-89,5,318,15,28,0,0,0,344,1,757,9,0,221,13,0,0,1,8,0,0,0,0,18,0,5,2,0,0,0,0,3,0,529,0,102,0 32 | MA-90,0,5,188,1091,14,0,2,0,5,3,41,0,24,439,0,0,44,353,9,0,0,0,582,0,26,2,0,1,0,2,6,0,12,5,2462,1 33 | MA-91,1,74,33,11,624,0,1,0,136,93,2,0,649,464,0,7,2,75,163,1,32,0,5,0,8,0,0,0,0,1,5,0,1,170,6,0 34 | MA-92,0,71,70,21,2,0,94,0,0,382,137,0,2,1,0,0,1,65,0,0,1,0,14,2,0,0,0,0,0,27,0,0,17,2,294,0 35 | MA-93,0,36,67,99,0,0,2,1,1,17,36,0,24,1,0,0,0,8,0,0,0,0,333,116,16,12,0,1,0,1,11,0,14,18,34,2 36 | MA-95,0,721,156,10,0,0,19,1,0,1942,6,0,3,0,0,0,202,240,1,0,1,0,2,0,11,12,1,0,0,0,10,0,29,35,1026,0 37 | MD-10,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,2,3,0,1,0,0,0 38 | MD-11,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0 39 | MD-12,0,23,11,6,1,0,0,0,3,3,0,1,1,6,1,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,13,0,0,4,4,1 40 | MD-13,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,1,0 41 | MD-3,1,3,1,0,0,0,0,0,8,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0 42 | MD-4,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 43 | MD-5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 44 | MD-7,0,2,2,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0 45 | MD-8,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 46 | MD-9,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 47 | PA-26,0,158,88,60,0,0,0,0,0,72,1,1,1,20,699,0,0,28,0,0,0,0,591,0,0,0,0,0,0,66,80,2,458,12,21,0 48 | PA-27,0,4,21,1,0,0,1,0,0,3,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,2,10,0 49 | PA-28,1,9,707,21,0,1,0,1,1,51,0,1,154,112,6,2,1,85,1,0,0,0,4,0,0,0,0,0,0,101,18,8,4,105,52,0 50 | PA-29,0,8,15,0,0,0,0,0,1,11,0,0,21,65,11,8,0,1,0,0,0,0,8,0,0,1,0,0,0,52,2,9,25,273,1462,0 51 | PA-31,0,11,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,2,0 52 | PA-32,0,48,73,2,0,0,0,0,0,158,0,0,1,7,2,1,0,128,0,0,0,0,3,0,0,5,0,0,0,0,7,0,3,6,277,0 53 | PA-38,0,2830,37,435,1,0,3,0,1,3209,0,4,47,19,71,0,1,152,0,0,0,2,2,0,2,4,0,0,0,133,150,0,319,182,43,2 54 | PA-39,0,162,1,4,0,0,0,0,0,55,0,1,62,668,93,167,0,333,0,0,0,0,8,0,0,4,0,0,0,774,18,1,0,17,1038,1 55 | PA-40,0,901,21,6,0,0,0,0,0,238,0,2,780,1,1,1,0,147,0,0,0,0,4,0,0,5,2,0,0,3,2,0,17,6,7,0 56 | PA-41,0,151,111,3,0,0,0,0,0,23,0,0,17,38,84,175,0,18,0,0,0,0,215,0,0,16,0,0,0,5,238,0,27,71,9,0 57 | PA-42,0,592,72,150,0,0,0,0,0,957,0,1,6,4,0,106,0,41,0,1,0,0,15,0,0,50,0,0,0,6,119,4,0,2084,39,0 58 | PA-43,0,43,7,87,1,0,0,1,0,61,0,0,10,1,2,1,0,98,2,0,0,0,521,0,0,0,0,0,0,0,5,0,2,1,25,0 59 | PA-46,1,1308,64,23,0,0,0,0,0,487,0,0,0,26,6,1,0,11,0,0,0,1,19,0,1,0,0,0,0,71,197,0,3,1,2,0 60 | PA-47,0,243,12,2,1,0,1,0,0,55,0,0,171,22,6,3,0,60,0,0,0,0,12,1,0,10,0,0,0,9,28,1,8,8,757,0 61 | PA-48,0,44,9,9,0,0,8,0,0,464,4,0,969,3,401,0,0,50,0,0,0,0,9,0,0,11,0,0,0,11,258,0,5,9,74,0 62 | PA-49,0,14,343,5,1,0,0,0,0,160,0,1,6,13,46,6,0,3,1,0,0,0,14,0,0,0,0,0,0,32,9,0,1,1,1052,0 63 | PA-51,0,39,0,77,0,0,0,0,0,177,0,0,252,19,1107,2,0,11,0,0,1,0,23,0,0,42,0,0,1,2,30,0,0,13,471,0 64 | PA-52,2,55,5,8,0,0,0,0,0,14,1,0,51,47,90,11,1,32,0,0,0,0,31,0,0,5,0,1,0,0,598,0,0,6,237,0 65 | PA-53,0,421,27,87,0,0,0,0,0,11,0,0,52,161,0,53,0,50,0,0,0,0,206,0,0,14,0,3,0,4,36,4,1,113,97,0 66 | PA-54,0,2,1,9,0,0,0,0,0,23,0,0,2,0,0,1,0,1,0,0,0,2,1,0,0,0,0,0,0,0,15,0,0,2,3,1 67 | PA-55,0,197,5,370,0,0,0,0,0,1023,0,0,73,2,7,4,0,29,0,0,0,0,82,0,0,4,0,1,0,0,829,0,33,14,4,0 68 | PA-57,0,22,2,185,2,0,0,0,0,160,0,0,25,32,1,3,0,23,1,0,0,0,28,1,0,3,0,0,0,5,1,0,326,58,40,0 69 | PA-58,0,173,60,122,0,0,0,0,3,24,0,1,7,25,43,389,0,116,0,0,0,0,0,0,0,3,1,0,0,119,33,0,5,3,10,1 70 | PA-59,0,223,83,27,1,0,0,0,1,917,0,0,7,38,0,16,0,793,0,0,0,1,0,0,0,0,0,0,0,31,65,0,289,20,2,0 71 | PA-61,0,32,85,30,0,0,2,0,1,17,0,0,37,26,0,14,0,27,0,0,0,0,1,0,0,1,0,0,0,60,42,0,19,0,175,0 72 | PA-62,0,8,0,439,0,0,1,0,0,14,0,0,6,16,3,107,0,71,0,0,0,0,7,0,0,4,0,0,0,11,10,0,0,14,10,0 73 | PA-63,0,254,18,392,0,0,1,2,0,37,0,1,144,121,16,8,0,37,0,1,1,0,96,0,1,3,0,0,0,42,356,0,3,500,1,0 74 | PA-64,0,4357,2,4,0,0,0,0,2,14,0,1,11,57,7,17,0,1,0,0,0,0,2,0,0,13,0,0,0,0,199,0,40,0,4,0 75 | PA-65,0,22,2,6,0,1,0,0,0,69,0,0,989,94,175,15,0,2,0,0,0,0,10,0,0,5,2,0,0,1,29,0,2,0,288,1 76 | PA-68,0,12,2,49,0,0,0,0,0,1939,0,1,3,3,80,21,1,14,0,0,0,0,21,0,0,320,0,0,0,46,72,1,0,23,13,2 77 | PA-70,0,27,44,9,0,2,2,0,1,18,0,1,19,86,6,75,0,3,0,0,0,0,13,1,0,58,0,0,0,161,1,5,0,105,29,0 78 | PA-71,0,162,2,5,0,0,0,0,1,58,0,0,97,680,173,0,1,159,0,0,0,0,465,0,0,2,0,0,0,7,22,0,0,1,303,0 79 | PA-73,0,6,22,101,0,0,0,2,0,10,0,0,1,57,0,1,2,7,0,0,0,0,2,1,0,22,0,0,0,8,12,0,0,0,4,0 80 | PA-74,1,168,15,0,0,0,0,0,8,179,0,1,36,4,0,2,1,147,0,0,0,0,1,0,0,5,3,1,0,168,447,3,1,3,257,0 81 | PA-77,0,542,1,3,0,0,1,0,2,1129,0,0,0,5,0,8,0,12,0,0,0,0,42,0,0,0,0,0,0,422,89,11,0,0,64,0 82 | PD-1,0,1,8,4,0,3088,0,1,4,15,0,6152,2,0,3,0,0,1,0,0,0,0,5,0,0,0,0,203,0,2,4,294,1,5,14,478 83 | PD-14,0,1,0,2,0,8,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,4,0,1,2,17 84 | PD-16,0,1,5,2,0,9,0,1,4,3,0,40,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,5,0,1,1,15,0,1,6,13 85 | PD-17,0,1,3,0,0,20,0,0,0,2,0,54,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,13,0,1,2,9,0,2,2,21 86 | PD-18,0,3,0,0,0,3051,0,0,0,6,0,6048,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,180,0,0,1,265,0,1,4,425 87 | PD-20,0,3,0,2,0,1,0,0,0,2,0,0,1,0,1,0,0,2,0,0,0,0,2,0,0,2,0,2116,0,2,2,3367,1,0,0,5803 88 | PD-5,0,2,0,0,0,1,1,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,1,0,0,0,6 89 | PD-7,4,2,0,0,1,2,0,0,0,2,0,2,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,3,1,0,0,1 90 | PD-8,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,4 91 | PD-9,0,17,0,0,0,4,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,2,6,0,0,3,10 92 | PDD-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 93 | PDD-10,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,1,0,0,0 94 | PDD-11,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0 95 | PDD-12,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,15,0,0,0,0,0,0,0 96 | PDD-13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23,0,0,0,0,0,0,0 97 | PDD-14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,58,0,0,0,0,0,0,0 98 | PDD-15,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5742,0,0,0,0,0,0,0 99 | PDD-16,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 100 | PDD-17,0,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0 101 | PDD-18,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 102 | PDD-19,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 103 | PDD-2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0 104 | PDD-20,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 105 | PDD-21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 106 | PDD-22,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 107 | PDD-23,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 108 | PDD-24,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 109 | PDD-25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 110 | PDD-26,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 111 | PDD-27,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 112 | PDD-28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 113 | PDD-29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 114 | PDD-3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0 115 | PDD-30,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 116 | PDD-31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 117 | PDD-32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 118 | PDD-33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2 119 | PDD-34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 120 | PDD-35,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0 121 | PDD-36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 122 | PDD-37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 123 | PDD-38,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 124 | PDD-39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 125 | PDD-4,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 126 | PDD-40,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 127 | PDD-41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 128 | PDD-42,0,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 129 | PDD-43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 130 | PDD-44,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 131 | PDD-45,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0 132 | PDD-46,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 133 | PDD-47,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 134 | PDD-48,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0 135 | PDD-49,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 136 | PDD-5,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 137 | PDD-50,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 138 | PDD-51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 139 | PDD-52,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 140 | PDD-53,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 141 | PDD-54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 142 | PDD-55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0 143 | PDD-56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 144 | PDD-57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 145 | PDD-58,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0 146 | PDD-59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 147 | PDD-6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0 148 | PDD-60,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 149 | PDD-61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 150 | PDD-62,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 151 | PDD-63,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 152 | PDD-64,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 153 | PDD-65,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 154 | PDD-66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0 155 | PDD-67,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 156 | PDD-68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0 157 | PDD-69,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 158 | PDD-7,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 159 | PDD-70,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 160 | PDD-71,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0 161 | PDD-72,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 162 | PDD-73,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 163 | PDD-74,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 164 | PDD-75,0,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 165 | PDD-76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0 166 | PDD-77,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0 167 | PDD-78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 168 | PDD-79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 169 | PDD-8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 170 | PDD-80,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 171 | PDD-81,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 172 | PDD-82,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1 173 | PDD-83,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 174 | PDD-84,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 175 | PDD-85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0 176 | PDD-86,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 177 | PDD-87,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 178 | PDD-88,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0 179 | PDD-89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 180 | PDD-9,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 181 | PDD-90,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 182 | PDD-91,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 183 | PDD-92,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0 184 | PDD-93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0 185 | PDD-94,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0 186 | PDD-95,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 187 | PDD-96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 188 | -------------------------------------------------------------------------------- /data/CRC/input/character_matrix.csv: -------------------------------------------------------------------------------- 1 | ,TSHZ3-chr19-31768568,TP53-chr17-7577548,TOX-chr8-59851979,STRN-chr2-37105068,SPEN-chr1-16258997,SPEN-chr1-16202934,PTPRD-chr9-10074741,PRKCB-chr16-24230186,PIK3CG-chr7-106508397,NRAS-chr1-115258747,NR4A3-chr9-102595561,NR3C2-chr4-149357051,MYH11-chr16-15917267,MN1-chr22-28193785,LRP1B-chr2-141609298,LINGO2-chr9-29178757,LINGO2-chr9-29123273,LINGO2-chr9-29117698,LINGO2-chr9-28778389,LINGO2-chr9-28064983,LAMB4-chr7-107732769,IL7R-chr5-35876645,IL21R-chr16-27448836,HELZ-chr17-65119071,FUS-chr16-31193877,FHIT-chr3-60412480,F8-chrX-154157378,EPHB6-chr7-142566125,CIITA-chr16-11002009,CHN1-chr2-175779934,CDK4-chr12-58144548,ATR-chr3-142269072,ATP7B-chr13-52534322,APC-chr5-112175328,APC-chr5-112164646,ALK-chr2-29416591,cluster_id 2 | MA-25,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,-1,0,-1,-1,0,0,0,-1,0,0,0,0 3 | MA-26,0,0,0,0,0,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,0,0,-1,0,0,0,0,0,0,0,-1,0,0,0,0 4 | MA-27,0,1,1,1,0,0,0,0,0,1,0,-1,0,1,0,-1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,0,3 5 | MA-28,0,1,-1,0,1,0,0,0,1,1,0,0,1,0,0,0,1,1,1,0,1,0,1,0,0,0,1,0,-1,0,1,0,0,0,1,0,2 6 | MA-29,0,1,1,-1,0,0,0,0,-1,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,-1,1,0,2 7 | MA-30,0,1,1,1,0,0,-1,1,0,1,-1,-1,1,1,0,0,0,1,0,0,0,0,0,0,0,-1,0,0,0,1,1,0,0,1,1,0,3 8 | MA-31,0,1,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,1,-1,0,3 9 | MA-32,1,1,1,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0,-1,0,0,0,0,1,0,1,1,1,0,3 10 | MA-33,0,1,0,0,1,-1,1,0,-1,1,0,0,0,0,0,0,1,1,1,0,-1,0,-1,0,-1,-1,1,0,0,-1,1,0,0,1,1,0,2 11 | MA-34,0,1,1,1,0,0,1,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,1,0,1,0,-1,0,-1,-1,1,0,1,0,1,0,3 12 | MA-35,0,1,0,1,0,0,1,0,0,1,0,0,0,-1,0,0,1,1,0,0,0,1,-1,0,1,0,1,0,0,-1,1,0,0,0,1,0,2 13 | MA-36,1,1,1,1,0,0,0,1,0,1,0,0,1,0,-1,0,1,1,0,0,0,1,1,0,1,1,0,0,0,1,1,0,0,1,0,0,3 14 | MA-37,-1,1,0,1,0,0,0,0,1,1,0,0,0,-1,-1,0,1,1,0,1,0,1,0,1,0,0,0,0,0,1,1,0,0,1,-1,0,2 15 | MA-38,1,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,1,1,0,-1,0,0,0,-1,0,0,0,-1,1,0,3 16 | MA-39,-1,1,1,0,0,0,1,0,0,1,0,0,1,0,-1,-1,-1,1,0,0,0,0,0,1,-1,0,0,0,0,0,1,0,0,0,1,0,3 17 | MA-40,0,1,0,1,0,0,0,0,1,1,0,0,1,-1,0,0,1,1,0,0,1,0,-1,0,0,0,0,0,0,0,1,0,0,0,1,0,2 18 | MA-41,1,1,1,0,0,0,0,1,0,-1,0,0,1,1,0,-1,0,1,0,0,-1,0,-1,-1,1,-1,0,0,0,0,1,0,0,0,-1,0,3 19 | MA-42,1,1,1,1,0,0,1,0,0,1,0,-1,1,0,0,-1,0,1,0,0,0,0,1,1,1,-1,0,0,-1,0,0,0,0,-1,1,0,3 20 | MA-43,1,1,1,-1,0,0,0,1,0,1,0,0,0,-1,0,0,0,1,0,0,0,0,1,1,1,1,-1,0,0,0,1,0,1,0,1,0,3 21 | MA-44,0,1,-1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,-1,1,1,0,0,0,0,0,1,0,0,1,0,0,3 22 | MA-45,0,1,1,0,1,-1,0,0,0,1,0,0,0,0,0,0,1,1,-1,1,0,1,1,0,1,-1,0,0,0,0,1,0,0,0,1,0,2 23 | MA-46,0,1,1,1,0,0,1,1,0,1,-1,0,-1,1,0,0,1,1,0,0,0,-1,1,1,1,1,0,0,0,0,1,0,1,0,1,0,3 24 | MA-47,0,1,1,0,0,0,0,0,0,1,1,0,0,-1,0,-1,0,1,0,0,-1,0,-1,0,1,1,0,0,0,0,1,0,1,1,0,0,3 25 | MA-48,0,-1,1,1,0,0,0,0,0,1,1,0,1,0,0,0,1,1,0,-1,-1,0,1,0,1,0,0,0,0,0,1,0,0,1,0,0,3 26 | MA-84,-1,1,1,1,0,0,1,1,-1,1,0,-1,1,1,0,0,1,-1,0,0,0,0,1,0,-1,1,0,0,0,0,1,0,0,0,1,0,3 27 | MA-85,0,-1,1,1,0,0,-1,0,1,1,0,0,0,1,0,0,-1,-1,1,-1,1,0,1,0,0,0,1,0,0,1,1,0,0,0,0,0,2 28 | MA-86,0,1,1,0,1,0,1,0,0,1,0,0,1,1,0,0,0,1,1,1,0,0,1,0,0,0,0,0,0,1,1,0,0,1,1,0,2 29 | MA-87,0,1,1,-1,0,0,1,1,0,1,1,0,0,0,-1,0,1,-1,0,0,0,0,1,-1,1,1,0,0,0,1,-1,0,0,0,1,0,3 30 | MA-88,-1,1,1,1,0,0,0,0,-1,1,-1,0,-1,-1,0,0,0,1,0,0,0,0,-1,0,0,-1,0,0,0,0,1,0,1,0,1,0,3 31 | MA-89,0,1,1,1,0,0,0,1,0,1,1,0,1,1,0,0,0,-1,0,0,0,0,1,0,1,0,0,0,0,-1,0,0,1,0,1,0,3 32 | MA-90,-1,-1,1,1,0,0,0,0,0,-1,1,0,1,1,0,0,1,1,1,0,0,0,1,0,1,0,0,-1,0,0,1,0,0,0,1,0,3 33 | MA-91,0,1,1,1,1,0,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1,-1,-1,0,0,0,0,0,0,-1,0,0,0,1,0,0,2 34 | MA-92,0,1,1,1,0,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,-1,1,0,0,-1,0,0,0,1,0,0,0,0,1,0,3 35 | MA-93,0,1,1,1,0,0,0,-1,0,1,1,0,0,0,0,0,-1,-1,0,0,-1,0,1,1,1,1,0,0,0,-1,0,0,1,1,1,0,3 36 | MA-95,0,1,1,1,0,0,1,0,0,1,0,0,-1,-1,0,0,1,1,0,0,0,-1,0,0,1,1,0,0,0,0,0,0,1,0,1,0,3 37 | MD-10,0,0,0,0,0,0,0,0,0,0,-1,0,-1,0,-1,0,0,0,0,0,0,-1,-1,-1,-1,0,-1,-1,0,0,0,0,0,0,0,0,0 38 | MD-11,-1,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,-1,0,0,0,0,0,0 39 | MD-12,0,1,0,-1,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,-1,0,0,0,0,0,-1,-1,0,0,0,1,0,0,0,0,0,0 40 | MD-13,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,-1,0,0,0,0,-1,0,-1,0,0,-1,0,-1,0,0,-1,0,0,0,0 41 | MD-3,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,-1,0,-1,-1,0,-1,-1,-1,0,0,0,0,0,0,0,0 42 | MD-4,0,0,-1,-1,0,0,-1,-1,0,0,0,-1,0,0,0,-1,-1,-1,0,-1,0,0,0,-1,0,-1,-1,-1,0,-1,0,0,0,-1,-1,0,0 43 | MD-5,0,0,0,0,0,0,-1,0,0,0,-1,0,-1,0,0,-1,-1,0,-1,0,0,-1,0,0,-1,0,-1,-1,0,0,0,0,-1,0,0,0,0 44 | MD-7,0,0,0,0,0,-1,0,-1,0,0,0,0,0,-1,-1,0,-1,0,-1,0,0,-1,-1,-1,-1,0,0,-1,-1,-1,0,0,0,0,0,0,0 45 | MD-8,-1,0,0,-1,0,0,0,0,0,0,-1,-1,0,0,-1,-1,-1,-1,0,-1,0,-1,0,0,-1,-1,-1,0,-1,-1,0,0,-1,0,0,0,0 46 | MD-9,0,0,-1,-1,0,0,0,0,-1,0,0,0,0,0,0,-1,0,0,0,-1,-1,0,0,0,-1,0,-1,-1,-1,-1,0,0,0,0,0,0,0 47 | PA-26,0,1,1,1,0,0,0,0,0,1,0,-1,0,1,1,-1,0,1,0,0,0,0,1,0,0,-1,0,0,0,1,1,0,1,1,0,0,1 48 | PA-27,0,0,1,0,0,0,-1,-1,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,-1,-1,0,0,0,0,0,0,0 49 | PA-28,-1,1,1,1,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,-1,0,-1,0,0,0,-1,0,0,1,1,0,0,1,1,0,1 50 | PA-29,0,-1,1,-1,0,0,0,0,0,1,0,0,1,1,0,1,0,-1,0,0,-1,-1,1,-1,0,0,0,0,-1,1,0,0,1,1,1,0,1 51 | PA-31,-1,1,-1,-1,-1,-1,-1,-1,-1,1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1 52 | PA-32,0,1,1,0,0,0,0,0,0,1,0,-1,0,-1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1 53 | PA-38,0,1,1,1,0,0,0,0,0,1,0,0,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1 54 | PA-39,0,1,0,0,0,0,0,0,0,1,0,-1,1,1,0,1,0,1,0,0,-1,0,1,0,0,0,0,-1,0,1,1,0,0,1,1,0,1 55 | PA-40,0,1,1,0,0,0,0,0,0,1,0,0,1,-1,0,0,0,1,0,0,0,0,-1,0,0,0,0,0,-1,0,0,0,1,0,0,0,1 56 | PA-41,0,1,1,0,0,0,-1,0,-1,1,0,0,1,1,1,1,0,1,0,0,0,0,1,0,0,0,0,-1,0,0,1,0,1,1,1,0,1 57 | PA-42,-1,1,1,1,0,-1,0,0,0,1,0,0,0,-1,0,1,0,1,0,0,-1,-1,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1 58 | PA-43,0,1,0,1,0,0,0,0,-1,1,0,0,1,0,0,0,0,1,0,0,-1,0,1,0,0,-1,0,0,0,-1,-1,0,0,0,1,0,1 59 | PA-46,0,1,1,1,0,0,0,0,0,1,0,0,-1,1,0,-1,0,0,0,0,0,0,1,-1,0,-1,-1,0,0,1,1,0,-1,0,-1,0,1 60 | PA-47,0,1,0,0,0,0,0,0,0,1,0,0,1,1,1,-1,0,1,-1,-1,0,0,0,-1,0,1,0,0,0,-1,1,0,0,1,1,0,1 61 | PA-48,0,1,1,1,0,0,1,0,0,1,0,0,1,-1,1,0,0,1,0,0,0,0,1,0,0,1,-1,0,0,1,1,0,0,1,1,0,1 62 | PA-49,0,1,1,-1,0,0,0,0,0,1,0,0,1,1,1,1,-1,-1,0,-1,0,-1,1,0,0,0,0,0,0,1,1,0,0,0,1,0,1 63 | PA-51,0,1,0,1,0,0,0,0,0,1,0,0,1,1,1,-1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,0,1 64 | PA-52,0,1,-1,-1,0,0,0,0,0,1,0,0,1,1,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1 65 | PA-53,0,1,1,1,0,0,0,0,-1,1,0,0,1,1,-1,1,0,1,0,0,0,0,1,0,0,0,-1,0,0,0,1,0,0,1,1,0,1 66 | PA-54,0,-1,0,0,0,0,0,-1,-1,1,0,0,0,0,0,-1,0,0,0,-1,-1,0,0,-1,0,-1,-1,0,0,0,1,0,0,0,0,0,0 67 | PA-55,0,1,0,1,0,0,0,0,0,1,0,0,1,-1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,1,-1,0,1 68 | PA-57,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,-1,0,1,0,0,0,-1,1,0,0,-1,0,0,0,0,-1,0,1,1,1,0,1 69 | PA-58,0,1,1,1,0,0,0,0,0,1,0,0,1,1,1,1,0,1,0,0,-1,-1,-1,0,0,-1,0,-1,0,1,0,0,0,-1,1,0,1 70 | PA-59,0,1,1,1,0,0,0,0,0,1,0,-1,0,1,0,0,0,1,0,0,0,0,-1,-1,0,0,0,-1,0,1,1,0,1,1,0,0,1 71 | PA-61,0,1,1,1,0,0,0,-1,0,1,0,0,1,1,0,1,0,1,0,0,-1,0,-1,0,0,0,0,0,0,1,1,0,0,0,1,0,1 72 | PA-62,0,-1,0,1,0,0,0,0,0,1,0,0,1,1,0,1,0,1,0,0,0,0,1,0,-1,0,0,0,0,1,1,0,-1,1,1,0,1 73 | PA-63,0,1,0,1,0,0,0,0,0,1,-1,0,1,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,-1,1,1,0,0,1,0,0,1 74 | PA-64,0,1,0,0,0,0,0,0,-1,1,0,0,1,1,0,1,0,0,0,0,0,0,-1,0,0,1,0,0,0,0,1,0,1,-1,0,0,1 75 | PA-65,0,1,-1,1,0,0,0,0,-1,1,0,0,1,1,1,1,0,0,0,0,0,0,1,0,0,1,0,-1,0,0,1,0,0,0,1,0,1 76 | PA-68,0,1,-1,1,0,0,0,0,0,1,0,0,-1,-1,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0,0,1,1,0,1 77 | PA-70,0,1,1,0,0,0,0,0,0,1,0,0,1,1,1,1,0,0,0,0,-1,0,1,-1,0,1,0,0,0,1,0,0,0,1,1,0,1 78 | PA-71,0,1,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,-1,0,1,0,1 79 | PA-73,0,-1,1,1,0,0,0,0,0,-1,0,0,-1,1,0,-1,0,0,0,0,-1,-1,-1,0,0,1,0,0,0,1,1,0,0,0,0,0,1 80 | PA-74,0,1,1,0,0,0,0,0,0,1,0,0,1,0,0,-1,0,1,0,0,0,0,0,0,0,1,0,0,0,1,1,0,-1,0,1,0,1 81 | PA-77,0,1,0,-1,0,0,0,0,0,1,0,0,0,0,0,-1,-1,1,0,0,0,-1,1,-1,-1,0,0,-1,0,1,1,0,0,0,1,0,1 82 | PD-1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0 83 | PD-14,-1,0,0,-1,0,1,-1,0,0,0,0,0,0,0,0,-1,-1,0,0,-1,0,0,-1,-1,-1,-1,-1,0,-1,-1,0,0,-1,0,0,1,0 84 | PD-16,0,0,0,-1,0,1,-1,0,0,0,0,1,-1,0,0,-1,-1,0,0,0,0,-1,0,-1,0,0,0,1,-1,-1,0,1,-1,0,0,0,0 85 | PD-17,0,0,0,-1,0,1,0,0,-1,0,-1,1,-1,0,0,-1,-1,0,0,-1,0,0,-1,0,0,-1,-1,1,-1,0,0,0,0,0,0,1,0 86 | PD-18,0,-1,0,0,0,1,0,0,0,0,0,1,0,-1,0,-1,0,0,0,0,-1,0,0,-1,0,-1,-1,1,0,-1,0,1,0,0,0,1,0 87 | PD-20,0,-1,0,0,0,0,0,0,-1,0,-1,0,0,-1,0,0,0,0,-1,0,0,0,-1,-1,-1,-1,0,1,0,-1,0,1,-1,0,-1,1,0 88 | PD-5,0,0,0,0,0,0,0,0,-1,0,-1,0,-1,-1,0,0,0,0,0,-1,-1,0,-1,-1,-1,-1,0,0,0,-1,0,0,0,0,-1,0,0 89 | PD-7,0,0,0,-1,0,0,0,0,-1,0,0,-1,-1,0,0,-1,-1,0,-1,-1,0,0,-1,0,-1,0,-1,0,-1,-1,0,0,0,0,-1,0,0 90 | PD-8,-1,0,-1,-1,0,0,-1,0,0,0,0,-1,-1,-1,0,0,0,-1,0,0,0,-1,-1,0,-1,0,0,0,-1,0,0,1,0,0,-1,0,0 91 | PD-9,-1,0,0,-1,0,0,-1,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,-1,0,0,-1,-1,0,-1,-1,0,1,-1,0,0,0,0 92 | PDD-1,0,0,-1,0,0,-1,0,0,0,0,0,0,-1,0,0,0,-1,-1,0,-1,0,0,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0 93 | PDD-10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 94 | PDD-11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,0,0,0,0,-1,0,0,0,1,0,0,0,0,0,0,0,0 95 | PDD-12,0,0,-1,0,0,0,0,0,0,0,0,0,0,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 96 | PDD-13,-1,0,0,-1,0,0,-1,0,-1,0,0,0,0,-1,-1,0,0,0,0,0,-1,0,-1,-1,-1,-1,0,-1,1,-1,0,0,-1,-1,0,-1,0 97 | PDD-14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,-1,1,0,0,0,0,0,0,0,0 98 | PDD-15,0,0,-1,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,-1,-1,-1,-1,0,-1,0,0,-1,0,0,1,0,0,0,-1,0,0,0,0 99 | PDD-16,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,-1,0,0,0,0,0,0,0,0,0,-1,0,0,0 100 | PDD-17,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,-1,0,0,0 101 | PDD-18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 102 | PDD-19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0 103 | PDD-2,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 104 | PDD-20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 105 | PDD-21,-1,-1,-1,-1,0,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,-1,-1,-1,-1,-1,-1,-1,-1,0,-1,-1,-1,-1,-1,0 106 | PDD-22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0 107 | PDD-23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 108 | PDD-24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0 109 | PDD-25,0,-1,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 110 | PDD-26,0,0,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 111 | PDD-27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 112 | PDD-28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,0,0,0,0,0,0,0,-1,-1,-1,0,0,0,0,0,0,-1,0 113 | PDD-29,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0 114 | PDD-3,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 115 | PDD-30,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0 116 | PDD-31,-1,0,0,0,0,0,0,0,0,0,0,-1,0,-1,0,0,0,0,0,0,-1,0,0,-1,-1,-1,-1,-1,-1,0,0,0,-1,0,0,0,0 117 | PDD-32,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 118 | PDD-33,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,-1,-1,-1,0,0,0,-1,0,-1,0,0,0,0,-1,0,0,-1,-1,0,0 119 | PDD-34,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0 120 | PDD-35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0 121 | PDD-36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0 122 | PDD-37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 123 | PDD-38,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,0,0,0,-1,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 124 | PDD-39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 125 | PDD-4,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0 126 | PDD-40,0,0,0,-1,0,-1,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,0,0,0,0,0 127 | PDD-41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 128 | PDD-42,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,-1,0,-1,0,-1,0,0,0,0,0,-1,0,0,0,0,0,-1,-1,0,0 129 | PDD-43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 130 | PDD-44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0 131 | PDD-45,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 132 | PDD-46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 133 | PDD-47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0 134 | PDD-48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 135 | PDD-49,0,0,0,0,0,0,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 136 | PDD-5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 137 | PDD-50,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0 138 | PDD-51,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,-1,0,0,0,0,0,0,0,-1,0,-1,0,0 139 | PDD-52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,0,0,0,0,0,-1,0,-1,0,0,0,0,0,0,0,-1,0,0 140 | PDD-53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0 141 | PDD-54,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0 142 | PDD-55,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,-1,-1,0,0,0,-1,0,0,-1,0,0,0,-1,0,0,0,0,0,0,0,0 143 | PDD-56,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,-1,0,0,0,0,0,0,0,0,0,-1,0,0,0,-1,0,0,0,0,0,0,0,0 144 | PDD-57,0,0,0,0,0,0,0,0,0,0,-1,0,0,-1,0,0,0,0,0,0,0,0,0,0,-1,-1,0,0,0,0,0,0,0,0,0,0,0 145 | PDD-58,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0 146 | PDD-59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0 147 | PDD-6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 148 | PDD-60,0,-1,0,-1,0,-1,-1,0,0,0,0,0,-1,-1,0,-1,-1,-1,0,0,-1,0,-1,0,0,-1,-1,0,0,0,0,-1,0,-1,0,0,0 149 | PDD-61,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,-1,0,0,0,0,-1,0,0,-1,0,0,0,0,-1,0,0,0,0,0,0,0,0,0 150 | PDD-62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,-1,-1,0,0,0,0,0,0,0,0,0,0,-1,0,0 151 | PDD-63,-1,0,0,0,0,-1,-1,0,0,0,0,0,0,-1,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,0 152 | PDD-64,0,0,0,0,0,-1,-1,0,0,0,0,0,0,-1,0,-1,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,-1,-1,0,0 153 | PDD-65,0,0,-1,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 154 | PDD-66,-1,-1,-1,0,-1,-1,-1,-1,0,-1,0,0,-1,-1,0,-1,-1,-1,-1,0,0,0,-1,-1,-1,0,0,0,-1,0,-1,-1,-1,0,0,0,0 155 | PDD-67,-1,-1,0,0,-1,0,0,-1,0,0,0,0,-1,-1,0,-1,0,0,0,0,0,0,-1,0,-1,0,0,-1,0,0,-1,0,0,0,0,0,0 156 | PDD-68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 157 | PDD-69,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0 158 | PDD-7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0 159 | PDD-70,0,0,0,0,0,-1,-1,0,0,0,-1,0,-1,0,0,0,-1,-1,0,0,-1,0,0,0,-1,0,0,0,-1,0,0,0,0,0,0,0,0 160 | PDD-71,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,-1,0,0,0,0,-1,0,-1,0,0,0,0,0,0,0,0,0,0 161 | PDD-72,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,0,-1,0,-1,0,0,0,0,0,0,0,0,0,0,0 162 | PDD-73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,0,0,0,-1,0,0,0,-1,0,0,0,0 163 | PDD-74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 164 | PDD-75,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,-1,0,0,0,0,0,-1,0,0,0,0,0,0,0 165 | PDD-76,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0 166 | PDD-77,0,-1,0,-1,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 167 | PDD-78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0 168 | PDD-79,0,0,0,0,0,-1,0,0,0,0,0,0,0,-1,0,0,0,0,0,-1,-1,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0 169 | PDD-8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 170 | PDD-80,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 171 | PDD-81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,-1,0,0,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0 172 | PDD-82,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,0,0,0,0,0,0,0,0,0,0 173 | PDD-83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,-1,0,-1,0,-1,-1,0,0,0,0,0,0,0 174 | PDD-84,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 175 | PDD-85,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,-1,0,0,-1,0,-1,0,-1,0,0,0,0,0,0,0 176 | PDD-86,0,0,0,0,-1,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 177 | PDD-87,0,-1,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,-1,-1,0,0,-1,0,-1,0,0,-1,-1,-1,0,0,0,0,0,0,0,0,0 178 | PDD-88,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,-1,-1,0,0 179 | PDD-89,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,0,0,0,0,0,0,0,-1,0,0,0,0,0,0 180 | PDD-9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 181 | PDD-90,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,-1,0,0,-1,0,0,-1,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 182 | PDD-91,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0 183 | PDD-92,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 184 | PDD-93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,-1,0,0,0,0,-1,0,0,0,0,0,0,0,0,0 185 | PDD-94,0,-1,0,0,0,0,0,0,0,0,-1,0,0,0,-1,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0 186 | PDD-95,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,0,-1,-1,0,0,0,0 187 | PDD-96,0,-1,0,0,0,0,0,-1,0,0,-1,0,-1,0,0,0,0,0,0,0,-1,-1,0,0,-1,-1,-1,0,0,0,0,0,0,0,0,0,0 188 | -------------------------------------------------------------------------------- /data/CRC/input/total_readcounts.csv: -------------------------------------------------------------------------------- 1 | ,TSHZ3-chr19-31768568,TP53-chr17-7577548,TOX-chr8-59851979,STRN-chr2-37105068,SPEN-chr1-16258997,SPEN-chr1-16202934,PTPRD-chr9-10074741,PRKCB-chr16-24230186,PIK3CG-chr7-106508397,NRAS-chr1-115258747,NR4A3-chr9-102595561,NR3C2-chr4-149357051,MYH11-chr16-15917267,MN1-chr22-28193785,LRP1B-chr2-141609298,LINGO2-chr9-29178757,LINGO2-chr9-29123273,LINGO2-chr9-29117698,LINGO2-chr9-28778389,LINGO2-chr9-28064983,LAMB4-chr7-107732769,IL7R-chr5-35876645,IL21R-chr16-27448836,HELZ-chr17-65119071,FUS-chr16-31193877,FHIT-chr3-60412480,F8-chrX-154157378,EPHB6-chr7-142566125,CIITA-chr16-11002009,CHN1-chr2-175779934,CDK4-chr12-58144548,ATR-chr3-142269072,ATP7B-chr13-52534322,APC-chr5-112175328,APC-chr5-112164646,ALK-chr2-29416591 2 | MA-25,66,843,154,2,1260,689,97,64,61,492,198,1583,118,1373,302,20,65,2,46,52,33,232,19,279,36,3,31,5,3,109,100,199,4,1533,1167,65 3 | MA-26,149,2423,1182,13,2963,8,1,38,97,1129,88,220,117,14,616,26,43,40,14,4,0,1853,45,148,8,94,68,57,163,361,531,18,2,367,456,800 4 | MA-27,81,1441,532,208,753,1359,11,399,1593,65,12,2,650,537,329,9,43,33,161,237,31,90,36,77,11,393,90,151,241,435,1061,67,82,179,54,973 5 | MA-28,12,69,4,199,1131,187,34,885,26,18,210,12,91,60,61,68,432,273,103,318,121,185,20,31,210,45,689,127,2,14,863,450,37,240,188,1087 6 | MA-29,213,559,20,5,73,265,97,481,4,104,543,444,48,16,585,55,40,71,36,109,12,24,26,280,44,40,419,252,16,95,43,781,495,9,80,1092 7 | MA-30,56,107,103,27,104,438,5,102,100,178,5,9,624,594,19,245,163,221,32,35,28,62,184,299,84,4,3843,28,249,104,229,35,915,211,47,328 8 | MA-31,236,52,328,545,5199,233,155,728,427,4143,70,572,451,2404,116,20,30,35,254,129,72,14,224,370,119,113,4489,27,585,45,766,165,895,239,9,4007 9 | MA-32,65,26,614,26,694,1252,944,71,113,774,158,288,42,16,23,16,46,31,46,32,12,140,55,188,148,2,59,71,815,256,95,148,636,40,29,1390 10 | MA-33,94,334,15,43,219,2,44,364,3,474,62,11,331,430,382,27,41,59,212,55,0,265,1,16,9,0,665,1735,1159,0,1295,705,272,444,243,1181 11 | MA-34,124,65,453,273,7892,523,115,51,134,408,235,297,19,121,70,111,30,28,13,59,1916,677,542,81,21,124,7,229,10,6,164,135,2822,974,1481,668 12 | MA-35,802,120,19,68,2056,190,207,212,189,1514,50,63,32,5,28,713,37,28,42,116,353,111,7,219,206,69,82,36,40,7,666,457,1813,2316,1508,11 13 | MA-36,750,72,637,31,4418,66,229,73,840,896,39,25,184,105,8,77,131,115,238,31,13,57,47,16,72,158,38,67,408,133,119,845,800,334,868,1622 14 | MA-37,10,2635,321,354,3926,731,81,452,32,67,274,512,55,4,4,66,127,147,144,43,105,547,17,21,18,34,83,374,308,741,473,109,560,94,1,380 15 | MA-38,574,885,113,647,527,609,53,278,849,3649,28,325,32,118,15,253,31,35,453,148,110,1462,11,74,116,1,890,2432,80,9,195,333,90,2,34,2251 16 | MA-39,4,268,325,28,1715,393,136,197,79,78,50,76,119,327,4,8,10,13,160,894,140,477,69,464,4,216,1238,44,90,296,59,905,1316,767,679,1190 17 | MA-40,2030,20,248,304,137,174,93,1569,50,26,155,96,227,5,568,47,87,1644,26,377,33,94,4,21,17,141,1026,37,27,187,335,350,477,1170,775,858 18 | MA-41,51,35,53,74,6985,33,223,135,87,3,33,62,173,27,31,0,50,42,79,25,0,236,5,2,16,10,216,167,13,155,796,1110,93,120,4,89 19 | MA-42,15,818,29,164,2827,56,300,230,1853,7647,62,8,22,63,44,6,646,645,50,45,206,479,226,23,252,3,560,229,8,22,940,75,1842,10,108,151 20 | MA-43,236,11,305,2,4820,1014,312,127,17,378,91,21,103,8,193,19,17,21,26,126,41,234,24,769,118,173,1,33,15,48,28,108,827,21,30,806 21 | MA-44,365,582,2,40,2227,335,15,544,550,1704,184,124,19,381,154,142,99,46,25,74,28,14,8,33,530,104,109,162,149,83,1200,3894,226,448,923,727 22 | MA-45,65,47,164,259,504,1,199,34,307,31,483,82,99,25,455,39,58,13,5,504,11,115,379,41,349,0,191,135,466,16,5685,1289,1085,19,67,24 23 | MA-46,332,42,178,84,1406,42,166,150,1240,419,2,632,8,774,621,305,394,386,54,163,49,5,80,36,118,17,18,503,361,188,5034,102,1828,66,23,58 24 | MA-47,86,174,163,170,7132,2268,11,222,873,71,32,53,572,0,477,9,12,12,316,63,6,233,2,35,29,102,119,556,16,240,117,1495,469,141,174,411 25 | MA-48,61,4,120,25,900,124,40,2351,67,1340,182,137,22,62,43,128,180,42,45,8,6,61,355,43,65,19,207,223,967,29,680,153,82,1100,1026,1686 26 | MA-84,6,250,91,655,1262,220,84,119,4,54,83,7,422,89,1108,43,13,3,578,18,16,25,227,206,10,33,466,438,248,18,508,412,25,32,74,1864 27 | MA-85,139,9,107,114,410,44,9,26,39,382,78,69,98,791,773,83,6,4,70,1,18,382,214,270,79,123,62,556,342,57,1141,1353,1056,11,112,455 28 | MA-86,558,13,1110,25,1712,1658,13,351,552,388,196,265,31,37,404,23,271,295,161,26,35,124,53,104,48,45,19,168,35,53,573,130,977,213,74,801 29 | MA-87,836,121,909,7,3918,301,693,449,105,22,28,248,13,184,6,99,12,6,52,54,24,264,28,4,65,58,27,22,23,268,3,61,16,74,270,88 30 | MA-88,7,619,223,52,275,767,38,226,1,264,2,1770,3,0,90,17,119,23,66,402,52,155,7,113,558,7,17,71,449,280,161,743,33,1155,1833,740 31 | MA-89,1336,320,42,37,460,149,118,723,220,758,13,33,355,121,32,11,14,9,183,101,32,79,27,32,11,24,1173,360,25,10,63,635,599,151,102,270 32 | MA-90,6,6,216,1099,833,482,161,186,85,3,53,307,26,441,13,139,329,356,89,47,209,712,628,18,161,227,251,9,62,100,33,132,362,1714,2467,5850 33 | MA-91,73,74,100,20,2224,266,60,103,267,93,153,131,727,516,101,35,48,79,330,21,43,2,10,231,96,278,206,63,139,9,443,762,215,183,225,269 34 | MA-92,141,72,148,26,5131,18,258,49,45,382,517,70,29,436,208,15,103,66,66,306,306,7,20,180,27,0,100,17,140,41,3990,1486,1018,194,297,148 35 | MA-93,76,37,150,130,3493,158,252,2,122,17,48,262,392,19,299,60,1,8,91,110,4,33,335,153,100,15,103,30,61,7,224,889,107,53,136,344 36 | MA-95,33,721,161,62,2677,1060,50,18,96,1946,76,27,4,3,85,17,229,240,704,21,326,3,65,27,16,20,2290,20,64,149,155,299,210,588,1030,337 37 | MD-10,794,1773,19,41,768,29,180,313,413,40,2,21,8,123,2,11,34,19,314,26,171,4,9,1,4,23,4,4,42,323,2822,1171,158,457,442,31 38 | MD-11,2,120,547,1519,113,1050,34,604,26,1668,29,11,197,0,90,23,22,49,543,295,34,268,353,67,92,401,16,5,14,4,6,494,545,1835,748,915 39 | MD-12,633,34,139,10,210,620,148,45,673,118,23,979,42,10,31,21,74,54,61,6,18,42,208,122,43,6,5,71,51,16,54,368,841,2971,2065,52 40 | MD-13,356,404,5,367,7844,15,135,133,79,2299,221,850,35,17,57,0,2,7,204,38,245,366,3,510,3,207,114,2,83,6,2815,194,3,25,12,84 41 | MD-3,178,11,48,94,374,460,6,880,1063,392,26,118,13,24,44,34,45,59,2,1,29,1,58,2,1,45,1,9,0,21,18,192,11,16,47,1960 42 | MD-4,290,335,0,8,14,239,0,8,296,477,20,2,30,20,59,0,0,1,31,0,64,123,177,5,61,4,8,2,17,1,461,93,12,8,8,316 43 | MD-5,36,45,26,209,105,1644,6,296,14,44,2,114,5,47,2178,8,8,11,6,24,41,1,23,193,8,129,0,0,144,14,1372,321,1,17,20,62 44 | MD-7,11,237,11,27,75,1,16,1,789,129,50,85,45,9,7,42,3,42,6,71,26,7,0,0,6,26,31,2,4,0,112,250,22,1934,596,2202 45 | MD-8,2,359,103,0,123,48,94,87,52,198,2,1,86,12,6,1,8,5,48,3,11,1,19,23,5,1,10,41,7,7,113,519,2,12,42,91 46 | MD-9,18,1420,1,2,7388,115,120,20,9,250,32,55,468,60,399,0,98,111,30,7,6,11,12,27,3,22,4,9,7,10,141,191,276,60,13,39 47 | PA-26,150,161,159,207,496,452,97,68,54,72,40,3,21,24,873,1,93,38,2730,208,116,20,608,44,82,5,47,12,109,157,100,1894,637,21,1071,267 48 | PA-27,322,38,80,100,53,15,1,7,13,175,30,70,3,13,107,95,32,12,17,86,20,13,101,31,79,678,9,2,9,10,447,201,165,443,291,788 49 | PA-28,9,11,717,97,2654,91,71,23,403,51,454,1779,172,115,97,46,1614,177,508,41,6,121,4,71,26,50,8,163,269,119,29,5901,12,139,57,21 50 | PA-29,103,9,15,8,30,94,32,115,211,11,297,177,25,66,567,17,79,5,16,198,5,4,14,9,67,72,309,301,0,52,41,7120,97,1001,1663,213 51 | PA-31,0,11,0,0,3,3,0,1,1,20,1,1,7,0,0,1,9,4,0,5,0,5,0,0,1,2,1,0,5,2,10,0,7,0,3,9 52 | PA-32,139,51,93,14,322,63,131,1514,156,159,16,10,28,9,77,245,122,128,434,52,71,116,84,65,23,285,229,40,119,41,531,1512,102,260,280,565 53 | PA-38,277,2834,112,1605,135,405,821,40,603,3212,1435,572,259,28,112,19,138,159,1420,20,111,364,11,89,69,68,33,3794,26,137,2376,399,1744,1411,483,4943 54 | PA-39,47,162,34,11,2679,145,62,305,111,57,624,5,88,681,1154,297,690,367,87,467,8,1490,15,81,181,12,141,5,668,777,19,545,922,18,1041,39 55 | PA-40,103,908,29,158,1181,143,43,40,93,239,72,28,951,2,17,223,406,150,160,26,35,12,7,150,124,56,1699,566,6,41,26,1308,110,66,78,4703 56 | PA-41,151,152,125,19,1012,852,7,2086,2,23,101,327,121,40,147,233,23,24,65,139,389,130,217,65,45,299,15,6,292,52,279,144,92,114,33,240 57 | PA-42,5,596,93,155,1314,4,132,21,28,963,55,29,99,8,16,117,46,41,1012,1032,9,2,17,95,58,342,237,14,41,148,149,1666,16,2179,1360,158 58 | PA-43,56,46,298,156,3160,146,45,92,1,62,348,45,11,75,47,47,170,105,475,376,1,22,580,14,40,0,58,75,37,4,8,110,733,259,213,14 59 | PA-46,15,1310,151,62,103,935,240,26,219,489,50,1534,1,27,299,2,276,203,153,821,72,88,23,0,314,4,6,287,118,91,197,362,5,39,8,69 60 | PA-47,79,244,223,19,797,67,87,259,88,55,166,108,196,158,15,4,74,66,10,8,36,118,165,2,253,41,305,1312,26,10,35,227,416,70,787,15 61 | PA-48,154,47,56,43,258,18,17,91,79,464,30,55,974,3,900,15,18,54,123,98,75,51,19,58,168,16,8,221,640,35,259,214,1164,30,118,218 62 | PA-49,35,15,391,9,150,18,123,17,178,164,69,44,12,39,67,47,6,7,1387,5,292,10,35,141,224,281,51,27,17,37,28,90,20,1225,1099,34 63 | PA-51,68,41,65,137,705,168,53,45,33,178,82,73,274,20,1531,3,35,31,318,79,233,49,25,290,43,45,880,621,201,52,149,34,174,995,551,1296 64 | PA-52,2699,60,5,9,432,56,96,26,169,15,579,61,55,49,4093,15,83,102,948,27,353,64,105,30,1505,411,23,272,33,189,600,73,40,340,242,123 65 | PA-53,16,422,31,107,589,24,56,118,4,12,12,1704,131,163,4,58,54,51,469,47,42,305,209,17,859,357,6,7781,1535,12,51,2551,20,158,111,3436 66 | PA-54,58,2,36,228,1149,30,111,9,5,91,16,23,290,100,21,4,114,83,17,10,0,528,85,1,145,8,7,26,14,283,52,200,444,771,1219,852 67 | PA-55,232,197,71,481,1170,62,124,115,132,1023,552,58,80,10,24,172,70,45,65,30,159,224,91,178,161,21,179,43,865,70,882,82,179,30,7,77 68 | PA-57,363,25,135,298,7851,2828,453,267,163,161,20,2065,365,418,15,7,38,38,20,91,678,3,28,51,53,6,800,1365,391,268,10,181,336,325,43,54 69 | PA-58,14,175,107,124,1126,58,273,78,570,24,33,12,39,28,56,412,59,120,102,156,5,8,0,26,61,7,133,8,26,169,504,228,115,8,12,740 70 | PA-59,19,223,93,210,101,99,59,87,52,917,177,8,813,41,52,193,1298,801,22,631,83,209,5,6,154,104,60,5,551,101,66,153,362,25,101,94 71 | PA-61,18,33,110,45,1919,54,467,7,190,17,97,168,42,27,94,55,56,81,229,27,9,616,3,74,2032,28,25,11,250,60,158,129,297,261,182,72 72 | PA-62,23,10,34,458,1640,192,927,21,74,14,1176,17,42,19,291,191,138,147,160,129,157,73,41,404,8,203,1005,237,838,20,14,135,6,25,19,637 73 | PA-63,16,257,241,422,514,41,211,67,251,39,7,206,150,122,155,280,43,63,155,543,302,27,700,435,12,70,59,97,5,54,360,882,455,572,483,1391 74 | PA-64,176,4365,13,14,155,90,495,69,7,14,31,113,15,82,325,57,932,574,70,68,224,193,4,70,93,28,42,536,45,12,218,792,45,1,1055,1856 75 | PA-65,104,24,9,14,15,69,54,98,6,71,1160,67,993,96,226,25,20,18,1300,220,50,30,12,11,312,32,651,10,26,33,35,786,657,43,289,1037 76 | PA-68,1552,12,7,83,47,368,75,1651,12,1941,95,409,3,5,725,560,130,369,117,33,71,77,28,30,124,587,120,167,720,76,94,61,333,32,15,189 77 | PA-70,278,29,46,140,242,24,2227,769,174,22,96,49,31,187,55,165,50,32,34,12,2,165,13,8,139,70,76,401,117,515,396,1562,144,143,31,307 78 | PA-71,228,162,17,17,805,799,156,231,98,58,94,706,110,685,2622,127,148,218,49,46,156,28,494,20,34,161,158,300,795,11,36,504,5,597,307,66 79 | PA-73,95,9,27,124,373,22,375,32,432,10,12,42,3,57,11,4,692,998,61,200,4,4,3,46,12,43,82,15,161,26,16,119,19,30,16,57 80 | PA-74,50,175,90,28,778,751,57,99,1529,188,161,2332,41,14,741,9,226,147,998,203,122,17,38,33,28,12,4640,737,243,461,454,7293,6,178,261,16 81 | PA-77,161,543,32,10,1007,110,27,1416,668,1133,86,375,45,156,306,8,10,52,45,643,32,2,49,4,9,206,137,3,1410,463,340,3861,67,43,99,311 82 | PD-1,529,7858,219,142,7884,3403,367,884,874,7669,4748,7129,142,1870,5520,51,232,578,1099,856,78,842,82,58,313,60,232,1124,77,50,3908,1132,796,1935,460,2130 83 | PD-14,6,7853,23,3,288,62,1,126,19,2365,87,276,50,838,368,3,3,24,26,3,45,20,5,3,2,1,4,840,1,5,154,23,0,554,12,36 84 | PD-16,107,18,82,6,546,17,3,452,817,151,4599,65,4,41,3478,1,7,45,15,11,16,10,25,0,36,56,93,13,7,6,298,69,9,287,151,839 85 | PD-17,72,285,25,4,4267,22,15,244,7,719,1,60,3,966,1642,2,7,91,87,4,11,98,3,27,163,0,4,23,8,39,409,650,55,879,208,61 86 | PD-18,319,9,27,13,7857,3229,150,41,29,4377,56,6296,83,1,15,1,214,414,166,13,1,698,49,2,99,1,5,237,57,0,2779,356,11,202,72,445 87 | PD-20,165,10,185,21,561,37,22,63,2,39,1,219,95,8,97,25,827,1344,8,34,18,32,2,2,0,7,142,2152,16,2,1308,4704,3,49,4,5810 88 | PD-5,523,777,77,1081,1392,47,20,40,1,537,4,343,3,7,27,67,28,51,19,7,5,479,1,8,0,0,4983,18,20,10,7815,467,594,52,0,82 89 | PD-7,1025,618,117,0,7928,548,122,12,1,19,31,8,10,39,150,8,9,12,3,3,40,38,0,35,1,32,9,566,0,0,2555,140,30,524,0,201 90 | PD-8,5,320,1,3,1017,376,7,31,22,29,37,4,7,7,905,22,66,6,27,17,85,5,8,164,6,13,134,61,4,31,433,18,41,52,8,1338 91 | PD-9,2,3486,122,2,1393,711,9,13,319,1061,12,103,51,59,404,62,11,6,38,99,16,73,0,149,207,2,3,94,0,7,7772,18,3,350,11,214 92 | PDD-1,508,45,4,152,597,6,103,159,14,35,164,22,3,33,582,296,3,2,602,4,1042,265,4,6,34,65,164,592,31,1250,1059,51,165,139,72,2876 93 | PDD-10,370,149,12,263,643,64,201,51,469,721,283,2234,589,35,190,19,14,28,31,70,84,462,32,14,14,170,611,135,21,14,1720,13,109,316,286,1658 94 | PDD-11,219,71,523,220,44,79,216,585,428,20,58,819,83,180,21,562,19,4,349,9,31,29,11,400,8,42,914,13,25,252,543,360,1467,160,26,576 95 | PDD-12,85,76,3,257,851,392,57,1362,363,337,65,212,298,5,10,6,91,163,171,187,161,37,12,391,13,304,617,441,371,823,61,13,18,43,35,316 96 | PDD-13,3,17,17,8,64,26,7,23,5,37,51,20,13,9,3,13,13,11,15,37,9,12,1,10,3,4,78,4,27,5,25,17,6,8,32,9 97 | PDD-14,17,245,152,98,514,313,32,435,33,701,485,317,197,225,24,29,27,24,240,643,67,146,14,4,20,75,910,9,200,62,247,261,108,85,102,184 98 | PDD-15,56,54,5,26,236,1507,40,64,21,41,5905,40,112,6,48,33,19,9,6,0,6,201,1,89,12,5,1677,33,5751,571,30,139,3,70,17,253 99 | PDD-16,209,10,27,374,198,30,44,108,179,558,115,464,86,75,49,205,68,85,62,6,43,189,20,8,74,55,479,598,34,229,369,225,39,4,114,354 100 | PDD-17,107,28,21,367,440,167,121,43,225,46,6,605,497,127,15,17,193,208,320,354,50,526,5,846,40,65,1009,472,15,61,40,184,789,8,34,606 101 | PDD-18,232,490,17,12,137,102,40,32,172,131,32,127,361,22,110,48,117,109,156,222,7,111,30,454,48,33,43,519,393,314,288,39,2039,108,96,29 102 | PDD-19,34,17,379,71,521,89,215,824,92,190,67,39,52,114,54,30,1,4,18,15,62,168,39,212,279,89,176,7,277,96,3138,526,1648,233,244,1000 103 | PDD-2,41,43,9,342,1422,17,158,81,18,101,106,239,58,64,406,56,31,15,26,47,2,583,3,94,119,513,775,74,137,780,351,166,863,96,54,38 104 | PDD-20,320,195,1117,245,2415,623,122,422,401,1531,542,45,16,232,9,94,37,53,78,35,88,564,240,247,178,266,119,191,80,224,437,299,413,99,43,255 105 | PDD-21,1,2,0,0,15,0,8,10,4,10,0,5,1,0,1,3,2,4,6,5,0,24,0,0,1,7,3,0,1,1,24,0,0,6,5,2 106 | PDD-22,468,1379,467,19,677,943,166,213,31,1121,389,60,14,21,118,551,94,242,76,71,154,49,43,1047,10,20,225,368,55,149,326,177,382,239,166,506 107 | PDD-23,449,225,529,363,1189,907,659,449,1112,1432,396,2110,1303,16,283,75,106,45,67,71,187,168,31,53,353,419,369,340,252,1847,3107,215,1478,103,236,2963 108 | PDD-24,361,224,67,286,17,158,75,232,86,393,108,331,16,416,46,792,717,498,286,116,62,37,22,85,1,342,77,423,34,261,363,188,378,504,390,148 109 | PDD-25,114,8,471,81,181,1,48,123,116,328,24,359,64,160,156,186,60,55,218,46,41,450,165,315,80,45,674,41,292,240,131,31,921,62,47,175 110 | PDD-26,27,84,10,8,499,21,94,518,1728,124,150,61,475,1145,97,557,512,502,8,1125,3,233,27,83,47,105,81,26,675,669,337,219,117,1015,743,186 111 | PDD-27,415,912,272,695,751,323,95,472,1240,497,244,524,178,296,312,54,365,374,448,29,41,126,46,266,14,25,101,254,980,517,1000,116,551,380,405,1359 112 | PDD-28,26,224,564,940,723,138,84,604,37,26,102,312,84,35,70,15,9,4,254,41,39,61,13,22,30,89,0,1,5,118,47,201,183,175,413,5 113 | PDD-29,1,1,1,0,1,2,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,3,2,0,0,0,1 114 | PDD-3,475,56,101,199,289,177,33,6,24,443,413,152,18,32,48,174,63,133,709,472,33,18,293,182,74,60,418,36,107,823,106,30,276,85,66,386 115 | PDD-30,192,394,33,152,2415,236,60,416,770,477,96,1395,40,0,95,473,87,112,36,523,15,270,320,278,14,246,197,197,3,22,562,450,39,65,113,212 116 | PDD-31,2,28,15,426,12,13,132,39,11,30,800,10,25,1,568,12,590,505,22,76,3,29,12,7,5,3,9,2,3,498,23,43,5,12,60,923 117 | PDD-32,105,726,16,104,905,789,62,250,164,1704,189,647,8,8,504,112,80,258,52,9,117,1412,11,135,88,12,913,354,211,160,844,254,110,90,66,1238 118 | PDD-33,168,86,580,51,106,10,240,11,164,863,100,162,56,21,12,258,27,3,10,4,11,281,13,6,493,8,68,88,12,178,6,168,285,2,7,447 119 | PDD-34,357,4,130,1168,2177,198,174,222,182,92,70,1654,225,211,333,288,36,21,43,6,4,161,17,85,228,10,32,312,47,203,187,402,547,99,57,509 120 | PDD-35,35,20,1212,69,429,222,49,303,744,547,17,141,1464,163,62,899,145,184,176,169,33,38,424,128,138,200,1504,115,894,4,794,174,24,678,399,475 121 | PDD-36,328,257,377,183,275,489,90,348,42,168,105,216,139,140,232,16,322,221,127,320,12,155,4,10,4,790,729,81,26,68,79,289,87,678,612,525 122 | PDD-37,667,241,138,119,16,156,198,78,66,1182,84,394,68,386,95,212,204,251,193,47,9,16,93,177,110,359,529,38,1100,142,138,311,86,20,39,722 123 | PDD-38,65,213,36,517,196,83,95,45,91,392,518,101,9,609,1,295,182,219,10,14,0,33,11,34,99,53,244,1059,89,401,762,49,311,680,479,916 124 | PDD-39,338,120,121,364,29,18,770,32,109,245,644,854,120,195,62,6,124,108,92,41,30,133,406,540,52,194,275,11,861,53,42,47,1694,819,666,44 125 | PDD-4,197,579,345,266,349,258,396,10,35,134,33,2172,35,121,60,147,11,2,1584,139,133,781,13,242,9,44,449,737,31,404,738,496,425,74,44,484 126 | PDD-40,74,60,124,8,1095,2,99,852,123,668,52,499,141,1,19,485,612,431,216,547,42,3360,12,198,39,265,208,21,79,7,1393,4,68,703,743,43 127 | PDD-41,308,27,98,59,11,126,47,172,75,253,92,31,26,23,58,63,31,25,177,58,43,513,51,552,37,164,26,28,67,48,15,262,685,100,70,1315 128 | PDD-42,241,151,29,69,193,80,15,5,68,468,164,62,81,202,83,26,17,7,384,8,121,8,31,612,60,149,523,9,118,287,96,13,997,3,7,210 129 | PDD-43,25,50,664,57,393,505,156,93,20,681,40,366,185,48,12,26,450,471,57,20,6,7,34,13,32,133,56,432,13,291,76,150,68,94,78,609 130 | PDD-44,220,30,963,896,48,98,42,606,723,1140,329,110,18,198,21,274,154,131,325,55,77,96,84,176,107,24,105,39,185,166,1279,1,372,841,735,18 131 | PDD-45,42,108,285,96,70,404,3,35,84,1524,222,199,31,1751,190,77,192,140,796,236,44,545,80,305,184,204,957,1462,194,166,40,300,3315,64,101,323 132 | PDD-46,84,499,639,800,140,67,71,190,543,2058,777,1780,829,177,206,432,203,217,145,28,117,90,88,25,145,179,168,26,138,69,1244,139,704,253,138,1305 133 | PDD-47,15,25,98,181,108,556,77,155,104,90,514,1587,47,109,28,265,86,114,6,371,163,260,32,987,8,60,554,118,20,185,147,11,393,613,424,83 134 | PDD-48,181,334,249,421,413,48,165,90,99,2478,165,78,180,114,144,10,19,27,167,9,105,932,76,192,51,78,179,866,267,18,189,37,20,240,123,252 135 | PDD-49,13,589,18,213,146,153,8,2,32,95,43,579,357,45,72,60,67,68,60,175,4,91,25,1363,71,11,746,2232,385,159,183,41,21,225,110,1738 136 | PDD-5,65,262,181,84,1292,345,177,97,870,2299,153,2167,425,519,71,178,45,27,1060,475,12,401,173,161,53,218,33,927,788,200,1779,371,70,128,12,884 137 | PDD-50,310,458,227,1261,88,64,57,17,36,1002,29,208,38,72,347,132,188,189,150,939,60,28,230,127,97,141,571,53,5,812,1037,60,598,91,27,275 138 | PDD-51,866,574,13,29,327,20,71,19,6,466,27,1669,1352,33,139,21,42,69,17,27,3,141,122,101,0,44,585,22,35,71,27,20,10,35,5,242 139 | PDD-52,946,316,298,265,254,37,117,1098,345,52,26,114,39,1642,115,59,1,10,611,87,53,889,31,320,0,175,1,175,157,487,1853,46,103,113,0,96 140 | PDD-53,791,187,190,501,83,206,17,456,25,130,805,664,24,110,176,132,57,73,25,85,38,166,18,71,35,2,11,19,256,201,484,427,205,185,153,239 141 | PDD-54,70,7,52,201,22,392,22,636,227,986,30,314,123,66,23,11,175,220,151,2,12,98,0,7,46,117,40,86,256,76,12,82,1006,45,14,95 142 | PDD-55,88,32,11,17,411,73,56,28,16,50,234,27,14,8,88,1,9,6,28,61,286,7,12,308,8,20,604,207,6,93,44,396,572,168,96,449 143 | PDD-56,49,160,185,124,63,70,428,22,53,561,16,3,8,489,6,365,160,112,260,17,146,15,23,20,1,36,34,423,9,14,195,431,962,342,307,138 144 | PDD-57,117,20,220,180,90,27,13,110,90,524,6,64,147,8,173,21,79,68,30,296,67,466,37,60,1,6,602,31,141,182,156,45,175,230,321,1076 145 | PDD-58,320,86,92,1207,912,416,670,456,17,78,10,229,454,506,110,210,138,168,823,457,170,128,16,2168,91,128,636,12,72,148,68,67,37,34,2,560 146 | PDD-59,221,206,247,170,37,109,51,287,144,2343,262,123,48,118,85,163,131,234,161,71,18,49,152,287,10,16,439,157,236,846,269,115,490,54,26,639 147 | PDD-6,626,134,16,55,235,129,247,157,252,796,403,67,73,46,17,198,135,166,1323,4,91,74,79,130,97,158,33,127,64,301,32,161,1308,742,317,263 148 | PDD-60,79,7,34,7,27,6,0,126,57,215,31,41,7,6,79,10,0,1,24,23,0,21,1,26,35,10,9,18,38,39,91,6,20,9,15,30 149 | PDD-61,191,52,120,229,11,1,17,621,73,100,27,1406,80,627,6,933,47,54,282,8,82,1040,4,63,35,39,1041,0,29,725,102,280,43,27,32,1605 150 | PDD-62,480,210,284,901,883,183,125,110,72,2263,353,50,17,43,51,421,26,50,3,464,340,446,2,8,132,176,108,96,451,740,1157,42,278,11,8,296 151 | PDD-63,4,232,112,26,1019,8,7,326,247,1595,139,64,74,3,160,8,52,49,33,251,28,221,261,344,28,75,22,15,36,30,100,38,350,5,0,47 152 | PDD-64,17,191,80,22,911,9,4,216,248,1196,118,53,74,1,164,3,57,23,33,191,45,202,151,250,22,66,30,10,40,26,124,62,322,5,8,31 153 | PDD-65,278,45,10,344,268,233,30,222,639,515,248,812,143,6,544,13,15,12,797,132,107,264,38,69,33,101,86,175,346,209,2073,104,1447,220,221,164 154 | PDD-66,0,0,8,29,1,5,3,6,18,3,900,1128,2,0,1115,3,4,0,0,734,1022,106,0,7,0,104,47,72,4,69,10,3,3,116,22,120 155 | PDD-67,3,0,1118,635,7,49,134,2,917,94,72,981,6,4,68,1,201,151,55,46,21,33,0,14,2,72,233,1,360,309,10,311,21,77,140,3215 156 | PDD-68,535,477,41,875,412,246,140,450,60,631,308,507,104,906,197,136,196,204,77,250,10,448,173,511,79,17,812,768,63,361,1665,208,131,608,775,284 157 | PDD-69,1863,71,120,1210,891,293,109,317,165,737,25,591,11,4,88,58,23,47,51,78,38,185,15,85,63,121,3,308,342,144,384,450,169,26,20,150 158 | PDD-7,311,83,256,289,416,205,216,1240,255,512,386,54,21,197,26,46,428,515,399,28,23,1280,171,88,352,64,4,1157,1193,107,299,349,549,134,40,545 159 | PDD-70,25,11,563,550,109,6,3,42,21,410,6,1598,1,62,130,98,2,2,559,463,10,37,24,551,2,136,41,66,3,67,3107,236,514,42,11,1038 160 | PDD-71,32,78,57,75,168,21,261,71,550,651,345,223,78,1,228,410,215,266,271,7,168,64,43,498,3,41,8,44,42,831,292,846,285,91,149,3470 161 | PDD-72,287,116,7,879,1119,184,147,27,110,218,578,756,65,25,17,454,34,61,507,6,3,179,169,7,41,1,426,244,1111,360,237,111,122,73,100,300 162 | PDD-73,332,80,48,211,215,81,35,64,28,827,506,632,433,95,13,104,80,20,22,168,74,82,6,16,0,50,18,25,9,79,688,291,6,333,109,270 163 | PDD-74,15,202,163,103,71,39,32,84,185,34,61,3343,138,147,49,88,62,58,60,330,53,130,21,201,13,17,40,480,166,177,63,33,21,514,447,2506 164 | PDD-75,58,39,99,10,272,23,53,96,1094,659,41,28,3612,34,222,26,58,16,171,122,0,12,0,2,12,452,616,55,21,7,14,621,209,78,251,89 165 | PDD-76,37,1,118,26,1754,112,24,103,113,849,83,664,30,176,15,81,255,228,119,33,3,264,23,499,9,609,15,320,34,93,102,29,51,20,84,352 166 | PDD-77,15,7,385,5,128,84,23,681,119,59,109,29,173,10,284,11,17,33,5,34,59,424,22,100,103,120,783,11,43,13,753,241,83,184,114,85 167 | PDD-78,20,246,24,34,866,679,397,20,167,1279,68,23,26,35,36,67,39,5,118,205,131,118,12,369,16,249,3,199,231,44,550,572,438,234,316,205 168 | PDD-79,86,31,22,2088,1078,6,84,248,34,1737,44,1330,23,7,573,193,256,207,75,6,5,2003,15,62,10,841,45,183,38,684,3121,325,396,210,163,1879 169 | PDD-8,99,40,48,1130,904,91,110,659,34,226,59,18,655,28,534,179,79,28,19,109,42,469,20,107,91,135,21,112,633,376,276,54,34,489,329,2191 170 | PDD-80,269,93,285,168,1446,16,115,109,202,192,1119,1027,9,16,101,53,44,38,54,224,50,169,195,671,53,126,236,361,276,221,172,164,631,462,273,139 171 | PDD-81,25,77,596,470,41,36,33,307,117,255,166,4357,442,47,8,208,45,29,110,174,9,1072,44,7,10,66,179,58,63,249,140,209,196,206,111,214 172 | PDD-82,408,121,101,76,99,93,8,461,129,337,80,374,271,38,724,142,150,111,497,247,59,27,11,71,4,14,1,88,142,56,19,853,33,41,39,1771 173 | PDD-83,22,957,67,342,802,559,35,264,705,682,180,110,96,34,416,13,117,81,5,22,76,317,47,140,4,24,3,373,8,8,198,458,516,69,159,305 174 | PDD-84,219,280,23,538,233,2,97,118,30,344,70,1213,32,200,254,1,164,177,105,299,43,368,21,29,26,84,434,67,597,80,287,55,423,304,147,88 175 | PDD-85,39,24,242,466,33,234,112,189,256,246,18,352,54,2,31,23,42,85,68,49,31,158,0,360,232,5,808,10,118,8,31,34,2382,284,521,424 176 | PDD-86,153,449,1359,814,4,60,11,734,483,3223,4,373,598,41,134,217,20,38,299,28,4,13,148,93,28,163,137,47,105,207,158,395,443,170,95,359 177 | PDD-87,319,1,42,45,334,509,159,195,138,581,85,202,5,18,128,115,7,2,613,460,0,296,7,1032,198,0,6,3,56,79,49,318,256,52,82,349 178 | PDD-88,488,15,558,314,379,457,4,514,41,76,35,16,360,13,15,95,242,81,282,20,21,188,0,376,46,12,298,41,17,85,172,356,345,8,7,275 179 | PDD-89,24,17,118,660,16,52,262,146,0,47,136,281,48,361,91,388,285,254,365,135,0,69,2,29,41,25,90,18,95,101,9,164,246,88,48,896 180 | PDD-9,1102,397,151,251,295,50,448,40,25,615,80,236,105,140,319,8,275,238,58,39,96,357,68,1624,143,94,229,131,317,83,311,657,54,143,169,1223 181 | PDD-90,164,43,188,115,9,60,262,38,2909,375,156,32,211,60,10,76,19,6,755,112,1,624,5,469,47,183,225,40,205,29,146,334,610,501,347,657 182 | PDD-91,23,109,171,517,17,10,387,89,522,89,465,204,292,553,32,12,157,135,100,97,4,768,228,5,8,64,390,36,993,86,55,997,114,963,565,232 183 | PDD-92,49,65,18,270,275,56,1,143,504,67,102,60,237,118,12,212,49,67,348,18,101,793,14,40,110,66,143,206,80,91,229,1104,276,24,46,650 184 | PDD-93,16,55,591,26,29,40,50,51,67,314,160,158,530,53,65,82,70,130,320,3,72,48,6,227,12,234,183,1,45,77,86,1161,675,56,24,188 185 | PDD-94,202,6,381,16,85,427,27,1589,16,414,2,784,73,265,10,570,27,7,11,40,23,26,65,84,20,34,887,76,79,8,46,1841,105,15,35,41 186 | PDD-95,30,113,488,114,108,19,29,857,82,56,70,9,12,77,45,32,145,121,111,40,22,89,62,32,12,88,585,623,0,6,106,6,8,119,58,111 187 | PDD-96,130,6,62,60,127,129,74,9,28,11,1,69,1,12,49,530,229,180,1928,169,9,5,29,113,7,0,7,50,29,83,362,44,124,42,33,760 188 | -------------------------------------------------------------------------------- /data/PDAC/PDAC_panel.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/raphael-group/ConDoR/08c116181dc54d7d5a73f3bd4bbc3deca62bf769/data/PDAC/PDAC_panel.xlsx -------------------------------------------------------------------------------- /data/PDAC/Snakefile: -------------------------------------------------------------------------------- 1 | # simulation snakefile 2 | configfile: "config.yaml" 3 | 4 | rule all: 5 | input: 6 | # condor 7 | #expand('condor/CRC2_k{k}_B.csv', filtered_product, seed=seeds, ncells=config['ncells'], ncharacters=config['ncharacters'], nclusters=config['nclusters'], missing=config['missing_rate'], k=config['k']), 8 | expand('condor_reads/M04_k{k}_B.csv', k=config['k']), 9 | #expand('sphyr/M04_k{k}.out', k=config['k']), 10 | expand('sphyr/M04_k{k}.dot', k=config['k']), 11 | expand('compass/M04_tree.gv') 12 | 13 | rule compass: 14 | output: 15 | tree_file='compass/M04_tree.gv', 16 | input: 17 | variants_file='input/M04_variants.csv', 18 | regions_file='input/M04_regions.csv', 19 | params: 20 | exec=config['compass_exec'], 21 | nchains=config['nchains'], 22 | chainlength=config['chainlength'], 23 | log: 24 | std='compass/M04.log', 25 | err='compass/M04.err.log', 26 | benchmark: 'compass/M04.benchmark', 27 | shell: 28 | '{params.exec} -i input/M04 -o {output.tree_file} --nchains {params.nchains} --chainlength {params.chainlength} --CNV 1 ' 29 | ' > {log.std} 2> {log.err}' 30 | 31 | #rule condor: 32 | # output: 33 | # B='condor/CRC2_k{k}_B.csv', 34 | # input: 35 | # character_matrix='ground_truth/CRC2_k{k}_character_matrix.csv', 36 | # params: 37 | # prefix='condor/CRC2_k{k}', 38 | # fp=config['estimated_fp'], 39 | # fn=config['estimated_fn'], 40 | # log: 41 | # std='condor/CRC2_k{k}.log', 42 | # err='condor/CRC2_k{k}.err.log', 43 | # benchmark: 'condor/CRC2_k{k}.benchmark', 44 | # shell: 45 | # 'python ../../src/condor.py -i {input.character_matrix} -a {params.fp} -b {params.fn} -k {wildcards.k} ' 46 | # ' -o {params.prefix} ' 47 | # ' > {log.std} 2> {log.err} ' 48 | 49 | rule sphyr: 50 | output: 51 | result='sphyr/M04_k{k}.out', 52 | input: 53 | character_matrix='input/sphyr_input.txt', 54 | params: 55 | exec=config['sphyr_exec'], 56 | #lT=7, 57 | lT=10, 58 | fn=config['sphyr_fn'], 59 | fp=config['sphyr_fp'], 60 | nmutations=config['nmutations'], 61 | log: 62 | std='sphyr/M04_k{k}.log', 63 | err='sphyr/M04_k{k}.err.log', 64 | benchmark: 'sphyr/M04_k{k}.benchmark', 65 | shell: 66 | '{params.exec} {input.character_matrix} {output.result}' 67 | ' -a {params.fp} -b {params.fn} -lT {params.lT} -lC {params.nmutations} -k {wildcards.k} -v ' 68 | ' > {log.std} 2> {log.err}' 69 | 70 | rule sphyr_viz: 71 | output: 72 | result='sphyr/M04_k{k}.dot', 73 | input: 74 | sphyr_output='sphyr/M04_k{k}.out', 75 | cell_names='input/sphyr_cell_names.txt', 76 | mutation_names='input/sphyr_mutation_names.txt', 77 | params: 78 | exec=config['sphyr_viz_exec'], 79 | log: 80 | err='sphyr/M04_k{k}_viz.err.log', 81 | benchmark: 'sphyr/M04_k{k}_viz.benchmark', 82 | shell: 83 | '{params.exec} {input.sphyr_output} -t {input.cell_names} -c {input.mutation_names} > {output.result} ' 84 | ' 2> {log.err} ' 85 | 86 | 87 | rule condor_reads: 88 | output: 89 | B='condor_reads/M04_k{k}_B.csv', 90 | input: 91 | character_matrix='input/character_matrix.csv', 92 | total_read_matrix='input/total_readcounts.csv', 93 | variant_read_matrix='input/alt_readcounts.csv', 94 | snp_list='input/snp_list.txt', 95 | params: 96 | prefix='condor_reads/M04_k{k}', 97 | fp=config['fp'], 98 | fn=config['fn'], 99 | ado=config['ado'], 100 | log: 101 | std='condor_reads/M04_k{k}.log', 102 | err='condor_reads/M04_k{k}.err.log', 103 | benchmark: 'condor_reads/M04_k{k}.benchmark', 104 | shell: 105 | 'python ../../src/condor.py -i {input.character_matrix} -a {params.fp} -b {params.fn} -k {wildcards.k} ' 106 | ' -r {input.total_read_matrix} -v {input.variant_read_matrix} -s {input.snp_list} --ado {params.ado} ' 107 | ' -o {params.prefix} ' 108 | ' > {log.std} 2> {log.err} ' 109 | -------------------------------------------------------------------------------- /data/PDAC/config.yaml: -------------------------------------------------------------------------------- 1 | # 2 | 3 | fp: 0.001 4 | fn: 0.001 5 | #fp: 0.01 6 | #fn: 0.01 7 | 8 | ado: 15 9 | 10 | k: 11 | #- 0 12 | - 1 13 | #- 2 14 | 15 | #sphyr parameters 16 | sphyr_exec: '/n/fs/ragr-data/users/palash/SPhyR/build/kDPFC' 17 | sphyr_viz_exec: '/n/fs/ragr-data/users/palash/SPhyR/build/visualize' 18 | sphyr_fp: 0.01 19 | sphyr_fn: 0.1 20 | nmutations: 7 21 | 22 | # compass parameters 23 | nchains: 4 24 | chainlength: 5000 25 | compass_exec: '/n/fs/ragr-data/users/palash/COMPASS/COMPASS' 26 | -------------------------------------------------------------------------------- /data/PDAC/input/M04_regions.csv: -------------------------------------------------------------------------------- 1 | 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2 | 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3 | 8_FGFR1,490,207,1085,406,143,382,28,532,291,792,186,809,851,261,755,244,401,654,181,435,1133,837,641,392,337,272,279,248,164,318,195,547,499,430,191,844,579,446,318,275,624,154,852,671,308,423,547,992,512,106,301,8,783,338,381,125,525,187,1866,51,1008,631,296,286,145,318,252,518,274,128,302,384,722,338,563,512,293,480,191,621,993,837,385,977,485,325,248,515,819,632,130,543,224,156,485,611,215,1052,306,196,304,835,595,200,239,417,142,129,473,424,475,533,606,139,166,274,164,542,650,473,172,605,546,387,250,265,273,431,851,1080,243,165,378,1096,394,144,262,145,113,411,305,1538,1811,76,82,992,123,364,137,202,848,49,146,367,226,177,645,363,116,291,263,219,208,650,0,816,301,67,687,436,544,310,133,695,507,273,188,354,333,456,214,729,105,347,143,550,534,539,265,308,728,602,233,463,238,396,223,310,350,667,670,1458,744,909,1838,415,156,172,460,286,422,96,223,349,510,379,935,58,292,354,200,144,573,202,420,247,620,11,355,196,315,218,241,268,144,636,559,193,222,373,277,507,406,87,284,594,402,634,376,146,220,483,240,80,272,227,435,572,527,340,610,254,421,284,415,473,670,319,774,276,169,415,173,290,316,200,409,544,483,126,797,239,497,796,452,455,345,259,275,323,932,427,552,407,378,315,684,624,421,460,150,300,471,750,355,1130,258,384,1094,453,472,313,77,48,892,275,683,1271,578,68,388,485,510,323,236,160,224,301,425,124,424,498,412,519,82,388,464,755,608,494,579,477,887,580,233,304,227,328,1344,1594,384,326,194,263,124,229,157,531,273,80,907,177,103,486,286,199,581,270,1629,556,275,884,525,246,537,490,102,161,416,388,96,393,453,977,468,317,288,421,285,84,428,256,775,275,39,174,341,329,317,266,301,454,601,389,396,15,267,87,489,224,263,335,655,401,154,348,414,276,272,993,323,332,145,565,650,289,80,371,400,156,348,83,57,253,654,245,104,366,13,1197,364,301,303,593,181,135,334,114,192,111,337,722,189,881,462,351,326,239,310,923,226,875,437,911,152,208,1698,354,752,335,519,375,75,150,208,824,363,451,782,122,322,1602,247,623,702,561,667,168,429,881,143,851,289,117,188,332,603,155,624,296,15,265,788,172,643,565,275,566,611,466,203,153,1148,58,185,834,616,664,103,384,212,389,399,959,66,287,197,827,320,328,229,383,435,137,1353,325,239,651,421,237,84,111,194,751,605,368,449,240,59,274,817,525,290,121,374,141,550,482,232,260,812,159,417,693,488,480,123,613,700,746,940,398,1168,327,393,236,780,434,940,250,870,119,355,94,337,630,134,146,428,317,277,310,449,416,366,2014,310,66,690,653,568,445,636,254,456,75,299,274,360,379,227,956,680,148,269,408,255,455,235,210,353,240,353,556,440,476,742,360,369,251,281,1282,475,197,407,455,930,179,299,772,164,148,254,34,658,222,464,303,279,313,146,982,492,381,197,248,205,111,602,179,176,341,254,463,164,94,210,431,236,387,443,1366,523,589,129,946,530,95,687,829,105,234,364,1422,135,462,345,419,389,233,468,493,444,90,827,637,93,419,170,472,218,253,177,508,259,136,148,245,133,356,321,889,461,921,506,393,1408,124,226,520,749,46,951,815,166,1311,279,1470,213,181,998,226,146,435,219,984,99,219,373,102,84,201,478,421,137,624,252,531,343,493,145,793,220,169,354,368,122,92,138,391,558,284,725,692,405,407,392,194,183,234,702,372,321,416,299,454,155,112,345,322,310,1043,388,280,142,358,368,341,523,80,209,112,137,386,814,1612,482,267,507,353,314,126,1255,141,522,348,306,98,225,582,764,922,338,246,257,322,781,121,753,1374,1012,171,279,903,455,233,193,622,552,680,227,302,61,100,346,372,720,359,321,532,331,330,759,519,104,199,104,1311,309,441,228,655,102,213,602,370,398,474,483,201,161,141,396,538,194,277,170,97,531,165,255,172,470,841,791,94,359,456,847,142,175,167,484,657,110,136,460,440,922,482,329,330,358,548,193,354,374,152,98,299,10,357,65,247,423,575,390,461,232,1027,427,202,542,189,310,129,854,535,440,192,79,253,161,66,328,476,463,424,995,315,200,311,670,414,150,366,536,683,399,527,205,390,228,354,291,143,102,1500,374,218,1577,189,673,367,777,514,149,404,672,255,368,811,593,196,333,857,403,450,298,487,1728,362,965,955,554,432,500,640,249,771,157,449,688,255,503,69,622,270,205,1862,726,2442,633,763,209,382,96,2080,54,550,2646,1185,2999,552,946,4669,1122,1794,2224,852,864,873,1362,171,760,1150,2384,397,835,1485,447,1438,401,422,1137,1463,962,2389,623,977,768,3596,899,291,2569,408,410,382,356,2066,364,643,316,1514,3412,1436,355,996,1240,444,181,249,400,1481,401,309,494,1012,171,812,1062,596,6260,220,2977,1243,3434,635,516,761,584,991,498,216,78,368,1043,670,224,2310,272,569,369,564,2711,449,1224,559,256,884,1410,302,284,484,216,1619,2665,149,541,1315,1106,1098,519,461,2515,1402,297,2451,1303,735,936,1260,569,1204,295,204,1566,1411,431,253,3277,1593,1146,117,2167,128,392,1059,525,2673,2003,900,458,130,367,2332,3018,242,1641,471,473,507,513,1591,742,772,452,846,1258,398,561,335,429,1157,797,328,1042,3155,2251,226,982,886,2157,602,2589,840,624,816,482,1027,658,2066,370,4655,264,177,968,2034,1094,1162,1435,220,1979,1217,646,692,192,482,1449,447,1109,1258,461,554,594,1193,1461,1181,870,1503,4907,934,1334,1976,1413,384,1865,1762,456,184,6075,1487,540,1142,862,1949,1942,665,1320,2357,822,1349,658,1098,138,1846,0,972,570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4 | 10_MGMT,22,71,311,373,27,170,69,96,10,124,36,289,37,68,136,9,86,144,20,172,431,126,165,121,5,218,22,58,51,144,166,45,96,3,23,118,161,191,112,93,116,1,65,63,54,70,294,101,14,48,185,55,197,23,167,17,123,22,891,289,219,71,67,101,23,86,46,149,136,85,115,39,165,43,94,167,120,501,117,114,366,317,112,79,68,30,86,23,220,179,2,133,93,1,124,120,116,102,136,45,110,92,69,40,339,254,212,31,24,166,77,136,43,28,78,386,74,63,21,67,110,170,154,176,10,423,225,148,249,71,104,26,24,73,187,50,103,60,123,25,72,102,539,217,89,429,186,21,90,255,95,49,145,517,32,208,65,104,77,94,39,30,150,135,298,277,216,168,107,161,168,17,70,58,130,49,48,59,25,255,31,154,7,60,233,42,98,6,30,81,350,36,221,103,51,72,5,21,43,157,229,723,71,334,371,86,128,44,284,108,23,173,151,47,91,146,180,125,10,47,42,103,57,57,201,309,152,40,52,148,12,138,107,107,78,87,342,0,98,256,77,45,578,25,125,84,18,216,94,35,66,41,79,284,147,125,279,100,199,16,236,64,7,60,135,91,497,24,186,104,54,223,56,95,45,99,78,66,126,52,237,68,75,30,73,65,27,37,34,25,165,96,190,70,309,178,156,142,71,120,72,36,379,290,18,250,71,91,127,104,25,85,160,17,147,24,132,146,112,12,158,189,73,90,21,117,76,161,43,26,74,33,183,556,69,240,129,65,34,63,48,338,259,241,109,94,33,43,427,88,18,56,139,32,96,140,90,100,81,15,184,161,88,90,91,44,123,236,166,14,200,131,143,451,66,45,107,11,263,318,41,14,82,19,143,115,107,219,77,31,59,171,146,45,220,97,65,112,163,70,139,122,56,33,64,40,67,95,76,76,86,37,240,81,265,95,4,114,75,233,132,63,57,183,39,124,63,177,30,27,61,36,110,176,14,74,124,47,43,49,88,119,139,79,74,55,29,32,33,250,117,158,307,495,184,200,200,101,473,224,109,437,79,143,30,135,490,108,257,358,260,95,137,90,53,109,68,162,234,11,61,899,36,53,85,101,95,63,170,70,138,79,92,44,60,54,313,75,179,50,246,267,166,83,81,351,18,87,79,51,37,86,199,72,15,83,201,190,99,81,265,48,134,190,717,84,167,118,27,76,119,23,64,87,103,122,68,223,340,65,120,75,43,175,10,86,61,157,213,56,158,159,22,199,53,92,108,50,626,135,294,74,60,126,120,104,18,148,287,103,91,145,108,154,365,165,227,45,83,73,250,49,148,310,35,136,425,36,249,53,117,64,106,44,123,309,78,97,310,312,164,71,189,109,29,53,136,92,132,169,50,125,181,23,118,19,164,263,83,71,99,247,122,45,100,205,499,12,66,97,55,76,75,79,110,68,392,49,104,174,67,231,169,20,28,39,83,52,142,153,290,198,52,258,88,12,24,112,501,83,54,104,121,201,108,79,23,134,16,209,293,686,182,162,15,401,57,38,205,215,93,265,100,828,199,263,392,56,95,83,139,155,208,57,54,39,52,142,102,4,479,47,139,54,157,298,127,55,23,58,117,155,73,140,234,0,562,231,11,46,109,91,187,282,145,613,59,230,202,8,53,75,153,210,173,140,66,34,93,38,76,123,160,107,97,200,26,40,84,92,38,165,93,264,150,158,63,79,54,211,184,35,140,219,210,162,290,144,151,124,16,65,217,38,288,202,146,12,39,116,158,369,0,107,44,13,274,39,338,107,72,53,201,175,118,685,181,189,198,59,23,50,15,74,75,77,80,26,133,95,88,0,55,181,105,91,35,15,245,266,250,68,24,156,56,26,88,133,118,325,107,209,192,26,25,50,4,24,458,67,83,81,141,68,204,39,4,153,156,67,15,133,68,238,70,16,18,594,3,78,165,41,55,212,73,120,184,74,87,89,2,118,41,123,67,165,43,261,1,10,157,17,356,4,93,69,120,94,404,177,41,123,103,115,31,55,264,112,157,91,2,30,19,26,150,138,197,72,85,24,94,9,49,71,0,23,172,34,57,22,196,33,83,101,367,102,46,137,136,195,34,170,85,184,92,123,22,122,66,358,68,166,33,20,42,33,41,92,379,71,75,90,106,68,334,102,29,176,141,96,97,89,1,3,104,476,71,27,76,75,380,208,191,176,33,140,52,11,247,54,65,156,67,79,132,104,50,184,173,21,29,0,192,304,27,85,97,314,99,187,1057,643,746,7,257,1606,219,626,909,167,173,143,271,100,2,50,359,642,87,605,129,336,46,37,9,242,214,252,40,227,15,1812,144,270,1719,121,13,235,180,9,42,706,48,138,469,64,32,56,705,141,12,48,95,46,204,77,469,259,97,100,39,108,484,433,349,148,40,0,194,67,337,329,106,32,22,191,33,500,53,322,0,201,68,34,92,285,50,25,56,63,1892,0,94,2,41,268,598,126,232,182,169,608,207,90,271,41,327,80,158,124,3,101,177,173,14,1556,653,529,50,524,1356,300,184,180,893,317,14,137,209,218,1083,25,559,182,176,137,1594,68,222,0,125,199,189,352,251,213,42,1112,214,207,25,95,341,7,171,231,703,1265,216,206,62,61,0,222,422,443,459,85,886,136,27,259,617,2192,3,211,167,224,625,304,388,111,170,17,489,17,139,103,449,38,110,1120,152,34,35,1291,198,231,48,171,833,1011,203,55,158,604,364,252,129,478,1040,231,5,307,39,969,374,77,145,140,0,188,235,175,32,140,6,265,3,29,170,125,1,325,256,655,79,32,52,388,0,182,205,32,730,332,5,407,145,280,63,1058,10,204,247,166,273,99,681,127,505,187,292,204,86,114,28,242,372,527,72,267,123,126,312,812,83,9,46,69,222,297,13,387,40,349,80,258,89,244,75,49,290,54,0,282,237,279,349,50,316,104,1125,148,98,229,149,617,53,1218,30,105,190,64,97,253,788,23,13,311,35,5,11,165,413,106,371,3067,9,18,326,128,225,310,109,61,123,175,30,272,163,524,943,979,92,330,52,155,15,832,73,348,246,110,51,97,88,46,215,161,56,140,589,34,98,314,189,74,31,470,250,17,198,0,216,192,72,114,12,398,273,638,866,20,228,47,199,136,265,152,169,80,27,209,113,100,45,9,116,104,179,674,37,37,667,59,381,54,40,373,314,68,64,173,53,118,133,1248,397,79,440,120,133,131,4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5 | 1_SPTA1,36,7,80,215,90,24,138,5,16,82,60,81,26,52,35,14,13,12,0,81,129,41,4,135,1,192,32,11,34,8,32,10,192,104,20,29,64,17,79,28,122,84,18,18,18,39,38,123,40,33,43,64,91,5,0,30,1,25,32,51,69,58,14,5,21,3,104,0,246,10,55,17,85,74,0,6,26,346,36,2,45,138,57,48,17,80,47,25,105,4,53,100,165,61,46,163,193,18,61,28,61,43,80,94,88,9,20,54,132,50,18,42,34,127,11,79,4,11,22,26,14,29,63,52,0,4,75,13,141,41,11,106,59,85,70,18,0,62,39,3,81,55,131,98,24,170,129,13,56,119,123,112,118,157,0,36,53,52,102,61,38,61,0,69,20,95,77,104,59,50,75,12,30,61,31,5,0,39,64,19,9,26,179,49,89,87,82,0,55,39,64,23,38,7,29,25,8,39,69,25,45,131,8,119,46,4,3,87,122,90,14,24,28,37,12,59,23,64,22,26,28,54,62,90,125,56,39,28,41,13,47,10,55,10,28,48,152,50,97,31,33,25,48,78,66,23,10,16,168,6,69,9,71,323,3,17,11,11,28,45,13,30,7,23,8,11,40,33,240,56,26,121,51,23,24,22,4,61,5,41,89,47,7,36,38,52,6,30,2,52,72,31,35,111,99,27,24,20,19,28,16,55,45,10,6,81,27,10,65,10,35,13,23,25,15,6,14,134,12,13,179,24,33,79,1,2,19,54,31,84,52,30,15,118,82,79,25,55,65,74,50,56,151,113,86,66,44,173,108,56,48,80,94,55,5,118,25,44,3,28,82,25,82,27,22,67,82,85,62,34,125,34,98,85,26,4,52,15,56,223,80,2,80,86,73,36,23,23,80,28,19,37,57,41,7,60,75,104,11,6,160,77,30,39,70,69,53,22,41,36,103,5,27,1,88,41,56,81,20,151,82,31,20,38,30,36,38,51,3,27,45,27,14,57,4,63,13,6,68,21,61,20,18,24,16,88,53,52,13,59,0,1,185,156,73,146,87,33,111,81,76,222,105,75,91,133,283,95,44,150,29,12,19,141,44,82,48,212,16,37,42,176,74,102,5,31,56,25,30,90,32,27,11,64,84,0,118,31,18,9,206,64,49,14,110,13,33,33,69,45,54,27,21,73,31,40,2,103,21,16,84,41,53,115,43,17,48,17,19,29,101,3,6,51,192,18,41,142,70,39,17,25,16,1,46,16,40,38,11,29,67,186,1,35,29,98,60,8,135,67,53,26,71,47,37,25,36,54,43,171,63,13,14,18,113,108,86,39,35,12,85,125,54,33,86,62,64,9,54,48,8,34,50,4,29,12,5,8,4,110,189,34,176,16,56,23,0,90,46,23,52,31,92,4,11,45,73,64,62,55,18,121,0,50,72,60,173,71,17,20,20,111,67,16,231,8,179,38,33,83,26,41,71,71,6,37,0,5,44,64,82,19,10,31,21,46,35,38,138,17,52,50,43,82,47,8,59,22,57,45,70,84,35,0,42,150,16,169,34,18,24,110,24,154,3,57,62,48,57,59,33,45,31,41,72,20,30,37,82,5,121,21,6,26,80,30,63,12,71,13,56,49,23,73,104,46,71,84,184,42,19,63,19,119,54,119,36,116,115,35,30,32,100,50,79,91,8,33,31,82,54,45,37,75,52,106,59,7,2,5,66,20,32,62,60,103,39,15,24,61,33,23,122,37,64,10,6,14,77,34,26,22,17,28,24,80,6,10,6,38,18,23,16,118,142,8,18,19,177,26,42,14,57,28,149,130,51,21,81,123,28,18,180,50,135,63,44,28,86,5,94,32,91,40,61,66,23,47,55,87,49,17,14,83,20,62,31,19,91,124,40,55,76,62,17,15,95,24,189,73,23,39,47,46,49,27,28,21,113,5,32,35,45,96,12,21,11,54,11,23,175,74,16,22,21,1,36,100,72,22,16,66,45,115,22,104,130,92,350,45,23,28,86,1,6,51,21,45,165,36,40,40,42,29,48,18,28,8,332,144,10,223,8,49,10,7,9,45,56,45,77,6,6,37,94,42,134,43,20,42,21,15,20,108,75,109,13,254,50,49,47,52,20,9,11,74,96,115,10,41,174,105,109,17,91,4,7,217,123,34,44,32,13,96,70,99,45,39,412,15,245,80,23,39,19,57,112,45,40,24,165,46,96,105,10,37,38,36,51,80,0,100,79,67,49,68,45,48,221,44,6,184,184,20,35,81,214,7,81,197,480,40,32,184,153,550,67,28,189,74,139,4,87,185,18,135,499,160,94,58,32,139,73,283,15,80,235,73,94,17,5,881,165,71,43,232,166,189,95,83,50,170,123,44,473,185,110,191,9,180,127,100,292,107,184,111,137,43,107,220,33,50,242,169,373,87,20,70,163,23,66,313,138,110,64,21,56,374,22,197,132,76,124,135,77,92,215,199,135,173,2,89,448,7,148,268,440,178,45,267,301,358,48,10,164,31,279,276,186,173,39,24,6,142,111,815,200,402,194,43,288,38,92,143,139,20,41,42,200,83,189,77,174,34,169,209,415,271,310,37,119,599,17,35,122,248,67,119,376,90,107,106,42,266,29,47,64,388,239,162,56,206,77,141,376,12,8,173,83,574,83,294,527,1932,16,227,46,663,90,38,226,70,17,498,347,32,90,147,163,761,11,256,121,89,206,256,115,345,149,102,162,94,123,76,86,223,26,515,139,201,521,58,130,206,86,351,114,323,64,100,248,80,126,82,52,142,331,42,77,96,140,85,36,165,589,116,182,41,110,75,150,432,156,212,400,59,137,134,30,295,114,448,386,21,252,46,79,38,214,176,193,135,27,169,121,13,91,1031,185,289,167,30,15,339,283,272,120,161,6,321,42,16,31,202,140,119,42,213,233,116,94,282,237,83,7,100,177,154,33,40,78,377,214,108,147,3,156,278,98,576,130,234,25,61,138,19,135,98,273,85,301,107,202,88,128,73,45,726,102,13,406,158,302,137,212,38,161,37,156,29,45,166,253,485,156,208,264,87,179,3,85,19,35,465,164,57,60,170,51,662,47,117,105,69,124,169,144,28,106,53,44,164,150,88,163,46,80,136,75,226,165,472,182,105,27,20,244,138,135,214,241,72,150,247,112,63,50,76,41,133,184,152,218,155,184,64,0,97,31,120,4,9,100,337,79,112,162,142,299,10,41,262,303,104,140,132,98,72,37,111,14,123,654,154,56,133,23,177,73,184,91,38,77,242,252,234,8,154,115,172,394,222,316,188,53,271,171,70,45,75,83,351,23,195,332,42,224,53,255,137,21,116,141,128,99,242,217,194,327,349,399,239,212,337,25,107,61,143,55,138,46,70,219,81,45,251,265,94,81,243,241,282,105,56,169,419,5,304,54,326,586,17,199,112,36,225,100,54,56,42,40,436,92,232,81,156,493,72,406,536,428,713,75,341,61,80,108,124,282,141,103,27,63,287,192,1124,5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6 | -------------------------------------------------------------------------------- /data/PDAC/input/snp_list.txt: -------------------------------------------------------------------------------- 1 | chr10:131506192:C/T 2 | chr10:131506283:C/T 3 | chr1:158612236:A/G 4 | -------------------------------------------------------------------------------- /data/PDAC/input/sphyr_mutation_names.txt: -------------------------------------------------------------------------------- 1 | BRCA2 2 | TGFBR2_1 3 | TGFBR2_2 4 | FGFR1 5 | MGMT_1 6 | MGMT_2 7 | SPTA1 8 | -------------------------------------------------------------------------------- /data/sample/overview_B.csv: -------------------------------------------------------------------------------- 1 | ,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c15,c16,c17,c18,c19,c20,c21,c22,c23,c24 2 | s0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 3 | s1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0 4 | s2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0 5 | s3,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 6 | s4,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 7 | s5,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0 8 | s6,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1 9 | s7,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0 10 | s8,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1 11 | s9,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 12 | s10,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0 13 | s11,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 14 | s12,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0 15 | s13,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0 16 | s14,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 17 | s15,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0 18 | s16,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0 19 | s17,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0 20 | s18,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0 21 | s19,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1 22 | s20,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0 23 | s21,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0 24 | s22,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0 25 | s23,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0 26 | s24,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0 27 | -------------------------------------------------------------------------------- /data/sample/overview_character_matrix.csv: -------------------------------------------------------------------------------- 1 | ,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c15,c16,c17,c18,c19,c20,c21,c22,c23,c24,cluster_id 2 | s0,0,0,1,-1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 3 | s1,0,0,-1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,3 4 | s2,0,0,1,0,0,0,0,0,0,1,-1,0,-1,0,1,0,0,-1,0,0,0,0,0,0,0,0 5 | s3,-1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,1,0,0,0 6 | s4,0,0,1,0,-1,-1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,-1,0 7 | s5,0,-1,1,0,0,0,0,0,1,0,0,0,0,-1,0,0,0,0,0,0,1,0,0,0,0,3 8 | s6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,-1,0,-1,0,0,0,-1,1,3 9 | s7,0,-1,1,-1,0,-1,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,1,0,0,0,0,3 10 | s8,0,0,0,0,0,0,0,-1,-1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,3 11 | s9,0,0,0,0,-1,0,0,0,0,0,-1,1,-1,0,-1,0,0,0,0,0,0,0,0,-1,0,0 12 | s10,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,-1,2 13 | s11,0,0,0,-1,0,1,0,0,1,0,-1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 14 | s12,-1,0,1,0,0,-1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3 15 | s13,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,-1,0,0,1,0,0,0,0,4 16 | s14,0,0,0,0,0,0,1,0,-1,0,0,-1,0,-1,0,0,0,0,0,0,0,0,0,0,0,0 17 | s15,0,0,1,0,1,-1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0 18 | s16,0,0,-1,0,0,1,0,-1,0,-1,0,0,0,-1,0,-1,1,0,0,0,0,0,0,0,0,0 19 | s17,-1,-1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0 20 | s18,0,0,-1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,-1,1,0,0,0,-1,1,-1,0 21 | s19,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,3 22 | s20,0,0,1,0,0,0,0,0,0,0,0,-1,0,1,0,0,0,0,0,0,1,0,-1,0,0,0 23 | s21,0,0,0,0,0,0,0,0,-1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1 24 | s22,0,0,1,0,0,0,0,0,-1,0,0,0,0,1,0,0,0,0,0,0,-1,0,0,0,0,0 25 | s23,0,-1,0,0,-1,0,-1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1 26 | s24,0,-1,-1,-1,0,0,0,0,0,0,0,0,1,0,0,0,-1,0,1,0,0,0,0,1,0,0 27 | -------------------------------------------------------------------------------- /data/sample/overview_character_matrix_without_noise.csv: -------------------------------------------------------------------------------- 1 | ,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c15,c16,c17,c18,c19,c20,c21,c22,c23,c24,cluster_id 2 | s0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 3 | s1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3 4 | s2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 5 | s3,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 6 | s4,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 7 | s5,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3 8 | s6,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,3 9 | s7,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3 10 | s8,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,3 11 | s9,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 12 | s10,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2 13 | s11,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 14 | s12,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3 15 | s13,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4 16 | s14,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 17 | s15,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0 18 | s16,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0 19 | s17,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 20 | s18,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0 21 | s19,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,3 22 | s20,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0 23 | s21,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1 24 | s22,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0 25 | s23,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1 26 | s24,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0 27 | -------------------------------------------------------------------------------- /data/sample/overview_copynumbertree_scarlet.txt: -------------------------------------------------------------------------------- 1 | 0,1,c1,c2,c7,c10,c11,c14,c18,c19,c20,c21,c23 2 | 0,3,c1,c3,c7,c11,c12,c13,c14,c15,c16,c18,c19,c21,c24 3 | 0,2,c1,c3,c4,c11,c12,c14,c16,c19,c21,c24 4 | 3,4,c0,c5,c7,c8,c10,c17,c21,c24 5 | -------------------------------------------------------------------------------- /data/sample/overview_multi_state_character_matrix.csv: -------------------------------------------------------------------------------- 1 | ,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c15,c16,c17,c18,c19,c20,c21,c22,c23,c24,cluster_id 2 | s0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 3 | s1,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,3 4 | s2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 5 | s3,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 6 | s4,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 7 | s5,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,3 8 | s6,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,3 9 | s7,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,3 10 | s8,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,3 11 | s9,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 12 | s10,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2 13 | s11,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 14 | s12,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,3 15 | s13,0,0,1,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,4 16 | s14,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 17 | s15,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0 18 | s16,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0 19 | s17,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 20 | s18,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0 21 | s19,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,3 22 | s20,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0 23 | s21,1,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1 24 | s22,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0 25 | s23,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,1 26 | s24,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0 27 | -------------------------------------------------------------------------------- /data/sample/overview_multi_state_tree_node_character_matrix.csv: -------------------------------------------------------------------------------- 1 | ,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c15,c16,c17,c18,c19,c20,c21,c22,c23,c24,cluster_id 2 | root,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 3 | c2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 4 | c20,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 5 | c23,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 6 | c13,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0 7 | c11,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 8 | d1,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1 9 | d3,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,3 10 | d2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2 11 | c5,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 12 | c14,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 13 | c8,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,3 14 | c16,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0 15 | c22,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0 16 | c17,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0 17 | c1,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1 18 | c10,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,3 19 | d4,0,0,1,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,4 20 | c6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 21 | c4,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0 22 | c18,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0 23 | c19,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0 24 | c9,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 25 | c7,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0 26 | c24,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,3 27 | c3,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0 28 | c0,1,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1 29 | c21,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0 30 | c15,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,1 31 | c12,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0 32 | -------------------------------------------------------------------------------- /data/sample/overview_phiscs.txt: -------------------------------------------------------------------------------- 1 | cell_idx/mut_idx c0 c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13 c14 c15 c16 c17 c18 c19 c20 c21 c22 c23 c24 2 | s0 0 0 1 ? 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 3 | s1 0 0 ? 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 ? 0 0 4 | s2 0 0 1 0 0 0 0 0 0 1 ? 0 ? 0 1 0 0 ? 0 0 0 0 0 0 0 5 | s3 ? 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ? 1 0 0 6 | s4 0 0 1 0 ? ? 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 ? 7 | s5 0 ? 1 0 0 0 0 0 1 0 0 0 0 ? 0 0 0 0 0 0 1 0 0 0 0 8 | s6 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 ? 0 ? 0 0 0 ? 1 9 | s7 0 ? 1 ? 0 ? 0 0 0 0 0 0 ? 0 0 0 0 0 0 0 1 0 0 0 0 10 | s8 0 0 0 0 0 0 0 ? ? 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 11 | s9 0 0 0 0 ? 0 0 0 0 0 ? 1 ? 0 ? 0 0 0 0 0 0 0 0 ? 0 12 | s10 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 ? 13 | s11 0 0 0 ? 0 1 0 0 1 0 ? 0 0 0 0 0 0 0 0 0 0 0 1 0 0 14 | s12 ? 0 1 0 0 ? 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 | s13 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 ? 0 0 1 0 0 0 0 16 | s14 0 0 0 0 0 0 1 0 ? 0 0 ? 0 ? 0 0 0 0 0 0 0 0 0 0 0 17 | s15 0 0 1 0 1 ? 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 18 | s16 0 0 ? 0 0 1 0 ? 0 ? 0 0 0 ? 0 ? 1 0 0 0 0 0 0 0 0 19 | s17 ? ? 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 ? 0 0 0 0 0 20 | s18 0 0 ? 0 0 0 0 1 0 0 0 0 0 0 0 0 0 ? 1 0 0 0 ? 1 ? 21 | s19 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 22 | s20 0 0 1 0 0 0 0 0 0 0 0 ? 0 1 0 0 0 0 0 0 1 0 ? 0 0 23 | s21 0 0 0 0 0 0 0 0 ? 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 24 | s22 0 0 1 0 0 0 0 0 ? 0 0 0 0 1 0 0 0 0 0 0 ? 0 0 0 0 25 | s23 0 ? 0 0 ? 0 ? 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 26 | s24 0 ? ? ? 0 0 0 0 0 0 0 0 1 0 0 0 ? 0 1 0 0 0 0 1 0 27 | -------------------------------------------------------------------------------- /data/sample/overview_read_count.csv: -------------------------------------------------------------------------------- 1 | ,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c15,c16,c17,c18,c19,c20,c21,c22,c23,c24 2 | s0,52,55,39,0,61,48,55,49,45,43,35,59,61,46,50,49,55,58,55,51,56,55,59,48,48 3 | s1,46,47,0,38,60,49,42,42,49,51,38,46,51,50,47,47,59,48,54,48,46,55,0,54,54 4 | s2,70,51,45,51,54,44,53,57,47,40,0,60,0,63,63,57,51,0,35,43,61,39,53,58,53 5 | s3,0,41,46,42,49,50,52,48,51,61,55,49,60,54,51,49,54,38,51,58,47,0,52,54,33 6 | s4,47,60,53,55,0,0,42,48,53,50,49,57,53,58,62,40,54,55,41,49,45,48,50,53,0 7 | s5,48,0,49,46,47,48,56,44,60,47,52,58,54,0,46,56,50,56,48,46,65,63,52,45,46 8 | s6,44,53,57,43,38,63,49,56,55,51,63,54,57,46,50,38,56,0,63,0,50,60,42,0,44 9 | s7,41,0,43,0,63,0,50,47,50,67,59,46,0,45,48,49,42,58,44,65,54,65,52,50,44 10 | s8,38,51,47,48,67,58,53,0,0,43,64,48,62,56,45,52,49,45,45,50,50,39,38,54,52 11 | s9,57,46,46,37,0,55,53,46,42,47,0,56,0,59,0,47,45,54,40,53,52,57,46,0,44 12 | s10,42,50,52,59,52,50,41,49,41,45,47,52,41,49,48,46,56,51,44,51,44,58,54,45,0 13 | s11,52,52,46,0,43,45,51,40,39,55,0,55,49,50,51,41,61,44,39,45,49,56,53,55,57 14 | s12,0,52,40,64,55,0,45,44,48,53,48,50,57,44,48,61,44,45,53,41,45,59,48,53,49 15 | s13,45,54,35,52,58,54,40,64,50,42,53,40,43,59,45,41,48,0,53,55,56,55,50,33,56 16 | 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/data/sample/overview_read_count_without_missing.csv: -------------------------------------------------------------------------------- 1 | ,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c15,c16,c17,c18,c19,c20,c21,c22,c23,c24 2 | s0,52,55,39,41,61,48,55,49,45,43,35,59,61,46,50,49,55,58,55,51,56,55,59,48,48 3 | s1,46,47,49,38,60,49,42,42,49,51,38,46,51,50,47,47,59,48,54,48,46,55,41,54,54 4 | s2,70,51,45,51,54,44,53,57,47,40,52,60,43,63,63,57,51,45,35,43,61,39,53,58,53 5 | s3,47,41,46,42,49,50,52,48,51,61,55,49,60,54,51,49,54,38,51,58,47,50,52,54,33 6 | s4,47,60,53,55,34,55,42,48,53,50,49,57,53,58,62,40,54,55,41,49,45,48,50,53,55 7 | s5,48,40,49,46,47,48,56,44,60,47,52,58,54,41,46,56,50,56,48,46,65,63,52,45,46 8 | s6,44,53,57,43,38,63,49,56,55,51,63,54,57,46,50,38,56,65,63,55,50,60,42,42,44 9 | s7,41,50,43,39,63,64,50,47,50,67,59,46,52,45,48,49,42,58,44,65,54,65,52,50,44 10 | s8,38,51,47,48,67,58,53,53,53,43,64,48,62,56,45,52,49,45,45,50,50,39,38,54,52 11 | 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4 | 0 0 -1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 5 | 0 0 1 0 0 0 0 0 0 1 -1 0 -1 0 1 0 0 -1 0 0 0 0 0 0 0 6 | -1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 1 0 0 7 | 0 0 1 0 -1 -1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 -1 8 | 0 -1 1 0 0 0 0 0 1 0 0 0 0 -1 0 0 0 0 0 0 1 0 0 0 0 9 | 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 -1 0 -1 0 0 0 -1 1 10 | 0 -1 1 -1 0 -1 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 1 0 0 0 0 11 | 0 0 0 0 0 0 0 -1 -1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 12 | 0 0 0 0 -1 0 0 0 0 0 -1 1 -1 0 -1 0 0 0 0 0 0 0 0 -1 0 13 | 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 -1 14 | 0 0 0 -1 0 1 0 0 1 0 -1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 15 | -1 0 1 0 0 -1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 | 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 -1 0 0 1 0 0 0 0 17 | 0 0 0 0 0 0 1 0 -1 0 0 -1 0 -1 0 0 0 0 0 0 0 0 0 0 0 18 | 0 0 1 0 1 -1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 19 | 0 0 -1 0 0 1 0 -1 0 -1 0 0 0 -1 0 -1 1 0 0 0 0 0 0 0 0 20 | -1 -1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 21 | 0 0 -1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 -1 1 0 0 0 -1 1 -1 22 | 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 23 | 0 0 1 0 0 0 0 0 0 0 0 -1 0 1 0 0 0 0 0 0 1 0 -1 0 0 24 | 0 0 0 0 0 0 0 0 -1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 25 | 0 0 1 0 0 0 0 0 -1 0 0 0 0 1 0 0 0 0 0 0 -1 0 0 0 0 26 | 0 -1 0 0 -1 0 -1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 27 | 0 -1 -1 -1 0 0 0 0 0 0 0 0 1 0 0 0 -1 0 1 0 0 0 0 1 0 28 | -------------------------------------------------------------------------------- /data/sample/overview_tree.dot: -------------------------------------------------------------------------------- 1 | digraph N { 2 | overlap="false" 3 | rankdir="TB" 4 | 0 [label="root", style="bold"]; 5 | 1 [label="c2_1", style="bold"]; 6 | 2 [label="c5_1", style="bold"]; 7 | 3 [label="c22_1", style="bold"]; 8 | 4 [label="s0", style="bold"]; 9 | 5 [label="c20_1", style="bold"]; 10 | 6 [label="c8_1", style="bold"]; 11 | 7 [label="s1", style="bold"]; 12 | 8 [label="c9_1", style="bold"]; 13 | 9 [label="c14_1", style="bold"]; 14 | 10 [label="s2", style="bold"]; 15 | 11 [label="s3", style="bold"]; 16 | 12 [label="s4", style="bold"]; 17 | 13 [label="s5", style="bold"]; 18 | 14 [label="c24_1", style="bold"]; 19 | 15 [label="s6", style="bold"]; 20 | 16 [label="c10_1", style="bold"]; 21 | 17 [label="s7", style="bold"]; 22 | 18 [label="s8", style="bold"]; 23 | 19 [label="c11_1", style="bold"]; 24 | 20 [label="s9", style="bold"]; 25 | 21 [label="c13_1", style="bold"]; 26 | 22 [label="s10", style="bold"]; 27 | 23 [label="s11", style="bold"]; 28 | 24 [label="s12", style="bold"]; 29 | 25 [label="s13", style="bold"]; 30 | 26 [label="c6_1", style="bold"]; 31 | 27 [label="s14", style="bold"]; 32 | 28 [label="c23_1", style="bold"]; 33 | 29 [label="c17_1", style="bold"]; 34 | 30 [label="c4_1", style="bold"]; 35 | 31 [label="s15", style="bold"]; 36 | 32 [label="c19_1", style="bold"]; 37 | 33 [label="c16_1", style="bold"]; 38 | 34 [label="s16", style="bold"]; 39 | 35 [label="s17", style="bold"]; 40 | 36 [label="c18_1", style="bold"]; 41 | 37 [label="c7_1", style="bold"]; 42 | 38 [label="s18", style="bold"]; 43 | 39 [label="s19", style="bold"]; 44 | 40 [label="c3_1", style="bold"]; 45 | 41 [label="s20", style="bold"]; 46 | 42 [label="c2_2", style="bold"]; 47 | 43 [label="c20_2", style="bold"]; 48 | 44 [label="s21", style="bold"]; 49 | 45 [label="c21_1", style="bold"]; 50 | 46 [label="s22", style="bold"]; 51 | 47 [label="c15_1", style="bold"]; 52 | 48 [label="s23", style="bold"]; 53 | 49 [label="c12_1", style="bold"]; 54 | 50 [label="s24", style="bold"]; 55 | 0 -> 1 [style="bold"]; 56 | 0 -> 19 [style="bold"]; 57 | 0 -> 26 [style="bold"]; 58 | 1 -> 2 [style="bold"]; 59 | 1 -> 5 [style="bold"]; 60 | 1 -> 8 [style="bold"]; 61 | 1 -> 28 [style="bold"]; 62 | 2 -> 3 [style="bold"]; 63 | 2 -> 32 [style="bold"]; 64 | 3 -> 4 [style="bold"]; 65 | 3 -> 11 [style="bold"]; 66 | 3 -> 12 [style="bold"]; 67 | 3 -> 23 [style="bold"]; 68 | 5 -> 6 [style="bold"]; 69 | 5 -> 16 [style="bold"]; 70 | 5 -> 21 [style="bold"]; 71 | 6 -> 7 [style="bold"]; 72 | 6 -> 13 [style="bold"]; 73 | 6 -> 14 [style="bold"]; 74 | 6 -> 25 [style="bold"]; 75 | 8 -> 9 [style="bold"]; 76 | 9 -> 10 [style="bold"]; 77 | 9 -> 35 [style="bold"]; 78 | 14 -> 15 [style="bold"]; 79 | 14 -> 18 [style="bold"]; 80 | 14 -> 39 [style="bold"]; 81 | 16 -> 17 [style="bold"]; 82 | 16 -> 24 [style="bold"]; 83 | 19 -> 20 [style="bold"]; 84 | 21 -> 22 [style="bold"]; 85 | 21 -> 40 [style="bold"]; 86 | 21 -> 42 [style="bold"]; 87 | 21 -> 45 [style="bold"]; 88 | 26 -> 27 [style="bold"]; 89 | 28 -> 29 [style="bold"]; 90 | 28 -> 36 [style="bold"]; 91 | 29 -> 30 [style="bold"]; 92 | 30 -> 31 [style="bold"]; 93 | 32 -> 33 [style="bold"]; 94 | 33 -> 34 [style="bold"]; 95 | 36 -> 37 [style="bold"]; 96 | 36 -> 49 [style="bold"]; 97 | 37 -> 38 [style="bold"]; 98 | 40 -> 41 [style="bold"]; 99 | 42 -> 43 [style="bold"]; 100 | 43 -> 44 [style="bold"]; 101 | 43 -> 47 [style="bold"]; 102 | 45 -> 46 [style="bold"]; 103 | 47 -> 48 [style="bold"]; 104 | 49 -> 50 [style="bold"]; 105 | } -------------------------------------------------------------------------------- /data/sample/overview_tree.newick: -------------------------------------------------------------------------------- 1 | ((((s0,s3,s4,s11),s16),((s1,s5,(s6,s8,s19),s13),(s7,s12),(s10,s20,(s21,s23),s22)),(s2,s17),(s15,(s18,s24))),s9,s14); -------------------------------------------------------------------------------- /data/sample/overview_tree_edgelist.csv: -------------------------------------------------------------------------------- 1 | root,c2 2 | root,c11 3 | root,c6 4 | c2,c20 5 | c2,c23 6 | c2,c5 7 | c2,c14 8 | c20,c13 9 | c23,c17 10 | c23,c18 11 | c13,d1 12 | c13,d3 13 | c13,d2 14 | c13,c3 15 | c13,c21 16 | c11,s9 17 | d1,c1 18 | d1,c0 19 | d3,c8 20 | d3,c10 21 | d2,s10 22 | c5,c16 23 | c5,c22 24 | c14,c9 25 | c8,d4 26 | c8,c24 27 | c8,s1 28 | c8,s5 29 | c16,c19 30 | c22,s0 31 | c22,s3 32 | c22,s4 33 | c22,s11 34 | c17,c4 35 | c1,c15 36 | c10,s7 37 | c10,s12 38 | d4,s13 39 | c6,s14 40 | c4,s15 41 | c18,c7 42 | c18,c12 43 | c19,s16 44 | c9,s2 45 | c9,s17 46 | c7,s18 47 | c24,s6 48 | c24,s8 49 | c24,s19 50 | c3,s20 51 | c0,s21 52 | c21,s22 53 | c15,s23 54 | c12,s24 55 | -------------------------------------------------------------------------------- /data/sample/overview_tree_without_cells.dot: -------------------------------------------------------------------------------- 1 | digraph N { 2 | overlap="false" 3 | rankdir="TB" 4 | 0 [label="root", style="bold"]; 5 | 1 [label="c2_1", style="bold"]; 6 | 2 [label="c5_1", style="bold"]; 7 | 3 [label="c22_1", style="bold"]; 8 | 4 [label="c20_1", style="bold"]; 9 | 5 [label="c8_1", style="bold"]; 10 | 6 [label="c9_1", style="bold"]; 11 | 7 [label="c14_1", style="bold"]; 12 | 8 [label="c24_1", style="bold"]; 13 | 9 [label="c10_1", style="bold"]; 14 | 10 [label="c11_1", style="bold"]; 15 | 11 [label="c13_1", style="bold"]; 16 | 12 [label="c6_1", style="bold"]; 17 | 13 [label="c23_1", style="bold"]; 18 | 14 [label="c17_1", style="bold"]; 19 | 15 [label="c4_1", style="bold"]; 20 | 16 [label="c19_1", style="bold"]; 21 | 17 [label="c16_1", style="bold"]; 22 | 18 [label="c18_1", style="bold"]; 23 | 19 [label="c7_1", style="bold"]; 24 | 20 [label="c3_1", style="bold"]; 25 | 21 [label="c2_2", style="bold"]; 26 | 22 [label="c20_2", style="bold"]; 27 | 23 [label="c21_1", style="bold"]; 28 | 24 [label="c15_1", style="bold"]; 29 | 25 [label="c12_1", style="bold"]; 30 | 0 -> 1 [style="bold"]; 31 | 0 -> 10 [style="bold"]; 32 | 0 -> 12 [style="bold"]; 33 | 1 -> 2 [style="bold"]; 34 | 1 -> 4 [style="bold"]; 35 | 1 -> 6 [style="bold"]; 36 | 1 -> 13 [style="bold"]; 37 | 2 -> 3 [style="bold"]; 38 | 2 -> 16 [style="bold"]; 39 | 4 -> 5 [style="bold"]; 40 | 4 -> 9 [style="bold"]; 41 | 4 -> 11 [style="bold"]; 42 | 5 -> 8 [style="bold"]; 43 | 6 -> 7 [style="bold"]; 44 | 11 -> 20 [style="bold"]; 45 | 11 -> 21 [style="bold"]; 46 | 11 -> 23 [style="bold"]; 47 | 13 -> 14 [style="bold"]; 48 | 13 -> 18 [style="bold"]; 49 | 14 -> 15 [style="bold"]; 50 | 16 -> 17 [style="bold"]; 51 | 18 -> 19 [style="bold"]; 52 | 18 -> 25 [style="bold"]; 53 | 21 -> 22 [style="bold"]; 54 | 22 -> 24 [style="bold"]; 55 | } -------------------------------------------------------------------------------- /data/sample/overview_variant_count.csv: -------------------------------------------------------------------------------- 1 | ,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c15,c16,c17,c18,c19,c20,c21,c22,c23,c24 2 | s0,0,0,5,0,0,48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,59,0,0 3 | s1,0,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0 4 | s2,0,0,5,0,0,1,0,0,0,11,0,0,0,0,28,0,0,0,0,0,0,0,0,0,0 5 | s3,0,0,4,0,0,50,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,52,0,0 6 | s4,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,50,0,0 7 | s5,0,0,12,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0 8 | s6,0,0,5,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,9 9 | s7,0,0,8,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,14,0,0,0,0 10 | s8,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,1,8 11 | s9,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0 12 | s10,0,0,20,0,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,1,0,0,0,0 13 | s11,0,0,4,0,0,45,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,53,0,0 14 | s12,0,0,5,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,3,0,0,0,0 15 | s13,0,0,4,0,0,1,0,0,9,0,0,0,11,0,0,0,0,0,0,0,23,0,0,0,0 16 | s14,0,1,0,0,0,0,52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 17 | s15,0,0,20,0,37,0,0,0,0,0,0,0,0,0,0,0,0,44,0,0,0,0,0,28,0 18 | s16,0,0,0,0,0,49,0,0,0,0,0,0,0,0,0,0,14,0,1,4,0,0,0,0,0 19 | s17,0,0,17,0,0,0,0,0,0,21,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0 20 | s18,0,0,0,0,0,0,0,6,0,0,0,0,0,0,2,0,0,0,20,0,0,0,0,16,0 21 | s19,0,0,23,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,18,0,0,0,14 22 | s20,0,0,13,3,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,5,0,0,0,0 23 | s21,0,0,0,0,0,0,0,1,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0 24 | s22,0,0,19,0,0,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,4,0,0,0 25 | s23,0,0,0,0,0,0,0,0,0,0,0,0,0,28,0,2,0,0,0,0,0,0,0,0,0 26 | s24,0,0,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,14,0,1,0,0,11,0 27 | -------------------------------------------------------------------------------- /data/sample/overview_variant_count_without_missing.csv: -------------------------------------------------------------------------------- 1 | ,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c15,c16,c17,c18,c19,c20,c21,c22,c23,c24 2 | s0,0,0,5,0,0,48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,59,0,0 3 | s1,0,0,18,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0 4 | s2,0,0,5,0,0,1,0,0,0,11,0,0,0,0,28,0,0,0,0,0,0,0,0,0,0 5 | s3,0,0,4,0,0,50,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,52,0,0 6 | s4,0,0,17,0,0,55,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,50,0,0 7 | s5,0,0,12,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0 8 | s6,0,0,5,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,9 9 | s7,0,0,8,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,14,0,0,0,0 10 | s8,0,0,4,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,12,0,0,1,8 11 | s9,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0 12 | s10,0,0,20,0,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,1,0,0,0,0 13 | s11,0,0,4,0,0,45,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,53,0,0 14 | s12,0,0,5,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,3,0,0,0,0 15 | s13,0,0,4,0,0,1,0,0,9,0,0,0,11,0,0,0,0,0,0,0,23,0,0,0,0 16 | s14,0,1,0,0,0,0,52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 17 | s15,0,0,20,0,37,0,0,0,0,0,0,0,0,0,0,0,0,44,0,0,0,0,0,28,0 18 | s16,0,0,4,0,0,49,0,1,0,0,0,0,0,0,0,0,14,0,1,4,0,0,0,0,0 19 | s17,0,0,17,0,0,0,0,0,0,21,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0 20 | s18,0,0,9,0,0,0,0,6,0,0,0,0,0,0,2,0,0,0,20,0,0,0,0,16,0 21 | s19,0,0,23,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,18,0,0,0,14 22 | s20,0,0,13,3,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,5,0,0,0,0 23 | s21,0,0,0,0,0,0,0,1,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0 24 | s22,0,0,19,0,0,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,5,4,0,0,0 25 | s23,0,11,0,0,0,0,0,0,0,0,0,0,0,28,0,2,0,0,0,0,0,0,0,0,0 26 | s24,0,0,13,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,14,0,1,0,0,11,0 27 | -------------------------------------------------------------------------------- /data/simulation/Snakefile: -------------------------------------------------------------------------------- 1 | # simulation snakefile 2 | configfile: "config.yaml" 3 | 4 | seeds = [ i for i in range(config["nseeds"])] 5 | 6 | import itertools 7 | 8 | def filter_combinator(combinator): 9 | def filtered_combinator(*args, **kwargs): 10 | for wc_comb in combinator(*args, **kwargs): 11 | #if int(wc_comb[1][-1]) >= int(wc_comb[2][-1]): 12 | if int(wc_comb[1][-1]) == int(wc_comb[2][-1]): 13 | yield wc_comb 14 | return filtered_combinator 15 | 16 | filtered_product = filter_combinator(itertools.product) 17 | 18 | rule all: 19 | input: 20 | # simulation 21 | #expand('ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_read_count.csv', filtered_product, seed=seeds, ncells=config['ncells'], ncharacters=config['ncharacters'], nclusters=config['nclusters'], missing=config['missing_rate'], k=config['k']), 22 | #expand('ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_character_matrix.csv', filtered_product, seed=seeds, ncells=config['ncells'], ncharacters=config['ncharacters'], nclusters=config['nclusters'], missing=config['missing_rate'], k=config['k']), 23 | # condor 24 | #expand('condor/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_B.csv', filtered_product, seed=seeds, ncells=config['ncells'], ncharacters=config['ncharacters'], nclusters=config['nclusters'], missing=config['missing_rate'], k=config['k']), 25 | #expand('condor_reads/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_B.csv', filtered_product, seed=seeds, ncells=config['ncells'], ncharacters=config['ncharacters'], nclusters=config['nclusters'], missing=config['missing_rate'], k=config['k']), 26 | # scarlet 27 | #expand('scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.edgelist', filtered_product, seed=seeds, ncells=config['ncells'], ncharacters=config['ncharacters'], nclusters=config['nclusters'], missing=config['missing_rate'], k=config['k']), 28 | # sphyr 29 | #expand('sphyr/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.out', filtered_product, seed=seeds, ncells=config['ncells'], ncharacters=config['ncharacters'], nclusters=config['nclusters'], missing=config['missing_rate'], k=config['k']), 30 | # scite 31 | #expand('scite/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_ml0.newick', filtered_product, seed=seeds, ncells=config['ncells'], ncharacters=config['ncharacters'], nclusters=config['nclusters'], missing=config['missing_rate'], k=config['k']), 32 | # sifit 33 | #expand('sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_mlTree.newick', filtered_product, seed=seeds, ncells=config['ncells'], ncharacters=config['ncharacters'], nclusters=config['nclusters'], missing=config['missing_rate'], k=config['k']), 34 | #expand('sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_character_matrix.tsv', filtered_product, seed=seeds, ncells=config['ncells'], ncharacters=config['ncharacters'], nclusters=config['nclusters'], missing=config['missing_rate'], k=config['k']), 35 | # evaluate 36 | expand('results/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_{method}.csv', filtered_product, seed=seeds, ncells=config['ncells'], ncharacters=config['ncharacters'], nclusters=config['nclusters'], missing=config['missing_rate'], k=config['k'], method=config['eval_methods']), 37 | 38 | rule simulate: 39 | output: 40 | readcount_matrix="ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_read_count.csv", 41 | variantcount_matrix="ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_variant_count.csv", 42 | character_matrix="ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_character_matrix.csv", 43 | #sphyr_file='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_sphyr.txt', 44 | #scite_file='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_scite.txt', 45 | #phiscs_file='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_phiscs.txt', 46 | #sifit_file='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_sifit.txt', 47 | scarlet_file='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_readcounts_scarlet.csv', 48 | scarlet_file2='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_copynumbertree_scarlet.txt', 49 | params: 50 | prefix="ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}", 51 | fn=config['fn'], 52 | fp=config['fp'], 53 | mutation_rate=config['lambda'], 54 | vaf_threshold=config['vaf_threshold'], 55 | read_threshold=config['read_threshold'], 56 | log: 57 | std="ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.log", 58 | err="ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.err.log", 59 | benchmark: "ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.benchmark", 60 | shell: 61 | "python ../../src/simulation_reads.py -s {wildcards.seed} -o {params.prefix} " 62 | " -n {wildcards.ncells} -m {wildcards.ncharacters} -d {wildcards.missing} " 63 | " -p {wildcards.nclusters} -a {params.fp} -b {params.fn} -k {wildcards.k} -l {params.mutation_rate} " 64 | " -v > {log.std} 2> {log.err}" 65 | 66 | rule auxiliary: 67 | output: 68 | cellnames='auxiliary/n{ncells}_m{ncharacters}_cell_names.txt', 69 | genenames='auxiliary/n{ncells}_m{ncharacters}_gene_names.txt', 70 | params: 71 | prefix='auxiliary/n{ncells}_m{ncharacters}' 72 | shell: 73 | "python ../../scripts/auxiliary.py -n {wildcards.ncells} -m {wildcards.ncharacters} -o {params.prefix} " 74 | 75 | rule condor: 76 | output: 77 | B='condor/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_B.csv', 78 | input: 79 | character_matrix='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_character_matrix.csv', 80 | params: 81 | prefix='condor/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}', 82 | fp=config['estimated_fp'], 83 | fn=config['estimated_fn'], 84 | log: 85 | std='condor/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.log', 86 | err='condor/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.err.log', 87 | benchmark: 'condor/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.benchmark', 88 | shell: 89 | 'python ../../src/condor.py -i {input.character_matrix} -a {params.fp} -b {params.fn} -k {wildcards.k} ' 90 | ' -o {params.prefix} ' 91 | ' > {log.std} 2> {log.err} ' 92 | 93 | rule condor_reads: 94 | output: 95 | B='condor_reads/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_B.csv', 96 | input: 97 | character_matrix='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_character_matrix.csv', 98 | total_read_matrix='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_read_count.csv', 99 | variant_read_matrix='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_variant_count.csv', 100 | params: 101 | prefix='condor_reads/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}', 102 | fp=config['estimated_fp'], 103 | fn=config['estimated_fn'], 104 | log: 105 | std='condor_reads/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.log', 106 | err='condor_reads/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.err.log', 107 | benchmark: 'condor_reads/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.benchmark', 108 | shell: 109 | 'python ../../src/condor.py -i {input.character_matrix} -a {params.fp} -b {params.fn} -k {wildcards.k} ' 110 | ' -r {input.total_read_matrix} -v {input.variant_read_matrix} ' 111 | ' -o {params.prefix} ' 112 | ' > {log.std} 2> {log.err} ' 113 | 114 | rule scarlet: 115 | output: 116 | Bmatrix= 'scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.B_ancestor', 117 | input: 118 | readcount= 'ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_readcounts_scarlet.csv', 119 | copynumbertree= 'ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_copynumbertree_scarlet.txt', 120 | params: 121 | python_exec=config['python2_exec'], 122 | scarlet_exec=config['scarlet_exec'], 123 | prefix='scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}' 124 | log: 125 | std='scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.log', 126 | err='scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.err.log', 127 | benchmark: 'scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.benchmark', 128 | shell: 129 | '{params.python_exec} {params.scarlet_exec} {input.readcount} {input.copynumbertree} {params.prefix} ' 130 | ' > {log.std} 2> {log.err} ;' 131 | 132 | rule fix_scarlet: 133 | output: 134 | Bmatrix_fixed= 'scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.B_ancestor_fixed', 135 | input: 136 | Bmatrix= 'scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.B_ancestor', 137 | character_matrix='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_character_matrix.csv', 138 | params: 139 | prefix='scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}' 140 | shell: 141 | ' python ../../scripts/fix_scarlet_output.py -d {input.character_matrix} -i {params.prefix}.B_ancestor -o {params.prefix}' 142 | 143 | rule scarlet_plot: 144 | input: 145 | Bmatrix='scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.B_ancestor_fixed', 146 | copynumbertree= 'ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_copynumbertree_scarlet.txt', 147 | output: 148 | tree='scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.edgelist', 149 | params: 150 | python_exec=config['python2_exec'], 151 | scarlet_plot_exec=config['scarlet_plot_exec'], 152 | prefix='scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}' 153 | log: 154 | std='scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.plot.log', 155 | err='scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.plot.err.log', 156 | benchmark: 'scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.plot.benchmark', 157 | shell: 158 | '{params.python_exec} {params.scarlet_plot_exec} {input.Bmatrix} {input.copynumbertree} ALL {params.prefix} ' 159 | ' > {log.std} 2> {log.err} ' 160 | 161 | rule scarlet_star: 162 | output: 163 | #Bmatrix= 'scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_t{tree_idx}.B_ancestor', 164 | LLfile= 'scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_t{tree_idx}.LL', 165 | input: 166 | readcount= 'ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_readcounts_scarlet.csv', 167 | copynumbertree= 'cna_trees/m{ncharacters}_p{nclusters}_t{tree_idx}.txt', 168 | params: 169 | python_exec=config['python2_exec'], 170 | scarlet_exec=config['scarlet_exec'], 171 | prefix='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_t{tree_idx}' 172 | log: 173 | std='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_t{tree_idx}.log', 174 | err='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_t{tree_idx}.err.log', 175 | benchmark: 'scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_t{tree_idx}.benchmark', 176 | shell: 177 | '{params.python_exec} {params.scarlet_exec} {input.readcount} {input.copynumbertree} {params.prefix} ' 178 | ' > {log.std} 2> {log.err} ;' 179 | 180 | rule parse_scarlet_star: 181 | output: 182 | Bmatrix='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.B_ancestor', 183 | copynumbertree='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_copynumber_tree_scarlet_star.txt', 184 | input: 185 | LLfile='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_t0.LL', 186 | #Bfile='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_t0.B_ancestor', 187 | params: 188 | dir='scarlet_star', 189 | cna='cna_trees', 190 | log: 191 | std='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.log', 192 | err='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.err.log', 193 | benchmark: 'scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.benchmark', 194 | shell: 195 | 'python ../../src/parse_scarlet_star.py -n {wildcards.ncells} -m {wildcards.ncharacters} -p {wildcards.nclusters} -k {wildcards.k} -d {wildcards.missing} ' 196 | ' -s {wildcards.seed} --dir {params.dir} --cna {params.cna} ' 197 | ' > {log.std} 2> {log.err} ;' 198 | 199 | rule fix_scarlet_star: 200 | output: 201 | Bmatrix_fixed= 'scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.B_ancestor_fixed', 202 | input: 203 | Bmatrix= 'scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.B_ancestor', 204 | character_matrix='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_character_matrix.csv', 205 | params: 206 | prefix='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}' 207 | shell: 208 | ' python ../../scripts/fix_scarlet_output.py -d {input.character_matrix} -i {params.prefix}.B_ancestor -o {params.prefix}' 209 | 210 | rule scarlet_star_plot: 211 | input: 212 | Bmatrix='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.B_ancestor_fixed', 213 | copynumbertree= 'scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_copynumber_tree_scarlet_star.txt', 214 | output: 215 | tree='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.edgelist', 216 | params: 217 | python_exec=config['python2_exec'], 218 | scarlet_plot_exec=config['scarlet_plot_exec'], 219 | prefix='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}' 220 | log: 221 | std='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.plot.log', 222 | err='scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.plot.err.log', 223 | benchmark: 'scarlet_star/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.plot.benchmark', 224 | shell: 225 | '{params.python_exec} {params.scarlet_plot_exec} {input.Bmatrix} {input.copynumbertree} ALL {params.prefix} ' 226 | ' > {log.std} 2> {log.err} ' 227 | 228 | 229 | rule sphyr: 230 | output: 231 | result='sphyr/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.out', 232 | input: 233 | character_matrix='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_sphyr.txt', 234 | params: 235 | exec=config['sphyr_exec'], 236 | lT=lambda wildcards: int(wildcards.ncharacters) + int(wildcards.nclusters), 237 | fn=config['estimated_fn'], 238 | fp=config['estimated_fp'], 239 | log: 240 | std='sphyr/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.log', 241 | err='sphyr/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.err.log', 242 | benchmark: 'sphyr/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.benchmark', 243 | shell: 244 | '{params.exec} {input.character_matrix} {output.result} ' 245 | ' -a {params.fp} -b {params.fn} -lT {params.lT} -lC {wildcards.ncharacters} -k {wildcards.k} -v ' 246 | ' > {log.std} 2> {log.err} ' 247 | 248 | rule scite: 249 | output: 250 | result='scite/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_ml0.newick', 251 | input: 252 | character_matrix='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_scite.txt', 253 | params: 254 | prefix='scite/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}', 255 | niter=config['scite_niter'], 256 | nrestarts=config['scite_nrestarts'], 257 | exec=config['scite_exec'], 258 | fn=config['estimated_fn'], 259 | fp=config['estimated_fp'], 260 | benchmark: 'scite/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.benchmark', 261 | log: 262 | std='scite/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.log', 263 | err='scite/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.err.log', 264 | shell: 265 | '{params.exec} -i {input.character_matrix} -n {wildcards.ncharacters} -m {wildcards.ncells} -o {params.prefix} -a ' 266 | ' -cc 0 -l {params.niter} -r {params.nrestarts} -fd {params.fp} -ad {params.fn} 0 -max_treelist_size 1 ' 267 | ' > {log.std} 2> {log.err} ' 268 | 269 | rule sifit: 270 | output: 271 | result="sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_mlTree.newick", 272 | input: 273 | character_matrix='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_sifit.txt', 274 | params: 275 | input_prefix='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}', 276 | output_prefix='sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}', 277 | exec=config['sifit_exec'], 278 | niter=config['sifit_niter'], 279 | nrestarts=config['sifit_nrestarts'], 280 | fn=config['estimated_fn'], 281 | fp=config['estimated_fp'], 282 | benchmark: "sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.benchmark", 283 | log: 284 | std = "sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.log", 285 | err = "sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.err.log", 286 | shell: 287 | "java -jar {params.exec} -ipMat {input.character_matrix} -m {wildcards.ncells} -n {wildcards.ncharacters} -df 0 " 288 | " -l {params.niter} -r {params.nrestarts} -fp {params.fp} -fn {params.fn} " 289 | " > {log.std} 2> {log.err}; mv {params.input_prefix}_sifit_mlTree.newick {params.output_prefix}_mlTree.newick" 290 | 291 | def parse_sifit_fn(wc): 292 | with open(f'sifit/n{wc.ncells}_m{wc.ncharacters}_p{wc.nclusters}_k{wc.k}_d{wc.missing}_s{wc.seed}.log', 'r') as inp: 293 | for line in inp: 294 | if line.startswith('best false negative rate'): 295 | return float(line.rstrip('\n').split(' ')[-1]) 296 | 297 | def parse_sifit_w(wc): 298 | with open(f'sifit/n{wc.ncells}_m{wc.ncharacters}_p{wc.nclusters}_k{wc.k}_d{wc.missing}_s{wc.seed}.log', 'r') as inp: 299 | for line in inp: 300 | if line.startswith('best LOH parameter'): 301 | return float(line.rstrip('\n').split(' ')[-1]) 302 | 303 | def parse_sifit_d(wc): 304 | with open(f'sifit/n{wc.ncells}_m{wc.ncharacters}_p{wc.nclusters}_k{wc.k}_d{wc.missing}_s{wc.seed}.log', 'r') as inp: 305 | for line in inp: 306 | if line.startswith('best deletion parameter'): 307 | return float(line.rstrip('\n').split(' ')[-1]) 308 | 309 | rule sifit_ancestral: 310 | output: 311 | sifit_ancestral='sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_character_matrix.tsv', 312 | input: 313 | sifit_newick="sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_mlTree.newick", 314 | sifit_log='sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.log', 315 | cell_names='auxiliary/n{ncells}_m{ncharacters}_cell_names.txt', 316 | gene_names='auxiliary/n{ncells}_m{ncharacters}_gene_names.txt', 317 | character_matrix='ground_truth/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_sifit.txt', 318 | params: 319 | inferred_fn=parse_sifit_fn, 320 | inferred_w=parse_sifit_w, 321 | inferred_d=parse_sifit_d, 322 | fp=config['estimated_fp'], 323 | exec=config['sifit_exec'], 324 | benchmark: "sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.ancestral.benchmark", 325 | log: 326 | std = "sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.ancestral.log", 327 | err = "sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.ancestral.err.log", 328 | shell: 329 | " java -cp {params.exec} SiFit.algorithm.InferAncestralStates -ipMat {input.character_matrix} -tree {input.sifit_newick} " 330 | " -cellNames {input.cell_names} -geneNames {input.gene_names} -expectedMatrix {output.sifit_ancestral} " 331 | " -fp {params.fp} -fn {params.inferred_fn} -w {params.inferred_w} -d {params.inferred_d} -df 0 " 332 | " > {log.std} 2> {log.err}" 333 | 334 | 335 | def get_method_output(wc): 336 | ncells = wc.ncells 337 | ncharacters = wc.ncharacters 338 | nclusters = wc.nclusters 339 | k = wc.k 340 | missing = wc.missing 341 | seed = wc.seed 342 | 343 | if wc.method == 'scite': 344 | return 'scite/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_ml0.newick' 345 | if wc.method == 'sphyr': 346 | return 'sphyr/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.out' 347 | if wc.method == 'condor': 348 | return 'condor/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_B.csv' 349 | if wc.method == 'condorreads': 350 | return 'condor_reads/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_B.csv' 351 | if wc.method == 'sifit': 352 | return 'sifit/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_character_matrix.tsv' 353 | if wc.method == 'scarlet': 354 | return 'scarlet/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}.edgelist' 355 | 356 | rule evaluate: 357 | output: 358 | result='results/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_{method}.csv', 359 | input: 360 | method_output=get_method_output, 361 | params: 362 | sim_dir=config['sim_dir'], 363 | log: 364 | std='results/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_{method}.log', 365 | err='results/n{ncells}_m{ncharacters}_p{nclusters}_k{k}_d{missing}_s{seed}_{method}.err.log', 366 | shell: 367 | "python ../../src/evaluate_reads.py -s {wildcards.seed} --method {wildcards.method} -o {output.result} " 368 | " -n {wildcards.ncells} -m {wildcards.ncharacters} -d {wildcards.missing} " 369 | " -p {wildcards.nclusters} -k {wildcards.k} " 370 | " --dir {params.sim_dir} " 371 | " > {log.std} 2> {log.err} " 372 | -------------------------------------------------------------------------------- /data/simulation/config.yaml: -------------------------------------------------------------------------------- 1 | # simulation parameters 2 | ncells: 3 | - 25 4 | - 50 5 | - 100 6 | #- 1000 7 | 8 | ncharacters: 9 | - 25 10 | - 50 11 | - 100 12 | 13 | nclusters: 14 | - 3 15 | - 5 16 | 17 | fp: 0.001 18 | fn: 0.001 19 | lambda: 0.8 20 | 21 | missing_rate: 22 | - 0 23 | - 0.1 24 | 25 | k: 26 | - 1 27 | - 2 28 | - 3 29 | 30 | vaf_threshold: 0.1 31 | read_threshold: 5 32 | 33 | estimated_fp: 0.0018 34 | estimated_fn: 0.16 35 | 36 | nseeds: 5 37 | 38 | # scarlet parameters 39 | python2_exec: '/n/fs/ragr-data/users/palash/anaconda3/envs/scarlet/bin/python2' 40 | scarlet_exec: '/n/fs/ragr-data/users/palash/scarlet/code/scarlet.py' 41 | scarlet_plot_exec: '/n/fs/ragr-data/users/palash/scarlet/code/plot_tree.py' 42 | 43 | # sphyr parameters 44 | sphyr_exec: '/n/fs/ragr-data/users/palash/SPhyR/build/kDPFC' 45 | 46 | # scite parameters 47 | scite_nrestarts: 1 48 | scite_niter: 1000000 49 | scite_exec: '/n/fs/ragr-data/users/palash/SCITE/scite' 50 | 51 | # sifit parameters 52 | sifit_nrestarts: 1 53 | sifit_niter: 1000000 54 | sifit_exec: '/n/fs/ragr-data/users/palash/SiFit/SiFit.jar' 55 | 56 | # evaluation parameters 57 | eval_methods: 58 | - condorreads 59 | - sphyr 60 | - scite 61 | - sifit 62 | - scarlet 63 | 64 | sim_dir: '/n/fs/ragr-data/users/palash/constrained-Dollo/data/simulation_reads/' 65 | -------------------------------------------------------------------------------- /scripts/auxiliary.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Wed May 28 2022 5 | 6 | @author: Palash Sashittal 7 | """ 8 | 9 | import pandas as pd 10 | import sys 11 | import argparse 12 | import math 13 | import numpy as np 14 | 15 | def main(args): 16 | 17 | n = args.n 18 | m = args.m 19 | prefix = args.o 20 | cell_names_string = ' '.join([f'sc{idx}' for idx in range(1,n+1)]) 21 | 22 | with open(f'{prefix}_cell_names.txt', 'w') as out: 23 | out.write(cell_names_string) 24 | 25 | with open(f'{prefix}_gene_names.txt', 'w') as out: 26 | for idx in range(m): 27 | out.write(f'{idx}\n') 28 | 29 | 30 | if __name__ == "__main__": 31 | parser = argparse.ArgumentParser() 32 | parser.add_argument('-n', type=int, help='number of samples', required=True) 33 | parser.add_argument('-m', type=int, help='number of SNV mutations', required=True) 34 | parser.add_argument('-o', type=str, help='output prefix', required=True) 35 | args = parser.parse_args(None if sys.argv[1:] else ['-h']) 36 | 37 | main(args) 38 | -------------------------------------------------------------------------------- /scripts/fix_scarlet_output.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Wed Jun 14 2022 5 | 6 | @author: Palash Sashittal 7 | """ 8 | 9 | import pandas as pd 10 | import sys 11 | import argparse 12 | import itertools 13 | import math 14 | import numpy as np 15 | 16 | import networkx as nx 17 | 18 | def main(args): 19 | 20 | df_B_ancestor = pd.read_csv(args.i, index_col = 0) 21 | df_character_matrix = pd.read_csv(args.d, index_col = 0) 22 | 23 | df_B_ancestor_fixed = df_B_ancestor.copy() 24 | for cell in df_character_matrix.index: 25 | if cell not in df_B_ancestor.index: 26 | cluster_id = df_character_matrix.loc[cell]['cluster_id'] 27 | ancestor_data = df_B_ancestor.loc[f'ANC:{cluster_id}'] 28 | ancestor_data['CN'] = cluster_id 29 | df_B_ancestor_fixed.loc[cell] = ancestor_data 30 | 31 | df_B_ancestor_fixed.to_csv(f'{args.o}.B_ancestor_fixed') 32 | 33 | if __name__ == "__main__": 34 | parser = argparse.ArgumentParser() 35 | parser.add_argument('-i', type=str, help='possibly incorrect scarlet file', required=True) 36 | parser.add_argument('-d', type=str, help='input character matrix', required=True) 37 | parser.add_argument('-o', type=str, help='output prefix', required=True) 38 | args = parser.parse_args(None if sys.argv[1:] else ['-h']) 39 | 40 | main(args) -------------------------------------------------------------------------------- /src/condor.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Wed Mar 5 2021 5 | 6 | @author: Palash Sashittal 7 | """ 8 | 9 | import pandas as pd 10 | import sys 11 | import argparse 12 | import itertools 13 | import math 14 | import numpy as np 15 | 16 | from solveConstrainedDollo import solveConstrainedDollo 17 | 18 | def tree_to_newick(T, root=None): 19 | if root is None: 20 | roots = list(filter(lambda p: p[1] == 0, T.in_degree())) 21 | assert 1 == len(roots) 22 | root = roots[0][0] 23 | subgs = [] 24 | while len(T[root]) == 1: 25 | root = list(T[root])[0] 26 | for child in T[root]: 27 | while len(T[child]) == 1: 28 | child = list(T[child])[0] 29 | if len(T[child]) > 0: 30 | child_newick = tree_to_newick(T, root=child) 31 | if child_newick != '()': 32 | subgs.append(child_newick) 33 | else: 34 | #if child.startswith('s'): 35 | subgs.append(child) 36 | # return "(" + ','.join(map(str, subgs)) + ")" 37 | if len(subgs) == 1: 38 | return str(subgs[0]) 39 | else: 40 | return "(" + ','.join(map(str, subgs)) + ")" 41 | 42 | def main(args): 43 | 44 | df_character_matrix = pd.read_csv(f'{args.i}', index_col = 0) 45 | if args.r is not None: 46 | df_total_readcounts = pd.read_csv(f'{args.r}', index_col = 0) 47 | if args.v is not None: 48 | df_variant_readcounts = pd.read_csv(f'{args.v}', index_col = 0) 49 | 50 | snp_list = [] 51 | if args.s is not None: 52 | with open(args.s, 'r') as inp: 53 | for line in inp: 54 | snp_list.append(line.rstrip('\n')) 55 | 56 | k = args.k 57 | fp = args.a 58 | fn = args.b 59 | ado = args.ado 60 | 61 | if args.r is not None: 62 | solver = solveConstrainedDollo(df_character_matrix, df_total_readcounts=df_total_readcounts, 63 | df_variant_readcounts=df_variant_readcounts, k=k, fp=fp, fn=fn, 64 | ado_precision = ado, snp_list=snp_list) 65 | else: 66 | solver = solveConstrainedDollo(df_character_matrix, k=k, fp=fp, fn=fn) 67 | 68 | solver.solveSetInclusion() 69 | 70 | prefix = args.o 71 | solver.writeSolution(f'{prefix}_B.csv') 72 | solver.writeDOT(f'{prefix}_tree.dot') 73 | solver.writeDOT(f'{prefix}_tree_without_cells.dot', withcells=False) 74 | 75 | if solver.solT_cell is not None: 76 | with open(f'{prefix}_tree.newick', 'w') as out: 77 | out.write(tree_to_newick(solver.solT_cell) + ';') 78 | 79 | if __name__ == "__main__": 80 | parser = argparse.ArgumentParser() 81 | parser.add_argument('-i', type=str, help='csv file with mutation matrix and cluster id') 82 | parser.add_argument('-r', type=str, help='csv file with total read count matrix') 83 | parser.add_argument('-v', type=str, help='csv file with variant read count matrix') 84 | # parser.add_argument('--ado', type=float, help='allelic dropout rate [0.15]', default=0.15) 85 | parser.add_argument('-s', type=str, help='file containing list of SNPs') 86 | parser.add_argument('-a', type=float, help='false positive error rate [0.001]', default = 0.001) 87 | parser.add_argument('-b', type=float, help='false negative error rate [0.001]', default = 0.001) 88 | parser.add_argument('--ado', type=float, help='precision parameter for ADO', default=15) 89 | parser.add_argument('-k', type=int, help='maximum number of losses for an SNV', default = 0) 90 | parser.add_argument('-o', type=str, help='output prefix', required=True) 91 | parser.add_argument('-t', type=int, help='time limit in seconds [1800]', default = 1800) 92 | 93 | args = parser.parse_args(None if sys.argv[1:] else ['-h']) 94 | 95 | if args.i is None and (args.r is None and args.v is None): 96 | raise Exception("please provide either the binarized mutation matrix, or the total and variant readcount matrices!") 97 | 98 | main(args) 99 | -------------------------------------------------------------------------------- /src/evaluate_reads.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Wed May 5 2022 5 | 6 | @author: Palash Sashittal 7 | """ 8 | 9 | import pandas as pd 10 | import sys 11 | import argparse 12 | import itertools 13 | import math 14 | import numpy as np 15 | 16 | import networkx as nx 17 | import ete3 18 | from solveConstrainedDollo import solveConstrainedDollo 19 | 20 | sys.setrecursionlimit(1500) 21 | 22 | def get_scarlet_tree_dataframe(scarlet_fname, scarlet_edgelist_fname): 23 | 24 | df_corrected = pd.read_csv(scarlet_fname, index_col = 0) 25 | 26 | T_scarlet_nx = nx.DiGraph() 27 | with open(scarlet_edgelist_fname, 'r') as inp: 28 | for line in inp: 29 | nodes = line.rstrip('\n').split(',') 30 | parent_node = nodes[0].split(' ')[0] 31 | child_node = nodes[1].split(' ')[0] 32 | 33 | parent_type = parent_node.split(':')[0] 34 | parent_name = parent_node.split(':')[1] 35 | child_type = child_node.split(':')[0] 36 | child_name = child_node.split(':')[1] 37 | 38 | if parent_type == 'ROOT': 39 | parent_node_name = f'r{parent_name}' 40 | elif parent_type == 'MUT': 41 | parent_node_name = f'{parent_name}_1' 42 | else: 43 | parent_node_name = parent_name 44 | 45 | if child_type == 'ROOT': 46 | child_node_name = f'r{child_name}' 47 | elif child_type == 'MUT': 48 | child_node_name = f'{child_name}_1' 49 | else: 50 | child_node_name = child_name 51 | 52 | T_scarlet_nx.add_edge(parent_node_name, child_node_name) 53 | 54 | Tsol = ete3.Tree(tree_to_newick(T_scarlet_nx) + ';') 55 | 56 | return Tsol, df_corrected, T_scarlet_nx 57 | 58 | def get_condor_tree_dataframe(condor_fname, condor_newick): 59 | df_multi = pd.read_csv(condor_fname, index_col = 0) 60 | 61 | df_corrected = df_multi.copy() 62 | df_corrected[df_corrected > 1] = 0 63 | 64 | Tsol = ete3.Tree(condor_newick, format=1) 65 | 66 | df_binary = solveConstrainedDollo.expand_multi_state_to_binary(df_multi) 67 | _, nxtree_condor = solveConstrainedDollo.generate_perfect_phylogeny(df_binary) 68 | 69 | return Tsol, df_corrected, nxtree_condor 70 | 71 | def read_sphyr(fname): 72 | 73 | with open(fname, 'r') as inp: 74 | idx = 0 75 | data = [] 76 | for line in inp: 77 | if idx == 0: 78 | n = int(line.split(' ')[0]) 79 | elif idx == 1: 80 | m = int(line.split(' ')[0]) 81 | else: 82 | data.append(list(map(int, line.split(' ')))) 83 | idx += 1 84 | 85 | return pd.DataFrame(data, columns = [f'c{idx}' for idx in range(m)], index = [f's{idx}' for idx in range(n)]) 86 | 87 | def tree_to_newick(T, root=None): 88 | if root is None: 89 | roots = list(filter(lambda p: p[1] == 0, T.in_degree())) 90 | assert 1 == len(roots) 91 | root = roots[0][0] 92 | subgs = [] 93 | while len(T[root]) == 1: 94 | root = list(T[root])[0] 95 | for child in T[root]: 96 | while len(T[child]) == 1: 97 | child = list(T[child])[0] 98 | if len(T[child]) > 0: 99 | child_newick = tree_to_newick(T, root=child) 100 | if child_newick != '()': 101 | subgs.append(child_newick) 102 | else: 103 | if child.startswith('s'): 104 | subgs.append(child) 105 | # return "(" + ','.join(map(str, subgs)) + ")" 106 | if len(subgs) == 1: 107 | return str(subgs[0]) 108 | else: 109 | return "(" + ','.join(map(str, subgs)) + ")" 110 | 111 | def get_sphyr_tree_dataframe(sphyr_fname): 112 | 113 | df_multi = read_sphyr(sphyr_fname) 114 | df_binary = solveConstrainedDollo.expand_multi_state_to_binary(df_multi) 115 | _, nxtree_sphyr = solveConstrainedDollo.generate_perfect_phylogeny(df_binary) 116 | 117 | df_corrected = df_multi.copy() 118 | df_corrected[df_corrected > 1] = 0 119 | 120 | T_sphyr = ete3.Tree(tree_to_newick(nxtree_sphyr)+';', format=1) 121 | 122 | return T_sphyr, df_corrected, nxtree_sphyr 123 | 124 | def get_sifit_tree_dataframe(sifit_fname, sifit_newick): 125 | df_corrected = pd.read_csv(sifit_fname, sep='\t', header=None, index_col = 0) 126 | n = len(df_corrected) 127 | m = len(df_corrected.columns) 128 | 129 | df_corrected.rename(columns={idx: f'c{idx-1}' for idx in range(1,m+1)}, index={f'sc{idx}': f's{idx-1}' for idx in range(1, n+1)}, inplace=True) 130 | df_corrected = df_corrected.loc[[f's{idx}' for idx in range(n)]] 131 | df_corrected = df_corrected.sort_index() 132 | 133 | sifit_newick_string = '' 134 | with open(sifit_newick, 'r') as inp: 135 | for line in inp: 136 | sifit_newick_string += line.rstrip('\n') 137 | 138 | mod_sifit_newick_list = [] 139 | for idx, string in enumerate(sifit_newick_string.split('sc')): 140 | if idx == 0: 141 | mod_sifit_newick_list.append(string) 142 | else: 143 | splited_string = string.split(':') 144 | splited_string[0] = str(int(splited_string[0]) - 1) 145 | mod_string = ':'.join(splited_string) 146 | mod_sifit_newick_list.append('s' + mod_string) 147 | 148 | T_sifit = ete3.Tree(''.join(mod_sifit_newick_list)) 149 | 150 | return T_sifit, df_corrected 151 | 152 | def get_scite_tree_dataframe(n, m, gv_fname): 153 | 154 | # scite_nx_tree = nx.DiGraph() 155 | # with open(gv_fname, 'r') as inp: 156 | # for line in inp: 157 | # if not line.startswith('digraph') and not line.startswith('node') and not line.startswith('}'): 158 | # data = line.rstrip(';\n').split(' -> ') 159 | # scite_nx_tree.add_edge(data[0], data[1]) 160 | # T_scite = ete3.Tree(tree_to_newick(scite_nx_tree) + ';') 161 | 162 | gv_file = open(gv_fname, 'r') 163 | gv_file.readline() 164 | gv_file.readline() 165 | 166 | pi = [-2 for i in range(m+1)] 167 | for _ in range(m): 168 | line = gv_file.readline() 169 | data = line.rstrip(";\n").split() 170 | source = int(data[0]) - 1 171 | target = int(data[2]) - 1 172 | assert 0 <= target < m 173 | parent = -2 174 | if source == m: 175 | parent = -1 176 | else: 177 | parent = source 178 | pi[target] = parent 179 | 180 | samples = [-2 for i in range(n)] 181 | gv_file.readline() 182 | 183 | for _ in range(n): 184 | line = gv_file.readline() 185 | data = line.rstrip(";\n").split() 186 | source = int(data[0]) - 1 187 | target = int(data[2][1:]) 188 | parent = -2 189 | if source == n: 190 | parent = -1 191 | else: 192 | parent = source 193 | samples[target] = source 194 | 195 | scite_corrected = [] 196 | for p in range(len(samples)): 197 | states = [ 0 for c in range(len(pi)) ] 198 | parent = samples[p] 199 | while parent != -1: 200 | #print p, parent 201 | states[parent] = 1 202 | parent = pi[parent] 203 | scite_corrected.append(states[:len(pi) - 1]) 204 | 205 | df_corrected = pd.DataFrame(scite_corrected, columns = [f'c{idx}' for idx in range(m)], index = [f's{idx}' for idx in range(n)]) 206 | 207 | df_corrected_modified = df_corrected.copy() 208 | df_corrected_modified.columns = [f'{x}_1' for x in df_corrected.columns] 209 | _, nxtree_scite = solveConstrainedDollo.generate_perfect_phylogeny(df_corrected_modified) 210 | T_scite = ete3.Tree(tree_to_newick(nxtree_scite) + ';') 211 | 212 | return T_scite, df_corrected, nxtree_scite 213 | 214 | def get_descendant_mutations(T, node): 215 | if node not in T.nodes: 216 | return [] 217 | if node.startswith('c'): 218 | descendants = [node] 219 | else: 220 | return [] 221 | for child in T[node]: 222 | descendants += get_descendant_mutations(T, child) 223 | return descendants 224 | 225 | def get_clustered_mutations(T, node): 226 | if node not in T.nodes: 227 | return [] 228 | if node.startswith('c'): 229 | clustered = [node] 230 | if len(list(T[node])) == 1: 231 | child = list(T[node])[0] 232 | if child.startswith('c'): 233 | clustered += get_clustered_mutations(T, child) 234 | else: 235 | return [] 236 | return clustered 237 | 238 | def main(args): 239 | 240 | n = args.n 241 | m = args.m 242 | p = args.p 243 | k = args.k 244 | d = args.d 245 | if d == 0: 246 | d = int(d) 247 | s = args.s 248 | 249 | output_fname = args.o 250 | 251 | method = args.method 252 | simulation_dir = args.dir 253 | 254 | if method == 'condor': 255 | 256 | condor_fname = f'{simulation_dir}/condor/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}_B.csv' 257 | condor_newick = f'{simulation_dir}/condor/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}_tree.newick' 258 | Tsol, df_corrected, nxtree_sol = get_condor_tree_dataframe(condor_fname, condor_newick) 259 | 260 | if method == 'condorreads': 261 | 262 | condor_fname = f'{simulation_dir}/condor_reads/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}_B.csv' 263 | condor_newick = f'{simulation_dir}/condor_reads/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}_tree.newick' 264 | Tsol, df_corrected, nxtree_sol = get_condor_tree_dataframe(condor_fname, condor_newick) 265 | 266 | elif method == 'sphyr': 267 | 268 | sphyr_fname = f'{simulation_dir}/sphyr/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}.out' 269 | Tsol, df_corrected, nxtree_sol = get_sphyr_tree_dataframe(sphyr_fname) 270 | 271 | elif method == 'sifit': 272 | 273 | sifit_fname = f'{simulation_dir}/sifit/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}_character_matrix.tsv' 274 | sifit_newick = f'{simulation_dir}/sifit/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}_mlTree.newick' 275 | 276 | Tsol, df_corrected = get_sifit_tree_dataframe(sifit_fname, sifit_newick) 277 | 278 | elif method == 'scite': 279 | 280 | # scite_newick = f'{simulation_dir}/scite/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}_ml0.newick' 281 | scite_gv_fname = f'{simulation_dir}/scite/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}_ml0.gv' 282 | Tsol, df_corrected, nxtree_sol = get_scite_tree_dataframe(n, m, scite_gv_fname) 283 | 284 | elif method == 'scarlet': 285 | 286 | scarlet_fname = f'{simulation_dir}/scarlet/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}.B' 287 | scarlet_edgelist_fname = f'{simulation_dir}/scarlet/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}.edgelist' 288 | Tsol, df_corrected, nxtree_sol = get_scarlet_tree_dataframe(scarlet_fname, scarlet_edgelist_fname) 289 | 290 | # evaluate 291 | gt_fname = f'{simulation_dir}/ground_truth/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}_character_matrix_without_noise.csv' 292 | gt_newick = f'{simulation_dir}/ground_truth/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}_tree.newick' 293 | gt_multi = f'{simulation_dir}/ground_truth/n{n}_m{m}_p{p}_k{k}_d{d}_s{s}_multi_state_character_matrix.csv' 294 | df_character_matrix = pd.read_csv(gt_fname, index_col = 0) 295 | df_character_matrix = df_character_matrix[df_character_matrix.columns[:-1]] 296 | Tground = ete3.Tree(gt_newick, format=1) 297 | df_Bcell = pd.read_csv(gt_multi, index_col = 0) 298 | _, nxtree_gt = solveConstrainedDollo.generate_perfect_phylogeny(solveConstrainedDollo.expand_multi_state_to_binary(df_Bcell[df_Bcell.columns[:-1]])) 299 | 300 | # RF metric 301 | (rf, rf_max, names, edges_t1, edges_t2, discarded_edges_t1, discarded_edges_t2) = Tground.robinson_foulds(Tsol, unrooted_trees=True) 302 | # mutation error 303 | nerror = np.sum(abs(df_corrected - df_character_matrix).values) 304 | 305 | # recall for ancestry and incomparibility 306 | if method != 'sifit': 307 | sol_descendant_dictionary = {} 308 | for mutation_idx in range(m): 309 | sol_descendant_dictionary[f'c{mutation_idx}_1'] = get_descendant_mutations(nxtree_sol, f'c{mutation_idx}_1') 310 | 311 | sol_clustered_dictionary = {} 312 | for mutation_idx in range(m): 313 | sol_clustered_dictionary[f'c{mutation_idx}_1'] = get_clustered_mutations(nxtree_sol, f'c{mutation_idx}_1') 314 | 315 | gt_descendant_dictionary = {} 316 | for mutation_idx in range(m): 317 | gt_descendant_dictionary[f'c{mutation_idx}_1'] = get_descendant_mutations(nxtree_gt, f'c{mutation_idx}_1') 318 | 319 | gt_clustered_dictionary = {} 320 | for mutation_idx in range(m): 321 | gt_clustered_dictionary[f'c{mutation_idx}_1'] = get_clustered_mutations(nxtree_gt, f'c{mutation_idx}_1') 322 | 323 | # confusion_mat = np.zeros((3,3)) 324 | # for mutation_idx1, mutation_idx2 in itertools.combinations(range(m), 2): 325 | # mutation1 = f'c{mutation_idx1}_1' 326 | # mutation2 = f'c{mutation_idx2}_1' 327 | 328 | # if mutation2 in sol_descendant_dictionary[mutation1] and mutation1 in sol_descendant_dictionary[mutation2]: 329 | # print('problem sol') 330 | # print(mutation1, mutation2) 331 | # raise ValueError 332 | # break 333 | 334 | # x_idx = 2 335 | # if mutation2 in sol_descendant_dictionary[mutation1]: 336 | # x_idx = 0 337 | # if mutation1 in sol_descendant_dictionary[mutation2]: 338 | # x_idx = 1 339 | 340 | # if mutation2 in gt_descendant_dictionary[mutation1] and mutation1 in gt_descendant_dictionary[mutation2]: 341 | # print('problem gt') 342 | # print(mutation1, mutation2) 343 | # raise ValueError 344 | # break 345 | 346 | # y_idx = 2 347 | # if mutation2 in gt_descendant_dictionary[mutation1]: 348 | # y_idx = 0 349 | # if mutation1 in gt_descendant_dictionary[mutation2]: 350 | # y_idx = 1 351 | 352 | # confusion_mat[x_idx, y_idx] += 1 353 | 354 | confusion_mat = np.zeros((4,4)) 355 | for mutation_idx1, mutation_idx2 in itertools.combinations(range(m), 2): 356 | mutation1 = f'c{mutation_idx1}_1' 357 | mutation2 = f'c{mutation_idx2}_1' 358 | 359 | if mutation2 in sol_descendant_dictionary[mutation1] and mutation1 in sol_descendant_dictionary[mutation2]: 360 | print('problem sol') 361 | print(mutation1, mutation2) 362 | break 363 | 364 | x_idx = 3 365 | if mutation1 in sol_clustered_dictionary[mutation2] or mutation2 in sol_clustered_dictionary[mutation1]: 366 | x_idx = 2 367 | else: 368 | if mutation2 in sol_descendant_dictionary[mutation1]: 369 | x_idx = 0 370 | if mutation1 in sol_descendant_dictionary[mutation2]: 371 | x_idx = 1 372 | 373 | if mutation2 in gt_descendant_dictionary[mutation1] and mutation1 in gt_descendant_dictionary[mutation2]: 374 | print('problem gt') 375 | print(mutation1, mutation2) 376 | break 377 | 378 | y_idx = 3 379 | if mutation1 in gt_clustered_dictionary[mutation2] or mutation2 in gt_clustered_dictionary[mutation1]: 380 | y_idx = 2 381 | else: 382 | if mutation2 in gt_descendant_dictionary[mutation1]: 383 | y_idx = 0 384 | if mutation1 in gt_descendant_dictionary[mutation2]: 385 | y_idx = 1 386 | 387 | confusion_mat[x_idx, y_idx] += 1 388 | 389 | ancestry_recall = (confusion_mat[0,0] + confusion_mat[1,1]) / np.sum(confusion_mat[:,:2]) 390 | # incomparability_recall = confusion_mat[2,2] / np.sum(confusion_mat[:,2]) 391 | incomparability_recall = np.nan 392 | accuracy = np.trace(confusion_mat)/np.sum(confusion_mat) 393 | else: 394 | ancestry_recall = np.nan 395 | incomparability_recall = np.nan 396 | accuracy = np.nan 397 | 398 | df_result = pd.DataFrame([[n, m, p, k, d, s, method, rf, rf_max, nerror, ancestry_recall, incomparability_recall, accuracy]], 399 | columns = ['ncells', 'ncharacters', 'nclusters', 'k', 'dropout', 'seed', 'method', 400 | 'RF', 'RF_max', 'nerror', 'ancestry_recall', 'incomparibility_recall', 'relation_accuracy']) 401 | 402 | df_result.to_csv(f'{output_fname}') 403 | 404 | def str2bool(v): 405 | if isinstance(v, bool): 406 | return v 407 | if v.lower() in ('yes', 'true', 't', 'y', '1'): 408 | return True 409 | elif v.lower() in ('no', 'false', 'f', 'n', '0'): 410 | return False 411 | else: 412 | raise argparse.ArgumentTypeError('Boolean value expected.') 413 | 414 | if __name__ == "__main__": 415 | parser = argparse.ArgumentParser() 416 | parser.add_argument('-n', type=int, help='number of samples [5]', default = 5) 417 | parser.add_argument('-m', type=int, help='number of SNV mutations [5]', default = 5) 418 | parser.add_argument('-p', type=int, help='number of clusters [1]', default = 1) 419 | parser.add_argument('-k', type=int, help='number of SNV losses per character [0]', default = 0) 420 | parser.add_argument('-o', type=str, help='output filename', default='sample.csv') 421 | parser.add_argument('-s', type=int, help='seed [0]', default = 0) 422 | parser.add_argument('-d', type=float, help='missing data rate [0.0]', default=0) 423 | parser.add_argument('--dir', type=str, help='simulation directory', required=True) 424 | parser.add_argument('--method', type=str, help='method for comparison with ground truth', required=True) 425 | parser.add_argument('-v', action='store_true', default=False) 426 | args = parser.parse_args(None if sys.argv[1:] else ['-h']) 427 | 428 | main(args) -------------------------------------------------------------------------------- /src/simulation_reads.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Wed Jun 29 2022 5 | 6 | @author: Palash Sashittal 7 | """ 8 | 9 | import pandas as pd 10 | import sys 11 | import argparse 12 | import itertools 13 | import math 14 | import numpy as np 15 | from scipy.stats import betabinom 16 | 17 | import networkx as nx 18 | 19 | def writeDOT(T, dot_file): 20 | with open(dot_file, 'w') as output: 21 | 22 | output.write(f'digraph N {{\n') 23 | output.write(f"\toverlap=\"false\"\n") 24 | output.write(f"\trankdir=\"TB\"\n") 25 | 26 | idx_dict = {} 27 | idx = 0 28 | for node in T.nodes: 29 | idx_dict[node] = idx 30 | output.write(f'\t{idx} [label=\"{node}\", style=\"bold\"];\n') 31 | idx += 1 32 | 33 | for edge in T.edges: 34 | output.write(f"\t{idx_dict[edge[0]]} -> {idx_dict[edge[1]]} [style=\"bold\"];\n") 35 | 36 | output.write(f'}}') 37 | 38 | def tree_to_newick(T, root=None): 39 | if root is None: 40 | roots = list(filter(lambda p: p[1] == 0, T.in_degree())) 41 | assert 1 == len(roots) 42 | root = roots[0][0] 43 | subgs = [] 44 | while len(T[root]) == 1: 45 | root = list(T[root])[0] 46 | for child in T[root]: 47 | while len(T[child]) == 1: 48 | child = list(T[child])[0] 49 | if len(T[child]) > 0: 50 | child_newick = tree_to_newick(T, root=child) 51 | if child_newick != '()': 52 | subgs.append(child_newick) 53 | else: 54 | if child.startswith('s'): 55 | subgs.append(child) 56 | # return "(" + ','.join(map(str, subgs)) + ")" 57 | if len(subgs) == 1: 58 | return str(subgs[0]) 59 | else: 60 | return "(" + ','.join(map(str, subgs)) + ")" 61 | 62 | def main(args): 63 | 64 | np.random.seed(args.s) 65 | 66 | T = nx.DiGraph() # mutation tree 67 | Tc = nx.DiGraph() # copy number tree 68 | 69 | # add root nodes 70 | T.add_node('root') 71 | Tc.add_node(0) 72 | 73 | # build tree 74 | ncharacters = args.m 75 | nmutations = ncharacters 76 | nclusters = args.p 77 | max_losses = args.k 78 | max_cn = args.maxcn 79 | mutation_rate = args.l 80 | nnodes = ncharacters + nclusters 81 | 82 | character_list = [f'c{character_index}' for character_index in range(ncharacters)] 83 | cluster_list = [f'd{cluster_index}' for cluster_index in range(1, nclusters)] 84 | event_order = np.random.permutation(character_list + cluster_list) 85 | loss_counter = np.zeros((ncharacters, 1)) 86 | loss_dictionary = {f'd{cluster_index}': [] for cluster_index in range(1, nclusters)} 87 | 88 | B = np.zeros((ncharacters + nclusters, ncharacters + 1), dtype=int) 89 | R = np.zeros((nclusters, ncharacters), dtype=int) 90 | R[0, :] = np.random.randint(max_cn - max_losses - 1, size = ncharacters) + max_losses + 1 91 | 92 | for node_index, event in enumerate(event_order): 93 | nprev_mutations = sum([1 for x in event_order[:node_index] if x.startswith('c')]) 94 | node_index += 1 95 | 96 | if event.startswith('d') and nprev_mutations > 0: 97 | while parent_node.startswith('d') or parent_node == 'root': 98 | parent_node_index = np.random.randint(node_index) 99 | parent_node = list(T.nodes)[parent_node_index] 100 | else: 101 | parent_node_index = np.random.randint(node_index) 102 | parent_node = list(T.nodes)[parent_node_index] 103 | 104 | T.add_edge(parent_node, event) 105 | B[node_index, :] = B[parent_node_index, :] 106 | if event.startswith('d'): 107 | cluster_id = int(event.lstrip('d')) 108 | B[node_index, -1] = cluster_id 109 | parent_cluster_id = B[parent_node_index, -1] 110 | Tc.add_edge(parent_cluster_id, cluster_id) 111 | R[cluster_id, :] = R[parent_cluster_id, :] 112 | 113 | for mutation in range(ncharacters): 114 | if B[parent_node_index, mutation] == 1 and loss_counter[mutation] < max_losses: 115 | if np.random.rand() < mutation_rate: 116 | B[node_index, mutation] = loss_counter[mutation] + 2 117 | loss_counter[mutation] += 1 118 | loss_dictionary[event].append(mutation) 119 | R[cluster_id, mutation] -= 1 120 | 121 | elif event.startswith('c'): 122 | mutation = int(event.lstrip('c')) 123 | B[node_index, mutation] = 1 124 | 125 | if args.v: 126 | print(parent_node_index, parent_node, node_index, event) 127 | 128 | # randomize the copy number states for mutations that have never been lost 129 | for mutation in range(nmutations): 130 | if loss_counter[mutation] == 0: 131 | for cluster_id in range(nclusters): 132 | R[cluster_id, mutation] = np.random.randint(max_cn - 1) + 1 133 | 134 | # check that all copy number states are non-zer positive 135 | assert(len(np.where(R == 0)[0]) == 0) 136 | 137 | # check all SNV losses are supported by CNVs 138 | for cn_edge in Tc.edges: 139 | for mutation in loss_dictionary[f'd{cn_edge[1]}']: 140 | assert(R[cn_edge[0], mutation] > R[cn_edge[1], mutation]) 141 | 142 | if args.v: 143 | print('-'*50) 144 | print('loss counter') 145 | print('-'*50) 146 | print(loss_counter) 147 | print('-'*50) 148 | print('loss dictionary') 149 | print('-'*50) 150 | print(loss_dictionary) 151 | print('-'*50) 152 | print('copy number states') 153 | print('-'*50) 154 | print(R) 155 | 156 | # assign cells and generate character-state matrix 157 | leaf_indices = [] 158 | for idx, node in enumerate(T.nodes): 159 | if len(T[node]) == 0: 160 | leaf_indices.append(idx) 161 | nleaves = len(leaf_indices) 162 | 163 | ncells = args.n 164 | assert(ncells > nclusters) 165 | cell_assignment = np.random.randint(ncharacters, size=ncells-nleaves) 166 | complete_cell_assignment = list(cell_assignment) + leaf_indices 167 | Bcell = B[complete_cell_assignment, :] 168 | 169 | # observed matrix 170 | A = B.copy() 171 | for mutation in range(ncharacters): 172 | A[A[:,mutation] > 1, mutation] = 0 173 | Acell = A[complete_cell_assignment, :] 174 | 175 | # cell tree 176 | celltree = T.copy() 177 | for cell_id, assigned_node_index in enumerate(complete_cell_assignment): 178 | celltree.add_edge(list(T.nodes)[assigned_node_index], f's{cell_id}') 179 | 180 | 181 | # generate read counts 182 | mean_coverage = args.cov 183 | fp_rate = args.a 184 | fn_rate = args.b 185 | ado_precision = args.ado 186 | 187 | Rtotal = np.zeros((ncells, nmutations), dtype=int) 188 | Vcount = np.zeros((ncells, nmutations), dtype=int) 189 | for cell in range(ncells): 190 | for mutation in range(nmutations): 191 | cluster_id = Acell[cell, -1] 192 | nvariant = Acell[cell, mutation] 193 | ntotal = R[cluster_id, mutation] 194 | 195 | latent_vaf = nvariant / ntotal 196 | 197 | nreads = np.random.poisson(mean_coverage) 198 | Rtotal[cell, mutation] = int(nreads) 199 | 200 | post_error_vaf = fp_rate + (1 - fp_rate - fn_rate) * latent_vaf 201 | ado_alpha = post_error_vaf * ado_precision 202 | ado_beta = ado_precision * (1 - post_error_vaf) 203 | nvariant_reads = betabinom.rvs(nreads, ado_alpha, ado_beta) 204 | 205 | Vcount[cell, mutation] = int(nvariant_reads) 206 | 207 | # generate the binarized mutation matrix 208 | vaf_threshold = args.vafthreshold 209 | variant_read_threshold = args.readthreshold 210 | VAF_mat = Vcount / Rtotal 211 | mutation_mat = ((VAF_mat >= vaf_threshold) & (Vcount >= variant_read_threshold)).astype(int) 212 | mutation_mat = np.hstack((mutation_mat, Acell[:,-1][:,np.newaxis])) 213 | 214 | # introduce missing entries 215 | Acell_missing = Acell.copy() 216 | Rtotal_missing = Rtotal.copy() 217 | Vcount_missing = Vcount.copy() 218 | Acell_noisy = mutation_mat.copy() 219 | 220 | missing_rate = args.d 221 | n_entries = ncells * ncharacters 222 | nmissing = math.floor(missing_rate * n_entries) 223 | selected_cell_indices = np.random.randint(ncells, size=nmissing) 224 | selected_character_indices = np.random.randint(ncharacters, size=nmissing) 225 | Acell_missing[selected_cell_indices, selected_character_indices] = -1 226 | Rtotal_missing[selected_cell_indices, selected_character_indices] = 0 227 | Vcount_missing[selected_cell_indices, selected_character_indices] = 0 228 | Acell_noisy[selected_cell_indices, selected_character_indices] = -1 229 | 230 | # write ground truth files 231 | prefix = args.o 232 | with open(f'{prefix}_tree_edgelist.csv', 'w') as out: 233 | for edge in celltree.edges: 234 | out.write(f'{edge[0]},{edge[1]}\n') 235 | 236 | with open(f'{prefix}_tree.newick', 'w') as out: 237 | out.write(tree_to_newick(celltree) + ';') 238 | 239 | writeDOT(celltree, f'{prefix}_tree.dot') 240 | 241 | df_B = pd.DataFrame(B, index=list(T.nodes), 242 | columns = [f'c{idx}' for idx in range(ncharacters)] + ['cluster_id'], dtype=int) 243 | df_Bcell = pd.DataFrame(Bcell, index=[f's{idx}' for idx in range(ncells)], 244 | columns = [f'c{idx}' for idx in range(ncharacters)] + ['cluster_id'], dtype=int) 245 | df_Acell = pd.DataFrame(Acell, index=[f's{idx}' for idx in range(ncells)], 246 | columns = [f'c{idx}' for idx in range(ncharacters)] + ['cluster_id'], dtype=int) 247 | df_Acell_noisy = pd.DataFrame(Acell_noisy, index=[f's{idx}' for idx in range(ncells)], 248 | columns = [f'c{idx}' for idx in range(ncharacters)] + ['cluster_id'], dtype=int) 249 | 250 | df_Rtotal = pd.DataFrame(Rtotal, index=[f's{idx}' for idx in range(ncells)], 251 | columns = [f'c{idx}' for idx in range(ncharacters)], dtype=int) 252 | df_Vcount = pd.DataFrame(Vcount, index=[f's{idx}' for idx in range(ncells)], 253 | columns = [f'c{idx}' for idx in range(ncharacters)], dtype=int) 254 | df_Rtotal_missing = pd.DataFrame(Rtotal_missing, index=[f's{idx}' for idx in range(ncells)], 255 | columns = [f'c{idx}' for idx in range(ncharacters)], dtype=int) 256 | df_Vcount_missing = pd.DataFrame(Vcount_missing, index=[f's{idx}' for idx in range(ncells)], 257 | columns = [f'c{idx}' for idx in range(ncharacters)], dtype=int) 258 | 259 | 260 | df_B.to_csv(f'{prefix}_multi_state_tree_node_character_matrix.csv') 261 | df_Bcell.to_csv(f'{prefix}_multi_state_character_matrix.csv') 262 | df_Acell.to_csv(f'{prefix}_character_matrix_without_noise.csv') 263 | df_Acell_noisy.to_csv(f'{prefix}_character_matrix.csv') 264 | 265 | df_Rtotal.to_csv(f'{prefix}_read_count_without_missing.csv') 266 | df_Vcount.to_csv(f'{prefix}_variant_count_without_missing.csv') 267 | df_Rtotal_missing.to_csv(f'{prefix}_read_count.csv') 268 | df_Vcount_missing.to_csv(f'{prefix}_variant_count.csv') 269 | 270 | # scarlet data format 271 | scarlet_readcount_data = [] 272 | scarlet_readcount_data_columns = ['cell_id', 'c'] 273 | for mutation in df_Rtotal_missing.columns: 274 | scarlet_readcount_data_columns.append(f'{mutation}_v') 275 | scarlet_readcount_data_columns.append(f'{mutation}_t') 276 | 277 | for cell in df_Rtotal_missing.index: 278 | cell_readcount_data = [cell, df_Acell.loc[cell]['cluster_id']] 279 | for mutation in df_Rtotal_missing.columns: 280 | cell_readcount_data.append(df_Vcount_missing.loc[cell][mutation]) 281 | cell_readcount_data.append(df_Rtotal_missing.loc[cell][mutation]) 282 | scarlet_readcount_data.append(cell_readcount_data) 283 | 284 | df_scarlet_readcounts = pd.DataFrame(scarlet_readcount_data, columns = scarlet_readcount_data_columns) 285 | df_scarlet_readcounts = df_scarlet_readcounts.set_index('cell_id') 286 | df_scarlet_readcounts.to_csv(f'{prefix}_readcounts_scarlet.csv') 287 | 288 | with open(f'{prefix}_copynumbertree_scarlet.txt', 'w') as out: 289 | for edge in Tc.edges: 290 | edge_data = [edge[0], edge[1]] 291 | for mutation in range(nmutations): 292 | if R[edge[0],mutation] > R[edge[1],mutation]: 293 | edge_data.append(f'c{mutation}') 294 | out.write(','.join(map(str,edge_data)) + '\n') 295 | 296 | # sphyr data format 297 | with open(f'{prefix}_sphyr.txt', 'w') as out: 298 | out.write(f'{len(df_Acell_noisy)}\n') 299 | out.write(f'{len(df_Acell_noisy.columns) - 1}\n') 300 | 301 | for idx, row in df_Acell_noisy.iterrows(): 302 | out.write(f"{' '.join(map(str,row.values[:-1]))}\n") 303 | 304 | 305 | # SCITE data format 306 | Acell_noisy_scite = Acell_noisy[:,:-1].T.copy().astype(int) 307 | Acell_noisy_scite[Acell_noisy_scite == -1] = 3 308 | np.savetxt(f"{prefix}_scite.txt", Acell_noisy_scite, delimiter=" ", fmt='%d') 309 | 310 | # SIFIT data format 311 | np.savetxt(f"{prefix}_sifit.txt", np.hstack((np.arange(Acell_noisy_scite.shape[0])[:,None], Acell_noisy_scite)), delimiter=" ", fmt='%d') 312 | 313 | # PhiSCS data format 314 | Acell_noisy_scite_str = Acell_noisy_scite.astype(str) 315 | Acell_noisy_scite_str = np.char.replace(Acell_noisy_scite_str, '3', '?').T 316 | snv_mat_phiscs = np.vstack((np.hstack((np.array([['cell_idx/mut_idx']]), np.array(df_Acell_noisy.columns)[np.newaxis,:-1])), 317 | np.hstack((np.array(df_Acell_noisy.index)[:,np.newaxis], Acell_noisy_scite_str)))) 318 | np.savetxt(f"{prefix}_phiscs.txt", snv_mat_phiscs, delimiter="\t", fmt='%s') 319 | 320 | if __name__ == "__main__": 321 | parser = argparse.ArgumentParser() 322 | parser.add_argument('-n', type=int, help='number of samples [5]', default = 5) 323 | parser.add_argument('-m', type=int, help='number of SNV mutations [5]', default = 5) 324 | parser.add_argument('-p', type=int, help='number of clusters [1]', default = 1) 325 | parser.add_argument('-k', type=int, help='number of SNV losses per character [0]', default = 0) 326 | parser.add_argument('-o', type=str, help='output prefix', default='sample') 327 | parser.add_argument('-s', type=int, help='seed [0]', default = 0) 328 | parser.add_argument('-d', type=float, help='missing data rate [0.0]', default=0) 329 | parser.add_argument('--cov', type=int, help='coverage of read count [50]', default = 50) 330 | parser.add_argument('-a', type=float, help='false positive error rate', default = 0) 331 | parser.add_argument('-b', type=float, help='false negative error rate', default = 0) 332 | parser.add_argument('--ado', type=float, help='precision parameter for ado [15]', default = 15) 333 | parser.add_argument('--maxcn', type=float, help='maximum allowed copy number [8]', default = 8) 334 | parser.add_argument('--readthreshold', type=int, help='variant read count threshold for generating the mutation matrix [5]', default=5) 335 | parser.add_argument('--vafthreshold', type=float, help='VAF threshold for generating the mutation matrix [0.1]', default = 0.1) 336 | parser.add_argument('-l', type=float, help='rate of mutation loss [0.8]', default = 0.8) 337 | parser.add_argument('-v', action='store_true', default=False) 338 | args = parser.parse_args(None if sys.argv[1:] else ['-h']) 339 | 340 | main(args) -------------------------------------------------------------------------------- /src/solveConstrainedDollo.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Wed May 5 2022 5 | 6 | @author: Palash Sashittal 7 | """ 8 | 9 | import gurobipy as gp 10 | import numpy as np 11 | import pandas as pd 12 | import networkx as nx 13 | import itertools 14 | from scipy.stats import betabinom 15 | 16 | # minimum correction tree parsimonious clone reconciliation problem 17 | class solveConstrainedDollo(): 18 | 19 | def __init__(self, df_character_matrix, df_total_readcounts = None, df_variant_readcounts = None, snp_list = [], 20 | k = None, fp = None, fn = None, ado_precision = 15, threads = 1, timelimit = None, verbose = True): 21 | 22 | # input character matrix and clustering 23 | self.df_character_matrix = df_character_matrix 24 | self.clustering = self.df_character_matrix['cluster_id'].values 25 | self.A = df_character_matrix.values[:, :-1] 26 | self.snp_list = snp_list 27 | self.mutation_list = list(df_character_matrix.columns[:-1]) 28 | 29 | # input data parameters 30 | self.ncells = len(self.df_character_matrix) 31 | self.nclusters = len(self.df_character_matrix['cluster_id'].unique()) 32 | self.nmutations = len(self.df_character_matrix.columns) - 1 33 | self.k = k 34 | self.fp = fp 35 | self.fn = fn 36 | 37 | # read count matrices 38 | self.df_total_readcounts = df_total_readcounts 39 | if df_total_readcounts is not None: 40 | self.cell_list = list(df_total_readcounts.index) 41 | self.mutation_list = list(df_total_readcounts.columns) 42 | 43 | bb_alpha = fp * ado_precision 44 | bb_beta = (1 - fp) * ado_precision 45 | 46 | coeff_mat = np.zeros((self.ncells, self.nmutations)) 47 | for cell_idx, cell in enumerate(self.cell_list): 48 | for mut_idx, mutation in enumerate(self.mutation_list): 49 | total_reads = df_total_readcounts.loc[cell][mutation] 50 | variant_reads = df_variant_readcounts.loc[cell][mutation] 51 | if total_reads > 0: 52 | #coeff = -np.log(total_reads) - betabinom.logpmf(variant_reads, total_reads, bb_alpha, bb_beta) 53 | coeff = betabinom.logpmf(variant_reads, total_reads, 1, 1) - betabinom.logpmf(variant_reads, total_reads, bb_alpha, bb_beta) 54 | coeff_mat[cell_idx, mut_idx] = coeff 55 | self.coeff_mat = coeff_mat 56 | 57 | # for cell in range(self.ncells): 58 | # for mutation in range(self.nmutations): 59 | # total_reads = df_total_readcounts.values[cell, mutation] 60 | # variant_reads = df_variant_readcounts.values[cell, mutation] 61 | # if total_reads > 0: 62 | # coeff = -np.log(total_reads) - betabinom.logpmf(variant_reads, total_reads, bb_alpha, bb_beta) 63 | # coeff_mat[cell, mutation] = coeff 64 | # self.coeff_mat = coeff_mat 65 | else: 66 | self.coeff_mat = None 67 | 68 | # gurobi parameters 69 | self.threads = threads 70 | self.worklimit = timelimit 71 | self.verbose = verbose 72 | 73 | 74 | self.fpweight = np.log(1 - fp) - np.log(fp) 75 | self.fnweight = np.log(fn) - np.log(1 - fn) 76 | 77 | # solution 78 | # self.B = np.zeros((self.ncells, self.nmutations)) 79 | self.solB = None 80 | self.solT_cell = None 81 | self.solT_mut = None 82 | 83 | def solveSetInclusion(self): 84 | 85 | ncells = self.ncells 86 | nmutations = self.nmutations 87 | nclusters = self.nclusters 88 | 89 | print(f'n = {ncells}, m = {nmutations}, p = {nclusters}') 90 | 91 | clustering = self.clustering 92 | k = self.k 93 | 94 | model = gp.Model('solveConstrainedDollo') 95 | 96 | # character matrix variables 97 | b = model.addVars(ncells, nmutations, vtype=gp.GRB.BINARY, name='b') 98 | c = model.addVars(nclusters, nmutations, k, vtype=gp.GRB.BINARY, name='c') 99 | 100 | # 1+ indicator 101 | x = model.addVars(ncells, nmutations, vtype=gp.GRB.CONTINUOUS, lb = 0, ub = 1, name='x') 102 | 103 | # row clustering consistency variables 104 | g = model.addVars(nclusters, nmutations, vtype=gp.GRB.CONTINUOUS, lb=0, ub=1, name='g') 105 | g0 = model.addVars(nclusters, nmutations, vtype=gp.GRB.CONTINUOUS, lb=0, ub=1, name='g0') 106 | g1 = model.addVars(nclusters, nmutations, vtype=gp.GRB.CONTINUOUS, lb=0, ub=1, name='g1') 107 | 108 | # column compatibility variables 109 | y0 = model.addVars(nmutations, nmutations, k, k, vtype=gp.GRB.CONTINUOUS, lb=0, ub=1, name='y0') 110 | y1 = model.addVars(nmutations, nmutations, k, vtype=gp.GRB.CONTINUOUS, lb=0, ub=1, name='y1') 111 | y2 = model.addVars(nmutations, nmutations, k, vtype=gp.GRB.CONTINUOUS, lb=0, ub=1, name='y2') 112 | y3 = model.addVars(nmutations, nmutations, vtype=gp.GRB.CONTINUOUS, lb=0, ub=1, name='y3') 113 | z0 = model.addVars(nmutations, nmutations, k, k, vtype=gp.GRB.CONTINUOUS, lb=0, ub=1, name='z0') 114 | z1 = model.addVars(nmutations, nmutations, k, vtype=gp.GRB.CONTINUOUS, lb=0, ub=1, name='z1') 115 | z2 = model.addVars(nmutations, nmutations, vtype=gp.GRB.CONTINUOUS, lb=0, ub=1, name='z2') 116 | 117 | # encode one-hot-like constraint on b and c 118 | for i in range(ncells): 119 | for j in range(nmutations): 120 | csum = gp.LinExpr() 121 | for s in range(k): 122 | cluster = clustering[i] 123 | csum += c[cluster,j,s] 124 | 125 | if self.mutation_list[j] not in self.snp_list: 126 | model.addConstr(csum + b[i,j] <= 1) 127 | else: 128 | model.addConstr(csum + b[i,j] == 1) 129 | model.addConstr(x[i,j] == b[i,j] + csum) 130 | 131 | # symmetry breaking 132 | for s in range(1,k): 133 | csum1 = gp.LinExpr() 134 | csum2 = gp.LinExpr() 135 | for l in range(nclusters): 136 | for j in range(nmutations): 137 | csum1 += c[l,j,s] 138 | csum2 += c[l,j,s-1] 139 | 140 | model.addConstr(csum2 >= csum1) 141 | 142 | # encode consistency constraints 143 | ## g0 constraints 144 | for l in range(nclusters): 145 | for j in range(nmutations): 146 | xsum = gp.LinExpr() 147 | cluster_size = 0 148 | for i in np.where(clustering == l)[0]: 149 | xsum += x[i,j] 150 | model.addConstr(g0[l,j] >= 1 - x[i,j]) 151 | cluster_size += 1 152 | model.addConstr(g0[l,j] <= cluster_size - xsum) 153 | 154 | ## g1 constraints 155 | for l in range(nclusters): 156 | for j in range(nmutations): 157 | bsum = gp.LinExpr() 158 | for i in np.where(clustering == l)[0]: 159 | bsum += b[i,j] 160 | model.addConstr(g1[l,j] >= b[i,j]) 161 | model.addConstr(g1[l,j] <= bsum) 162 | 163 | ## g constraints 164 | for l in range(nclusters): 165 | for j in range(nmutations): 166 | model.addConstr(g[l,j] <= g0[l,j]) 167 | model.addConstr(g[l,j] <= g1[l,j]) 168 | model.addConstr(g[l,j] >= g0[l,j] + g1[l,j] - 1) 169 | 170 | for j in range(nmutations): 171 | gsum = gp.LinExpr() 172 | for l in range(nclusters): 173 | gsum += g[l,j] 174 | model.addConstr(gsum <= 1) 175 | 176 | 177 | # encode set inclusion constraints 178 | ## y constraints (containment) 179 | for j1 in range(nmutations): 180 | for j2 in range(nmutations): 181 | if j1 == j2: 182 | continue 183 | for s1 in range(k): 184 | for s2 in range(k): 185 | for l in range(nclusters): 186 | model.addConstr(y0[j1,j2,s1,s2] <= 1 - c[l,j2,s2] + c[l,j1,s1]) 187 | 188 | for j1 in range(nmutations): 189 | for j2 in range(nmutations): 190 | if j1 == j2: 191 | continue 192 | for s2 in range(k): 193 | for l in range(nclusters): 194 | model.addConstr(y1[j1,j2,s2] <= 1 - c[l,j2,s2] + (1 - g0[l,j1])) 195 | 196 | for j1 in range(nmutations): 197 | for j2 in range(nmutations): 198 | if j1 == j2: 199 | continue 200 | for s1 in range(k): 201 | for l in range(nclusters): 202 | csum = gp.LinExpr() 203 | for s in range(k): 204 | csum += c[l,j2,s] 205 | model.addConstr(y2[j1,j2,s1] <= 1 - (g1[l,j2] + csum) + c[l,j1,s1]) 206 | # model.addConstr(y2[j1,j2,s1] <= 1 - (1 - g0[l,j2]) + c[l,j1,s1]) 207 | 208 | 209 | for j1 in range(nmutations): 210 | for j2 in range(nmutations): 211 | if j1 == j2: 212 | continue 213 | for i in range(ncells): 214 | model.addConstr(y3[j1,j2] <= 1 - x[i,j2] + x[i,j1]) 215 | 216 | ## z constraints (disjointness) 217 | for j1 in range(nmutations): 218 | for j2 in range(nmutations): 219 | if j1 >= j2: 220 | continue 221 | for s1 in range(k): 222 | for s2 in range(k): 223 | for l in range(nclusters): 224 | model.addConstr(z0[j1,j2,s1,s2] <= 2 - c[l,j1,s1] - c[l,j2,s2]) 225 | # model.addConstr(z1[j1,j2,s1,s2] <= 2 - c[l,j1,s1] - c[l,j2,s2] - (1 - g0[l,j1])) 226 | 227 | for i in range(ncells): 228 | l = clustering[i] 229 | model.addConstr(z2[j1,j2] <= 2 - x[i,j2] - x[i,j1]) 230 | 231 | for j1 in range(nmutations): 232 | for j2 in range(nmutations): 233 | if j1 == j2: 234 | continue 235 | for s2 in range(k): 236 | for l in range(nclusters): 237 | csum = gp.LinExpr() 238 | for s in range(k): 239 | csum += c[l,j1,s] 240 | model.addConstr(z1[j1,j2,s2] <= 2 - c[l,j2,s2] - (g1[l,j1] + csum)) 241 | # model.addConstr(z1[j1,j2,s2] <= 2 - c[l,j2,s2] - (1 - g0[l,j1])) 242 | 243 | ## avoid conflict 244 | for j1 in range(nmutations): 245 | for j2 in range(nmutations): 246 | if j1 >= j2: 247 | continue 248 | for s1 in range(k): 249 | for s2 in range(k): 250 | model.addConstr(y0[j1,j2,s1,s2] + y0[j2,j1,s2,s1] + z0[j1,j2,s1,s2] >= 1) 251 | # model.addConstr(y1[j1,j2,s1,s2] + y1[j2,j1,s1,s2] + z1[j1,j2,s1,s2] >= 1) 252 | # model.addConstr(y2[j1,j2,s1,s2] + y2[j2,j1,s1,s2] + z1[j1,j2,s1,s2] >= 1) 253 | model.addConstr(y3[j1,j2] + y3[j2,j1] + z2[j1,j2] >= 1) 254 | 255 | 256 | for j1 in range(nmutations): 257 | for j2 in range(nmutations): 258 | if j1 == j2: 259 | continue 260 | for s2 in range(k): 261 | model.addConstr(y1[j1,j2,s2] + y2[j2,j1,s2] + z1[j1,j2,s2] >= 1) 262 | 263 | 264 | # set objective function 265 | obj_sum = gp.LinExpr() 266 | if self.coeff_mat is not None: 267 | for i in range(ncells): 268 | for j in range(nmutations): 269 | if self.df_total_readcounts.values[i,j] > 0: 270 | obj_sum += self.coeff_mat[i,j] * b[i,j] 271 | else: 272 | for i in range(ncells): 273 | for j in range(nmutations): 274 | if self.df_character_matrix.values[i,j] == 0: 275 | obj_sum += self.fnweight * b[i,j] 276 | elif self.df_character_matrix.values[i,j] == 1: 277 | obj_sum += self.fpweight * b[i,j] 278 | 279 | model.setObjective(obj_sum, gp.GRB.MAXIMIZE) 280 | 281 | model.setParam(gp.GRB.Param.Threads, self.threads) 282 | model.setParam(gp.GRB.Param.Method, 4) 283 | 284 | model.setParam(gp.GRB.Param.FeasibilityTol, 1e-6) 285 | model.setParam(gp.GRB.Param.IntFeasTol, 1e-6) 286 | model.setParam(gp.GRB.Param.OptimalityTol, 1e-6) 287 | 288 | # model.write('test_model_turing.lp') 289 | 290 | model.optimize() 291 | if model.status == gp.GRB.OPTIMAL: 292 | nzero_entries = np.sum(self.A == 0) 293 | none_entries = np.sum(self.A == 1) 294 | opt_obj_value = model.getObjective().getValue() 295 | print(f'{nzero_entries}, {none_entries}, {opt_obj_value}') 296 | #print(f'log likelihood: {opt_obj_value + nzero_entries * np.log(1 - self.fn) + none_entries * np.log(self.fp)}') 297 | 298 | # solb = np.zeros((ncells, nmutations)) 299 | 300 | # for i in range(ncells): 301 | # for j in range(nmutations): 302 | # solb[i,j] = np.abs(model.getAttr('x', b)[i,j]) 303 | #solb = np.rint(np.reshape(model.getAttr('x', b).values(), (ncells, nmutations))) 304 | solb = np.rint(np.reshape(np.array(list(model.getAttr('x', b).values())), (ncells, nmutations))) 305 | 306 | solc = model.getAttr('x', c) 307 | for l in range(nclusters): 308 | for j in range(nmutations): 309 | for s in range(k): 310 | if solc[l,j,s] > 0.5: 311 | for i in np.where(clustering == l)[0]: 312 | solb[i,j] = s + 2 313 | 314 | # df_solb = pd.DataFrame(solb, index = self.df_character_matrix.index, 315 | # columns = self.df_character_matrix.columns[:-1]) 316 | 317 | # df_solb.to_csv('new_test_condor_output.csv') 318 | # df_solb.to_csv('newest_test_condor_output.csv') 319 | 320 | df_solb = pd.DataFrame(solb, index = self.df_character_matrix.index, 321 | columns = self.df_character_matrix.columns[:-1], dtype=int) 322 | # print(model.getAttr('x', b)) 323 | # print('-'*50) 324 | # print(model.getAttr('x', g)) 325 | # print('-'*50) 326 | # print(solc) 327 | 328 | self.solB = df_solb 329 | df_solb_binary = solveConstrainedDollo.expand_multi_state_to_binary(df_solb) 330 | self.solT_mut, self.solT_cell = solveConstrainedDollo.generate_perfect_phylogeny(df_solb_binary) 331 | 332 | def writeSolution(self, fname): 333 | if self.solB is not None: 334 | self.solB.to_csv(fname) 335 | 336 | @staticmethod 337 | def expand_multi_state_to_binary(df_multistate): 338 | 339 | ncells = len(df_multistate) 340 | binarized_mat = None 341 | binary_col_dict = {} 342 | for column in df_multistate.columns: 343 | max_state = df_multistate[column].max() 344 | for s in range(1, max_state+1): 345 | state_col = np.zeros((ncells)) 346 | if s == 1: 347 | state_col[df_multistate[column] > 0] = 1 348 | else: 349 | state_col[df_multistate[column] == s] = 1 350 | 351 | binary_col_dict[f'{column}_{s}'] = state_col 352 | 353 | df_binary = pd.DataFrame(binary_col_dict, index = df_multistate.index, dtype=int) 354 | return df_binary 355 | 356 | @staticmethod 357 | def generate_perfect_phylogeny(df_binary): 358 | 359 | solT_mut = nx.DiGraph() 360 | solT_mut.add_node('root') 361 | 362 | solT_cell = nx.DiGraph() 363 | solT_cell.add_node('root') 364 | 365 | df_binary = df_binary[df_binary.sum().sort_values(ascending=False).index] 366 | 367 | for cell_id, row in df_binary.iterrows(): 368 | if cell_id == 'root': 369 | continue 370 | 371 | curr_node = 'root' 372 | for column in df_binary.columns[row.values == 1]: 373 | if column in solT_mut[curr_node]: 374 | curr_node = column 375 | else: 376 | if column in solT_mut.nodes: 377 | raise NameError(f'{column} is being repeated') 378 | solT_mut.add_edge(curr_node, column) 379 | solT_cell.add_edge(curr_node, column) 380 | curr_node = column 381 | 382 | solT_cell.add_edge(curr_node, cell_id) 383 | 384 | return solT_mut, solT_cell 385 | 386 | def writeDOT(self, dot_file, withcells=True): 387 | if withcells is True: 388 | writeTree = self.solT_cell 389 | else: 390 | writeTree = self.solT_mut 391 | 392 | with open(dot_file, 'w') as output: 393 | 394 | output.write(f'digraph N {{\n') 395 | output.write(f"\toverlap=\"false\"\n") 396 | output.write(f"\trankdir=\"TB\"\n") 397 | 398 | idx_dict = {} 399 | idx = 0 400 | if writeTree is not None: 401 | for node in writeTree.nodes: 402 | idx_dict[node] = idx 403 | output.write(f'\t{idx} [label=\"{node}\", style=\"bold\"];\n') 404 | idx += 1 405 | 406 | for edge in writeTree.edges: 407 | output.write(f"\t{idx_dict[edge[0]]} -> {idx_dict[edge[1]]} [style=\"bold\"];\n") 408 | 409 | output.write(f'}}') 410 | --------------------------------------------------------------------------------