diff --git a/CHANGELOG.md b/CHANGELOG.md index 3deeed6..edd5f63 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 --- --- +## 0.2.1 - 2023-10 +### Fixed + - Improve CorrNMF model formulation (added signature biases) + - Improve multimodal exposure plot + ## 0.2.0 - 2023-10 ### Added - Support fixing arbitrary many a priori known signatures during inference. diff --git a/data/COSMIC_v3.3.1_SBS_GRCh38.txt b/data/COSMIC_v3.3.1_SBS_GRCh38.txt new file mode 100644 index 0000000..488f27b --- /dev/null +++ b/data/COSMIC_v3.3.1_SBS_GRCh38.txt @@ -0,0 +1,97 @@ +Type SBS1 SBS2 SBS3 SBS4 SBS5 SBS6 SBS7a SBS7b SBS7c SBS7d SBS8 SBS9 SBS10a SBS10b SBS10c SBS10d SBS11 SBS12 SBS13 SBS14 SBS15 SBS16 SBS17a SBS17b SBS18 SBS19 SBS20 SBS21 SBS22 SBS23 SBS24 SBS25 SBS26 SBS27 SBS28 SBS29 SBS30 SBS31 SBS32 SBS33 SBS34 SBS35 SBS36 SBS37 SBS38 SBS39 SBS40 SBS41 SBS42 SBS43 SBS44 SBS45 SBS46 SBS47 SBS48 SBS49 SBS50 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-A[C>T]T,92,51,181,18,32,157,33,57,43,119,125,31,26,52,121,117,67,63,37,78,252,155,68,192,224,123,110,74,123,113,36,133,64,45,129,143,191,107,75,108,370,165,107,35,17,57,94,67,134,26,195,34,38,95,87,182,47,109,84,89,51,31,51,241,35,150,45,48,135,200,45,128,61,126,114,40,153,40,53,59,119,119,147,66,164,139,47,67,267,158,51,145,59,196,263,36,39,88,121,18,24,40,81,43,25,26,18,86,51,123,34,47,29,21,71,119,46,61,22,251,139,33,27,39,24,32,31,229,37,22,26,19,39,37,115,18,50,30,74,84,36,27,66,8,23,18,37,32,155,83,218,67,84,40,31,61,112,159,70,81,33,141,29,26,123,85,53,54,67,89,95,101,182,89,126,40,34,17,27,13,16,260,117,74,60,47,58,19,47,37,40,163,119,65,43,31,137,31 -C[C>T]A,75,76,112,21,46,140,53,66,40,100,129,44,65,46,61,123,103,68,50,118,179,94,91,119,164,196,103,88,118,80,31,112,54,69,82,117,126,116,92,265,217,121,119,56,35,72,90,107,110,39,158,64,102,102,169,177,40,221,290,93,91,51,44,217,51,179,75,97,152,117,93,192,69,92,114,64,109,62,58,47,158,98,137,128,121,84,57,53,150,200,55,172,76,130,219,43,43,130,87,23,40,45,89,81,40,33,34,80,44,119,18,125,42,44,88,113,53,69,29,212,124,54,38,67,28,33,55,124,47,51,60,31,40,50,580,29,37,38,61,95,37,34,116,16,47,16,80,42,115,78,171,74,83,64,38,111,125,150,94,80,91,159,49,22,135,84,80,63,104,135,70,99,130,412,645,52,37,35,22,25,43,267,117,98,91,65,74,44,60,32,56,127,149,66,55,43,91,33 -C[C>T]C,69,67,89,28,39,97,59,59,54,77,93,51,33,35,52,79,73,46,50,78,170,60,67,75,129,114,70,66,90,78,52,101,28,45,61,92,96,94,73,137,183,93,67,57,31,50,53,73,57,36,138,51,48,95,119,130,54,109,79,78,57,46,56,167,36,142,50,65,88,87,63,120,60,114,82,45,62,57,42,48,97,90,95,62,103,82,42,48,124,131,57,102,69,97,126,44,50,110,60,21,40,46,63,53,42,35,21,63,56,102,27,47,35,33,56,83,42,77,20,121,105,67,31,43,33,39,44,105,49,34,42,24,22,48,141,36,48,28,55,86,25,33,61,28,45,31,52,40,70,48,142,64,46,70,20,73,89,114,73,63,51,123,45,26,108,58,70,61,48,106,59,83,91,100,138,43,38,39,30,24,30,175,100,103,87,54,51,30,60,41,42,116,105,48,54,36,74,25 -C[C>T]G,93,163,139,45,72,108,110,108,85,88,130,58,50,82,72,122,161,289,88,138,199,80,148,105,163,247,95,88,293,104,147,157,88,82,78,94,126,182,173,278,168,119,84,76,70,132,64,119,110,83,142,100,142,72,155,160,98,143,153,154,136,94,102,173,66,273,86,133,128,120,156,271,107,109,115,90,119,111,140,94,182,109,217,139,149,79,155,97,120,323,91,99,175,122,123,105,105,152,83,78,74,90,88,116,101,80,63,96,96,98,61,85,99,73,111,111,114,73,86,100,84,114,84,80,61,68,111,144,82,81,83,72,59,101,123,44,103,53,69,224,47,103,82,60,82,42,66,63,111,64,93,53,75,110,61,179,84,124,169,99,91,238,52,49,89,130,122,81,61,190,121,67,106,119,236,49,109,68,56,53,58,166,89,166,117,112,94,60,86,82,80,86,113,147,113,58,105,64 -C[C>T]T,107,94,185,27,49,220,68,75,56,162,117,43,42,63,125,119,104,84,54,101,295,149,97,168,216,181,167,101,128,131,54,123,81,57,130,131,205,134,97,185,368,185,156,45,31,65,104,79,180,36,219,57,73,112,148,259,52,155,186,79,89,48,58,242,50,175,81,68,165,186,88,203,64,141,153,51,129,62,47,62,158,132,163,93,165,144,78,91,266,240,69,189,75,167,221,43,59,121,130,39,35,52,92,75,47,38,32,94,70,146,34,85,49,50,90,135,60,104,25,271,186,56,43,59,37,51,45,200,43,47,49,36,50,55,283,21,59,34,68,94,52,38,87,24,46,22,81,41,155,107,223,97,96,65,43,116,166,162,77,87,65,178,46,41,152,98,77,75,83,101,94,113,189,224,330,52,58,37,29,31,35,374,125,104,95,76,70,40,58,43,46,162,134,90,54,36,126,33 -G[C>T]A,68,75,86,13,37,99,74,58,38,123,117,40,45,39,73,102,80,57,50,100,188,102,69,92,186,151,99,65,115,62,51,114,43,44,84,86,128,120,88,152,207,124,69,49,16,66,44,85,101,39,178,59,79,96,109,184,45,141,162,70,61,58,55,191,50,221,47,59,124,127,74,136,86,128,96,49,143,66,50,49,105,101,116,108,133,91,64,57,189,186,70,102,91,145,194,32,51,101,83,28,24,40,75,61,44,21,36,78,67,114,27,69,48,28,81,108,42,94,36,165,129,34,33,51,33,28,50,130,46,42,58,26,21,48,312,41,34,34,77,88,33,35,84,21,29,21,81,36,125,72,150,60,55,58,34,83,107,140,92,49,60,120,47,15,130,71,41,43,63,122,63,85,106,192,292,35,33,34,20,14,27,209,121,77,80,43,52,38,63,37,40,100,122,61,40,30,82,40 -G[C>T]C,46,61,95,22,44,103,80,45,44,79,110,46,33,31,70,70,87,46,44,85,157,57,104,74,136,150,89,64,124,63,37,91,47,36,77,75,117,128,75,121,159,112,71,52,34,53,60,70,84,40,138,52,53,81,74,153,47,99,80,58,67,59,37,201,28,140,52,54,109,103,61,124,62,78,76,25,75,74,30,27,109,92,76,48,123,81,65,56,156,172,55,107,64,114,143,54,50,109,57,30,22,32,70,56,36,30,36,70,46,82,38,41,21,37,62,73,45,81,20,129,109,58,27,41,29,27,59,106,38,31,31,23,26,51,98,24,29,21,68,110,32,31,64,19,43,18,55,49,78,56,135,46,60,56,35,81,74,107,61,49,49,105,34,18,92,76,45,46,37,99,57,71,89,105,133,41,39,42,31,22,30,163,75,66,67,54,40,21,41,45,30,96,78,51,59,21,75,33 -G[C>T]G,90,230,176,71,81,155,124,118,102,116,156,59,66,116,99,145,200,296,106,196,200,82,189,103,161,276,108,95,363,129,138,208,128,117,73,123,143,231,221,286,164,152,98,95,83,141,69,138,119,100,193,120,172,113,151,180,119,172,120,169,182,115,100,150,79,353,99,173,141,147,179,321,125,124,103,91,125,185,138,111,219,113,217,133,193,105,215,105,157,384,115,91,235,105,186,114,128,169,78,75,91,95,92,155,133,81,82,126,111,104,122,77,109,82,139,107,117,90,94,89,120,135,76,98,75,67,116,151,139,101,102,70,57,108,114,62,157,51,95,335,63,92,84,69,112,40,95,56,104,72,162,92,72,110,96,198,82,139,167,126,90,303,67,44,85,154,142,97,54,196,89,63,128,102,160,68,128,84,60,69,94,172,73,225,135,155,104,89,122,84,89,103,153,119,155,61,108,87 -G[C>T]T,74,55,129,10,22,110,35,49,38,93,109,37,31,43,72,72,64,58,35,59,178,93,77,124,153,122,88,72,102,76,45,96,48,36,85,91,128,102,76,150,192,133,82,53,28,46,53,73,88,25,140,55,50,77,72,176,38,98,102,83,61,52,44,187,32,164,39,68,101,126,59,109,45,89,101,44,91,67,53,37,111,106,112,52,114,89,54,52,163,123,48,111,68,119,158,31,48,110,87,29,36,36,72,48,24,22,23,73,36,88,30,53,30,21,58,111,43,69,29,160,95,48,24,36,37,32,29,137,36,33,34,26,36,44,165,30,46,27,51,97,32,33,56,22,45,14,39,30,91,70,148,59,52,42,20,78,107,128,75,72,53,111,34,19,94,68,43,52,59,113,69,75,113,104,158,41,41,33,29,21,25,226,64,82,71,61,45,34,35,38,39,108,97,51,38,21,78,29 -T[C>T]A,139,178,198,28,63,520,136,224,97,106,249,44,745,102,95,303,262,115,64,622,307,111,156,265,475,1753,726,266,155,82,48,153,91,80,162,452,305,257,104,5030,300,270,365,79,63,129,225,187,230,31,347,91,1254,210,2341,448,91,3157,7107,214,433,80,84,580,241,323,286,738,394,253,1642,1913,164,224,522,110,159,93,129,109,860,203,560,2552,222,220,119,108,242,1708,130,1810,222,183,210,62,84,291,147,44,81,172,172,203,83,185,43,99,154,211,38,2780,96,82,137,191,83,635,43,3737,349,94,41,278,84,43,85,156,66,177,484,33,47,109,17364,374,104,637,93,191,47,134,1617,36,44,21,2104,121,152,213,330,207,655,110,79,447,337,208,436,257,1486,511,111,160,399,123,112,306,1455,201,129,291,439,11943,17984,321,74,54,31,134,53,2266,539,688,125,214,222,143,76,50,287,320,244,197,81,412,132,62 -T[C>T]C,126,97,155,24,40,341,98,95,79,98,147,59,180,66,70,166,153,78,69,234,252,85,115,135,262,477,223,124,165,88,36,111,61,70,122,184,196,199,77,1004,243,165,202,65,42,98,83,130,151,42,269,77,235,183,579,207,71,626,1282,130,197,66,76,355,124,243,131,232,236,154,359,544,119,152,210,97,103,80,81,77,249,147,228,512,148,151,69,66,171,521,76,531,121,141,118,52,68,207,89,40,45,84,110,156,57,66,27,93,83,175,29,491,88,52,98,121,73,195,33,748,210,90,35,109,56,54,81,120,62,79,136,37,46,69,2807,76,54,144,86,184,42,54,312,27,44,37,398,57,91,141,223,91,197,103,58,222,146,161,183,110,322,347,80,58,153,106,82,111,372,162,92,203,253,2135,3444,101,59,49,34,48,42,634,182,244,140,117,113,80,61,50,93,210,181,95,82,120,98,49 -T[C>T]G,80,128,117,35,68,101,79,103,53,87,113,37,121,61,66,90,180,189,80,160,131,44,109,90,178,306,106,62,202,88,102,139,69,70,74,108,93,136,143,773,127,90,83,53,57,115,50,93,84,41,119,113,275,62,419,120,66,419,803,136,144,67,91,127,68,187,105,170,132,106,307,346,106,73,99,58,97,89,99,76,261,94,177,408,109,84,108,61,102,384,70,174,110,64,123,72,87,140,81,51,74,91,50,79,71,86,54,75,94,62,53,311,74,60,71,86,77,112,60,304,74,91,56,100,50,55,102,102,76,81,98,39,49,76,1164,67,94,103,55,184,39,67,201,36,61,37,193,51,64,72,89,55,69,78,68,144,64,89,170,100,250,242,48,43,69,89,85,76,160,173,88,59,88,887,1828,69,84,57,37,50,66,216,86,219,69,108,116,47,79,57,76,86,112,114,76,104,72,68 -T[C>T]T,152,128,244,26,72,382,109,147,116,137,192,37,377,91,98,245,186,90,49,386,317,121,141,207,381,1113,401,177,168,100,53,139,68,78,165,313,230,237,114,2588,365,255,279,65,43,127,143,176,217,42,287,79,644,165,1337,339,60,1590,3226,201,278,63,79,460,181,267,213,535,350,182,848,1107,146,182,346,91,128,80,90,102,559,156,388,1275,199,173,99,89,250,939,107,1035,177,229,210,56,89,257,119,30,57,141,164,175,57,142,33,95,109,179,46,1321,88,46,131,197,81,337,47,1727,258,95,39,214,50,52,96,182,69,143,238,49,50,65,7993,190,76,343,107,157,51,78,680,39,42,34,997,106,132,157,313,157,407,111,74,338,261,213,308,179,789,443,73,103,280,94,79,181,768,168,113,245,392,6084,9038,220,64,55,34,96,46,1297,264,485,131,139,195,105,80,54,202,298,219,148,91,302,124,62 -A[T>A]A,43,66,115,17,16,86,44,26,13,57,60,16,21,28,56,69,33,32,18,23,163,80,60,76,98,120,58,48,69,47,27,77,22,27,55,68,74,79,39,44,183,87,40,24,17,35,34,35,94,19,104,33,34,49,39,108,12,82,43,48,32,22,28,123,16,68,30,27,80,73,44,81,20,71,67,18,70,20,29,26,53,56,70,25,85,71,45,36,117,113,35,56,30,83,98,18,15,44,61,15,13,22,57,17,20,20,8,53,37,79,25,29,30,23,54,75,21,32,20,92,102,35,15,25,13,30,22,66,13,20,23,9,18,22,54,12,23,13,47,61,31,22,45,9,12,10,10,18,79,46,150,38,31,32,14,25,97,90,68,33,10,77,22,20,77,44,22,31,25,48,34,43,71,20,33,27,16,24,15,13,13,211,70,34,55,20,26,18,28,21,25,100,81,35,26,12,55,9 -A[T>A]C,25,19,75,20,24,46,31,21,27,48,64,16,16,20,41,61,24,30,24,21,118,68,38,66,66,97,62,30,43,41,11,43,22,22,43,39,60,38,26,44,134,68,38,23,9,22,27,31,74,23,107,16,31,39,40,93,24,68,25,27,28,34,21,101,12,49,42,23,59,78,27,58,25,61,45,15,56,17,17,26,49,62,46,17,63,42,22,26,112,65,25,44,39,64,69,25,22,43,48,27,21,18,48,15,25,17,15,48,35,68,21,15,28,17,37,65,27,57,8,91,58,29,15,24,15,15,27,79,9,25,12,20,19,40,24,8,28,9,42,34,13,11,38,16,15,20,19,20,55,36,89,25,35,32,9,30,63,71,41,20,24,57,20,23,45,28,23,25,20,32,27,28,60,20,32,17,17,16,16,13,22,141,50,44,37,30,36,11,34,37,15,73,36,30,28,17,47,19 -A[T>A]G,37,30,99,10,17,76,22,29,16,64,60,12,13,21,53,62,38,31,18,36,187,85,43,66,99,74,65,42,45,47,9,65,21,14,84,59,95,75,39,31,197,99,40,23,23,22,48,30,77,16,108,23,22,47,35,117,28,71,34,34,29,19,23,95,22,54,28,21,70,80,29,69,26,61,54,19,60,19,24,21,55,81,52,14,69,53,25,30,134,73,30,74,31,86,100,22,21,30,52,25,11,15,35,19,17,21,13,48,19,81,25,20,19,19,48,78,32,44,21,75,76,26,8,15,18,25,15,105,15,17,32,13,23,23,39,6,18,18,36,31,10,18,36,13,20,13,21,16,74,35,147,41,38,30,12,28,68,100,39,29,16,69,15,12,79,27,17,37,44,40,29,54,88,20,22,19,19,14,6,9,8,167,60,54,39,18,28,9,31,36,26,92,56,30,16,20,51,8 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+clustered_del_1-10Kb,2,0,2,3,0,2,1,1,1,5,4,2,1,3,1,0,2,0,0,0,3,1,1,0,0,0,0,7,0,0,1,3,1,1,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,1,0,1,1,2,2,0,0,0,0,0,0,3,0,0,1,0,0,0,1,0,2,0,0,3,1,0,2,0,0,1,0,0,0,0,1,3,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,1,3,4,10,0,5,0,1,8,0,3,5,0,1,0,3,0,1,0,5,0,9,0,2,2,0,0,0,1,0,0,0,6,0,0,3,3,3,0,3,2,0,3,1,0,0,1,0,3,2,0,0,3,0,0,1,0,2,0,5,4,1,2,1,0,2,6,2,1,0,1,0,1,0,4,0,1,0 +clustered_del_10-100Kb,1,0,0,2,0,2,0,0,1,1,3,1,0,0,1,0,0,0,2,0,0,0,3,0,0,0,2,3,0,0,0,3,1,0,1,2,0,0,0,0,4,2,1,1,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,0,1,0,1,0,0,2,1,0,0,1,0,0,5,0,0,1,0,0,0,2,0,1,0,0,4,1,0,1,0,0,2,0,0,0,1,0,3,2,0,0,1,0,0,0,0,2,0,0,2,0,0,0,0,0,1,2,0,2,2,2,2,1,8,0,2,5,0,0,0,1,0,4,0,5,0,5,0,3,1,0,0,0,0,0,0,3,4,0,1,2,0,2,0,2,2,2,1,0,1,0,1,1,0,4,0,3,6,1,0,4,0,4,0,2,2,2,3,1,0,2,1,1,0,0,2,1,1,0,4,0,0,0 +clustered_del_100Kb-1Mb,10,2,2,12,0,8,3,1,3,6,0,1,0,0,2,1,0,0,7,2,1,0,3,0,0,0,9,4,2,0,3,6,3,0,0,4,0,0,0,1,15,7,2,2,0,0,0,0,0,3,1,0,3,0,0,2,2,5,0,0,3,0,1,1,5,0,6,2,0,2,0,0,9,0,0,3,0,5,0,4,1,2,1,0,8,0,0,2,1,2,4,0,3,0,5,1,9,1,0,0,4,0,0,0,0,5,2,0,0,0,0,0,1,1,2,1,0,4,2,3,0,2,8,2,3,9,0,4,0,13,0,0,0,8,0,6,0,1,5,0,1,0,1,0,0,4,5,0,0,7,2,1,0,9,6,6,4,0,0,0,4,4,0,13,0,1,7,1,1,7,4,4,0,4,6,2,7,4,0,4,3,2,0,0,0,7,4,0,3,0,1,0 +clustered_del_1Mb-10Mb,24,1,0,27,0,10,8,0,14,6,1,0,1,0,7,1,2,0,12,5,0,0,1,0,0,0,3,0,3,0,11,10,16,3,3,9,0,0,0,2,6,17,0,7,0,0,4,0,0,4,0,3,3,0,0,4,2,28,0,0,6,0,2,2,3,0,6,1,0,6,0,3,15,0,0,5,0,3,1,8,0,6,2,0,17,0,1,3,7,5,19,1,5,0,5,2,10,6,0,0,4,0,1,0,0,11,1,0,7,0,0,0,5,1,5,0,1,9,3,22,4,1,24,15,6,31,0,4,0,24,0,1,0,1,0,26,0,3,11,1,0,3,0,0,0,20,20,0,2,16,10,5,0,6,20,16,15,0,1,1,0,8,1,18,0,9,35,5,3,11,8,12,0,9,14,5,9,7,0,6,1,2,0,3,1,13,5,0,0,0,0,0 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+clustered_tds_10-100Kb,0,0,2,3,0,1,0,0,1,4,1,2,0,0,2,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,4,2,0,2,2,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,9,0,0,1,0,1,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,1,1,0,1,0,0,4,0,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,5,1,0,1,2,4,0,3,6,1,0,3,0,0,0,9,2,2,0,6,0,2,1,3,2,0,0,0,0,0,0,7,4,0,0,1,1,0,2,0,5,3,2,1,1,0,2,0,0,1,0,4,3,0,0,2,0,1,0,3,2,2,1,0,0,3,1,0,0,0,0,1,0,1,0,1,2,0 +clustered_tds_100Kb-1Mb,14,0,0,11,0,7,0,0,3,7,1,2,0,0,2,0,2,0,8,1,2,2,3,0,0,0,2,0,4,0,3,6,4,0,0,10,0,0,0,0,4,4,1,0,0,0,1,0,0,4,0,0,0,0,0,0,0,8,0,0,2,0,0,3,0,0,3,1,0,1,0,0,3,0,0,3,0,2,0,2,0,1,0,0,5,0,0,0,0,3,3,0,2,0,5,0,4,0,0,0,5,0,0,0,0,7,0,0,1,0,0,0,1,0,5,3,0,6,1,2,0,1,5,4,5,7,0,4,0,8,0,0,0,35,0,7,0,2,5,0,0,1,0,0,0,11,13,0,0,3,5,0,1,2,10,8,7,0,0,0,7,4,0,9,0,4,10,0,0,7,3,5,0,6,8,0,4,2,0,5,8,1,0,7,0,4,2,0,0,1,2,0 +clustered_tds_1Mb-10Mb,21,0,1,21,0,15,4,0,17,2,0,0,4,0,5,0,3,0,9,8,0,1,2,0,0,0,4,0,2,0,13,6,11,6,4,5,0,0,0,2,8,15,1,11,0,0,7,0,0,3,1,4,1,0,0,3,0,37,0,0,6,0,0,1,6,1,4,2,0,4,0,2,12,0,0,7,0,1,0,3,0,5,2,0,10,0,2,2,2,2,16,0,8,0,4,2,17,1,0,0,2,0,1,0,0,9,1,0,4,0,0,0,2,0,8,0,1,5,5,14,3,5,16,25,4,28,0,4,0,24,0,1,1,2,0,28,1,7,14,0,1,1,0,0,2,14,13,0,2,16,9,5,0,8,17,13,20,0,2,0,3,6,2,9,0,6,26,1,3,4,7,8,0,6,10,5,9,9,0,5,0,1,0,6,0,14,4,0,0,0,1,1 +clustered_tds_>10Mb,9,0,0,18,0,14,10,0,1,3,0,0,4,0,16,1,0,0,18,2,1,0,0,0,0,0,0,4,0,0,32,4,4,0,4,8,0,0,0,0,2,10,0,1,0,0,8,0,0,9,0,3,0,0,0,0,5,19,0,0,0,0,1,0,4,2,7,1,0,5,0,1,1,0,0,4,0,0,0,2,1,2,1,0,5,0,8,2,1,6,7,2,1,0,3,7,8,9,0,0,5,0,0,0,1,7,1,0,2,0,0,0,7,1,9,1,0,1,1,4,2,13,13,2,7,14,0,0,0,48,0,0,0,0,1,12,1,3,5,1,0,0,2,0,0,11,1,0,1,5,5,4,0,6,4,12,4,0,2,0,1,2,0,3,0,6,30,2,2,15,0,11,0,6,4,13,8,11,0,3,0,0,0,0,1,23,2,0,0,0,0,2 +clustered_inv_1-10Kb,20,0,11,5,0,10,2,0,3,6,11,2,1,3,3,0,2,0,2,1,2,0,2,0,0,0,3,3,0,0,1,12,13,0,2,0,0,0,0,1,5,6,0,2,0,0,1,0,0,4,0,0,0,0,0,1,1,2,0,0,0,0,0,0,4,7,3,1,0,1,0,0,9,0,0,5,0,0,1,4,0,3,0,0,8,3,0,6,0,1,2,0,0,0,1,0,7,1,0,0,2,0,0,0,0,7,4,0,2,0,0,0,0,1,2,3,7,8,0,3,1,3,9,2,6,10,0,0,0,5,3,8,1,3,31,10,2,2,8,2,0,2,0,0,0,2,4,0,0,17,12,5,0,1,9,7,5,2,2,3,2,1,1,5,0,6,4,2,1,4,4,9,0,14,2,3,8,11,0,2,3,3,3,0,0,0,9,0,2,0,0,1 +clustered_inv_10-100Kb,4,0,0,7,0,6,2,0,5,6,1,4,0,0,3,0,4,0,0,1,3,0,3,0,0,0,1,0,0,0,3,5,2,0,1,2,0,0,0,1,6,8,0,4,0,0,1,0,0,2,0,2,0,0,0,0,3,2,0,0,2,0,0,1,1,0,0,1,0,2,0,0,10,0,0,2,0,1,0,0,1,3,0,0,10,1,1,0,1,3,3,0,2,0,2,0,6,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,3,4,2,1,4,1,0,6,0,0,0,11,0,0,0,6,1,7,1,1,2,0,0,1,0,0,0,3,5,0,0,9,2,4,1,2,4,9,3,1,1,0,0,4,0,5,0,4,8,0,0,7,2,7,0,11,5,3,2,2,0,8,3,1,0,0,0,3,1,0,0,0,1,0 +clustered_inv_100Kb-1Mb,25,2,1,17,0,16,1,0,10,2,1,2,1,0,3,0,1,0,11,1,1,0,3,0,0,0,4,4,3,0,2,14,10,0,0,9,0,0,0,0,7,16,2,5,0,0,2,0,0,6,0,4,0,0,0,0,1,11,0,0,6,0,1,0,4,0,11,2,0,2,0,0,7,0,0,2,0,2,3,3,2,3,3,0,10,0,3,1,1,5,8,0,3,0,6,1,11,2,0,0,5,0,0,0,0,13,1,1,5,0,0,0,0,0,5,0,0,9,2,4,2,3,12,5,9,15,0,5,0,19,0,1,0,4,0,27,0,3,9,1,2,1,0,0,4,11,6,0,0,2,7,8,4,5,17,11,4,0,1,1,3,8,1,12,0,5,17,1,1,8,4,6,0,6,12,4,7,6,0,7,2,3,0,0,0,14,8,0,1,0,3,0 +clustered_inv_1Mb-10Mb,39,2,1,45,0,36,13,0,42,5,0,0,4,0,14,0,2,0,24,14,2,1,1,0,0,0,13,0,9,0,17,24,26,3,8,12,0,0,0,5,21,30,2,11,0,0,9,0,0,16,2,10,2,0,0,7,4,48,0,0,10,0,2,3,12,2,8,1,0,12,0,6,40,0,0,10,0,6,0,13,4,15,0,0,23,0,3,4,10,7,25,0,6,0,8,2,38,5,0,0,8,0,2,0,1,18,2,0,9,0,0,0,6,2,23,0,3,17,4,28,12,7,37,38,20,68,0,10,0,40,0,0,2,1,2,52,1,13,20,2,1,0,0,0,0,25,16,0,3,30,11,12,0,16,28,30,28,0,1,3,1,12,1,32,0,17,47,1,8,19,13,13,0,18,25,4,30,11,0,13,3,6,0,2,1,29,9,0,0,0,0,1 +clustered_inv_>10Mb,16,0,0,27,0,35,25,2,0,3,0,0,3,0,21,3,0,0,31,2,0,1,0,0,0,0,2,8,1,0,57,8,8,0,7,32,0,0,0,1,8,23,0,7,0,0,9,0,0,11,3,9,2,0,0,1,5,39,0,0,1,0,2,2,10,1,7,10,0,9,0,3,0,0,0,10,0,3,1,8,4,8,10,0,13,0,9,6,1,9,19,4,1,0,16,5,37,12,0,0,8,0,0,0,5,6,8,0,8,0,0,0,14,1,15,1,1,9,8,13,0,27,25,2,21,40,0,0,0,87,0,1,0,2,0,10,1,2,22,2,0,2,2,0,0,22,11,0,2,12,7,8,0,28,11,24,4,0,8,1,2,2,1,1,0,15,56,7,3,15,0,25,0,11,12,32,29,19,0,6,0,0,0,0,2,54,2,0,0,0,0,4 +clustered_trans,50,0,3,108,0,78,85,0,40,69,5,2,0,3,0,0,14,0,13,11,3,4,23,0,0,0,19,13,1,0,19,130,23,11,0,12,0,0,0,2,56,50,0,16,0,0,13,0,0,107,4,6,20,0,0,6,6,5,0,0,1,0,0,20,58,19,0,5,0,19,0,0,78,12,0,9,0,11,1,23,4,48,18,0,33,0,0,5,1,9,64,0,18,0,14,3,68,0,0,0,14,0,8,0,0,39,17,1,2,0,0,0,6,0,34,1,2,24,5,75,0,3,274,30,11,98,0,5,0,111,0,9,5,7,0,138,0,17,100,0,1,10,6,0,14,9,15,0,0,38,84,12,0,24,36,67,9,7,0,1,12,36,6,21,0,40,11,7,8,97,5,36,0,94,28,9,44,36,0,14,14,8,1,0,11,13,1,5,8,5,4,3 +non-clustered_del_1-10Kb,13,7,54,4,2,6,2,21,3,16,25,51,3,34,10,2,285,5,4,8,36,36,36,40,43,21,19,78,2,51,14,13,16,1,1,3,0,7,4,3,3,26,0,5,1,0,2,1,0,1,0,2,1,0,0,38,7,133,1,0,6,0,2,46,0,88,0,1,1,3,0,53,14,1,2,4,0,1,3,4,0,3,2,1,4,60,23,82,1,25,4,0,7,0,3,1,2,7,72,111,8,0,0,59,1,7,19,27,58,27,63,14,1,7,3,21,71,69,6,5,5,7,23,40,54,4,40,82,2,5,2,38,11,39,40,4,11,46,2,0,1,2,45,56,3,71,40,0,8,3,28,6,4,45,27,0,7,56,3,20,35,4,140,5,0,15,25,0,3,3,15,13,46,13,29,10,6,6,52,1,116,34,48,8,4,5,4,64,133,32,48,0 +non-clustered_del_10-100Kb,4,3,10,3,0,6,0,11,1,21,11,46,7,15,19,2,48,3,2,9,31,18,15,7,16,18,18,64,2,41,34,5,29,1,1,2,1,1,0,2,4,38,2,3,0,1,0,0,0,0,2,2,1,0,0,20,1,15,0,0,1,0,0,23,1,30,1,0,2,4,0,41,7,0,1,2,0,1,0,5,1,0,0,0,1,10,4,17,2,7,2,0,3,1,1,1,0,17,12,24,2,0,0,28,0,5,10,5,27,10,24,15,0,11,1,40,17,42,9,4,5,3,14,30,17,3,14,32,0,6,4,65,8,14,22,1,2,17,2,0,1,1,8,25,1,21,17,1,10,5,34,10,5,127,5,3,10,23,4,8,22,0,51,1,0,11,6,0,2,6,7,9,20,10,15,7,1,0,9,4,98,13,34,7,2,1,6,18,135,3,15,0 +non-clustered_del_100Kb-1Mb,14,3,7,6,0,13,2,1,3,13,1,25,7,7,11,2,7,2,16,9,24,9,5,5,8,8,7,52,4,27,15,3,24,0,2,1,2,1,1,1,4,24,0,11,1,0,1,0,0,2,0,4,1,0,0,3,1,6,0,0,3,0,0,3,3,5,0,1,1,5,0,3,4,0,2,3,0,0,2,14,1,2,0,1,6,0,1,12,6,5,5,0,4,0,3,2,3,16,3,8,2,0,1,13,0,0,11,4,2,1,1,3,1,5,5,22,3,6,4,8,3,8,26,6,5,4,2,16,0,20,0,17,3,11,5,2,1,11,2,0,0,0,8,6,5,8,15,1,13,6,41,13,8,127,7,10,19,12,6,3,14,3,9,3,0,11,8,0,1,5,2,5,6,13,12,5,2,5,10,4,40,3,13,7,4,3,8,12,29,4,25,1 +non-clustered_del_1Mb-10Mb,8,2,6,9,0,29,4,6,4,11,2,4,2,3,5,1,7,2,9,11,7,3,5,1,4,2,5,4,8,2,5,6,18,2,3,2,0,0,0,4,6,15,0,8,0,0,5,0,0,14,1,2,0,0,0,3,3,7,0,0,6,0,0,0,1,4,2,0,0,3,1,4,8,0,0,6,0,1,0,3,0,6,4,0,10,2,3,8,2,11,7,1,9,0,7,2,7,7,3,8,2,0,1,2,0,4,2,4,11,3,0,2,3,0,8,2,3,8,8,7,4,6,23,1,7,15,5,7,0,20,1,5,1,4,3,6,2,6,6,0,0,1,7,6,4,13,8,0,1,8,6,8,4,4,15,12,8,5,3,1,5,1,4,2,0,15,8,3,1,4,5,8,1,18,2,2,11,12,2,9,9,8,2,8,5,1,3,7,6,5,3,2 +non-clustered_del_>10Mb,10,4,6,3,0,24,2,5,3,11,0,1,2,5,6,0,3,3,8,4,7,6,9,5,6,10,1,6,4,6,12,7,8,0,1,3,0,0,1,4,7,21,0,1,4,0,3,0,0,22,1,6,0,0,1,3,0,4,0,0,0,0,0,7,11,12,5,0,1,4,0,3,1,1,2,10,0,0,0,7,1,5,1,0,0,9,4,10,4,6,7,1,2,0,15,1,3,4,5,11,2,1,2,2,0,5,3,7,6,4,3,2,4,1,6,5,12,10,7,5,4,9,18,6,7,2,8,9,0,13,4,7,1,2,3,2,3,9,5,0,0,0,8,8,0,16,2,0,1,6,3,4,1,11,18,10,1,6,5,1,7,0,10,1,0,15,10,2,0,4,5,12,2,9,1,2,4,9,3,2,10,5,3,6,2,5,5,17,6,2,7,3 +non-clustered_tds_1-10Kb,2,0,76,1,0,2,1,126,1,7,2,24,2,20,2,0,1,0,0,1,34,40,125,63,48,246,0,1,2,32,0,2,1,0,0,0,0,1,0,0,2,7,0,1,0,0,0,0,0,2,0,0,0,1,0,3,0,8,0,1,1,0,0,2,1,2,0,1,0,0,0,1,0,1,0,2,0,0,0,0,2,1,0,0,0,2,131,6,0,1,1,0,2,0,1,0,0,5,177,266,2,0,0,27,0,1,4,79,5,39,3,86,1,4,2,1,5,6,3,0,0,2,1,49,73,2,53,180,0,2,78,4,0,6,33,2,98,224,0,0,0,0,3,154,3,303,10,0,0,1,3,1,3,2,55,1,5,257,0,81,12,0,6,0,0,2,1,0,0,2,5,1,4,3,16,1,0,0,2,1,13,2,1,7,3,0,0,345,8,160,93,0 +non-clustered_tds_10-100Kb,6,3,74,1,0,5,1,31,2,16,4,11,10,4,10,1,8,0,1,5,19,45,57,60,61,82,7,9,6,29,2,1,9,0,1,0,0,4,0,5,3,2,0,2,0,0,0,0,0,2,0,1,0,0,0,1,0,271,0,0,2,0,0,9,0,6,1,0,0,0,0,7,1,0,0,0,0,0,0,2,0,2,2,0,2,0,51,16,0,8,10,0,2,0,1,0,0,3,54,153,1,0,0,33,0,4,2,60,7,13,5,29,0,13,0,10,0,1,8,3,1,4,1,40,74,0,19,128,0,6,88,5,7,36,15,1,30,171,3,0,0,1,0,88,5,204,29,0,5,1,9,2,14,12,93,0,7,92,2,41,58,2,2,0,0,28,4,0,4,0,6,5,3,9,5,1,1,4,2,10,47,1,2,57,1,1,0,65,4,86,96,0 +non-clustered_tds_100Kb-1Mb,21,3,23,4,0,5,3,6,2,72,6,8,29,3,3,10,8,1,3,15,37,33,31,20,30,17,6,7,14,16,0,11,22,0,3,0,0,1,1,2,0,20,0,7,0,0,0,0,0,2,1,0,0,0,0,3,7,72,0,0,14,0,1,34,1,14,4,1,0,3,0,7,3,0,0,2,1,0,1,3,0,3,3,1,7,1,24,4,0,4,8,1,13,0,2,0,3,23,8,29,9,1,0,32,0,3,2,15,6,5,9,20,3,13,3,32,0,2,4,5,2,13,8,15,35,1,10,54,1,16,2,13,2,250,0,1,0,25,15,0,0,1,2,29,7,112,181,0,6,2,9,8,74,20,49,1,23,32,1,5,80,1,6,0,4,98,5,0,2,3,9,6,2,16,14,2,1,42,2,76,107,2,0,209,0,1,1,20,2,18,19,0 +non-clustered_tds_1Mb-10Mb,11,1,10,5,0,17,2,12,1,7,1,1,6,3,6,4,4,2,6,12,9,5,8,1,12,3,2,1,8,6,5,7,13,2,2,0,0,0,2,5,1,16,0,7,0,0,1,0,0,6,3,2,1,0,0,4,3,13,0,0,2,0,1,14,3,3,1,1,0,4,0,2,14,0,0,6,1,1,0,4,0,2,2,0,6,1,13,6,1,10,9,1,5,0,7,0,5,3,6,5,6,0,1,1,0,4,5,3,8,4,3,5,0,0,5,5,2,5,1,6,9,2,13,4,13,6,2,16,0,10,4,2,1,17,2,1,1,9,3,1,0,2,2,5,2,69,25,0,4,6,7,7,25,6,26,6,4,24,1,2,9,2,3,0,0,21,8,1,2,5,4,8,1,15,11,1,4,11,1,4,12,4,3,70,4,4,3,5,5,3,2,3 +non-clustered_tds_>10Mb,6,0,2,2,0,29,5,5,4,7,1,2,1,6,6,0,4,1,2,3,4,3,6,0,5,7,2,6,4,5,7,2,9,0,1,0,0,0,0,1,7,12,0,3,1,1,3,0,0,21,0,3,0,0,0,4,2,5,0,0,0,0,0,2,4,6,0,0,0,1,0,3,3,1,0,4,0,1,0,5,0,1,2,0,1,3,4,12,3,11,2,1,2,0,13,3,4,4,6,3,2,1,2,1,0,2,3,5,5,1,1,5,3,1,6,3,9,9,1,1,2,2,16,1,12,4,1,10,0,19,3,5,2,1,2,2,0,10,4,1,0,0,6,7,1,15,7,0,0,2,4,2,3,11,8,2,1,12,2,5,10,1,5,0,0,9,11,1,0,3,2,7,1,10,0,2,4,7,5,3,11,3,1,9,2,6,6,7,3,3,3,1 +non-clustered_inv_1-10Kb,6,1,533,46,0,18,176,43,4,21,11,14,4,13,7,1,21,2,9,16,8,21,181,4,6,11,16,9,11,4,10,17,21,0,0,2,0,0,1,7,5,9,0,11,0,1,0,0,0,3,0,1,0,0,0,14,2,10,1,0,0,0,0,9,2,17,0,0,0,9,0,7,19,0,3,7,0,0,1,2,0,3,1,1,8,2,5,22,1,7,18,1,7,0,6,1,7,7,10,16,4,0,1,9,0,16,12,5,6,21,12,4,1,7,4,11,21,19,11,10,6,3,12,8,8,4,8,14,0,4,63,18,4,7,249,3,28,10,1,94,49,40,8,17,40,25,34,0,13,13,23,12,0,15,12,214,8,17,17,5,18,1,13,34,0,17,10,1,1,66,8,103,8,30,8,5,11,18,15,11,15,8,77,4,6,0,6,22,9,15,6,0 +non-clustered_inv_10-100Kb,5,1,5,3,0,9,0,6,3,25,6,8,10,0,9,1,9,4,3,2,7,9,23,8,15,3,1,3,2,7,6,6,9,0,0,2,0,0,3,2,2,13,0,5,0,0,1,0,0,4,0,0,0,0,0,2,1,9,0,0,1,0,0,6,0,3,1,1,0,2,0,9,12,0,1,2,0,0,0,0,0,0,1,0,4,0,2,4,1,3,5,1,2,0,9,0,1,7,8,7,2,1,0,8,0,2,6,4,5,4,1,4,0,4,4,10,2,7,8,7,6,2,9,0,8,4,3,6,0,8,2,4,1,15,4,0,0,6,1,0,2,1,0,7,0,9,10,0,5,3,4,7,4,19,13,6,4,4,2,1,15,0,7,0,0,7,1,0,0,3,3,16,2,12,6,5,1,4,5,4,14,2,2,6,2,0,3,5,12,4,15,0 +non-clustered_inv_100Kb-1Mb,6,0,3,5,0,14,3,3,3,13,0,3,4,0,8,1,5,4,6,9,2,2,4,2,6,0,0,4,3,3,3,5,9,0,0,1,2,0,5,1,0,12,0,4,0,1,0,0,0,4,1,1,0,0,0,0,3,2,0,0,4,0,0,3,1,0,2,0,1,7,0,0,1,0,2,5,0,0,1,6,0,6,0,0,4,0,0,1,1,9,8,0,1,0,4,0,5,8,6,8,4,0,1,1,1,2,4,4,2,0,1,1,1,3,4,10,3,3,11,5,2,2,20,0,1,6,0,6,0,12,0,3,2,9,4,0,0,8,1,1,0,0,3,2,1,5,14,0,2,4,8,9,7,11,9,7,3,1,2,5,10,0,2,0,0,8,5,0,0,0,5,4,0,11,3,3,2,2,3,3,6,5,7,6,2,0,4,6,4,3,4,1 +non-clustered_inv_1Mb-10Mb,13,3,3,5,1,33,3,7,10,11,2,3,8,4,12,1,1,1,12,8,10,5,4,2,10,3,1,4,9,0,10,10,18,5,4,1,0,4,0,7,12,22,0,5,0,0,8,0,0,12,0,6,4,0,0,5,1,10,0,0,0,0,1,1,8,5,2,1,2,5,0,0,10,1,3,15,2,2,0,6,1,6,2,0,7,0,6,7,4,12,11,0,8,0,11,1,13,17,5,11,7,0,0,1,0,7,6,2,13,0,2,5,3,1,8,7,7,8,15,15,7,6,24,4,11,20,0,13,0,27,2,5,1,3,7,9,0,9,5,1,0,2,0,5,1,29,13,0,0,9,8,8,5,12,33,17,15,5,2,3,8,1,7,0,0,19,10,1,0,8,7,17,1,29,5,1,13,25,5,4,11,2,1,10,2,11,2,5,6,5,1,1 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[package.dependencies] @@ -1222,13 +1227,13 @@ plot = ["bokeh", "colorcet", "datashader", "holoviews", "matplotlib", "pandas", [[package]] name = "virtualenv" -version = "20.24.5" +version = "20.24.6" description = "Virtual Python Environment builder" optional = false python-versions = ">=3.7" files = [ - {file = "virtualenv-20.24.5-py3-none-any.whl", hash = "sha256:b80039f280f4919c77b30f1c23294ae357c4c8701042086e3fc005963e4e537b"}, - {file = "virtualenv-20.24.5.tar.gz", hash = "sha256:e8361967f6da6fbdf1426483bfe9fca8287c242ac0bc30429905721cefbff752"}, + {file = "virtualenv-20.24.6-py3-none-any.whl", hash = "sha256:520d056652454c5098a00c0f073611ccbea4c79089331f60bf9d7ba247bb7381"}, + {file = "virtualenv-20.24.6.tar.gz", hash = "sha256:02ece4f56fbf939dbbc33c0715159951d6bf14aaf5457b092e4548e1382455af"}, ] [package.dependencies] diff --git a/pyproject.toml b/pyproject.toml index aa0ef2a..54aadb8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "salamander-learn" -version = "0.2.0" +version = "0.2.1" description = "Salamander is a non-negative matrix factorization framework for signature analysis" license = "MIT" authors = ["Benedikt Geiger"] diff --git a/src/salamander/__init__.py b/src/salamander/__init__.py index ea8fcac..e0cc792 100644 --- a/src/salamander/__init__.py +++ b/src/salamander/__init__.py @@ -7,5 +7,5 @@ from .nmf_framework.multimodal_corrnmf import MultimodalCorrNMF from .nmf_framework.mvnmf import MvNMF -__version__ = "0.2.0" +__version__ = "0.2.1" __all__ = ["CorrNMFDet", "KLNMF", "MvNMF", "MultimodalCorrNMF"] diff --git a/src/salamander/nmf_framework/_utils_corrnmf.py b/src/salamander/nmf_framework/_utils_corrnmf.py index b4faf5c..288f52c 100644 --- a/src/salamander/nmf_framework/_utils_corrnmf.py +++ b/src/salamander/nmf_framework/_utils_corrnmf.py @@ -5,7 +5,9 @@ @njit -def update_alpha(X: np.ndarray, L: np.ndarray, U: np.ndarray) -> np.ndarray: +def update_alpha( + X: np.ndarray, beta: np.ndarray, L: np.ndarray, U: np.ndarray +) -> np.ndarray: """ Compute the new sample biases alpha according to the update rule of CorrNMF. @@ -14,8 +16,8 @@ def update_alpha(X: np.ndarray, L: np.ndarray, U: np.ndarray) -> np.ndarray: X : np.ndarray of shape (n_features, n_samples) data matrix - a: asdf - asdf + beta : np.ndarray of shape (n_signatures,) + signature biases L : np.ndarray of shape (dim_embeddings, n_signatures) signature embeddings @@ -26,13 +28,52 @@ def update_alpha(X: np.ndarray, L: np.ndarray, U: np.ndarray) -> np.ndarray: Returns ------- alpha : np.ndarray of shape (n_samples,) - The new sample biases alpha + The new sample biases """ - exp_LTU = np.exp(L.T @ U) - alpha = np.log(np.sum(X, axis=0)) - np.log(np.sum(exp_LTU, axis=0)) + second_term = np.exp(beta[:, np.newaxis] + L.T @ U) + alpha = np.log(np.sum(X, axis=0)) - np.log(np.sum(second_term, axis=0)) return alpha +@njit +def update_beta( + X: np.ndarray, p: np.ndarray, alpha: np.ndarray, L: np.ndarray, U: np.ndarray +) -> np.ndarray: + """ + Compute the new signature biases beta according to the update rule of CorrNMF. + + Parameters + ---------- + X : np.ndarray of shape (n_features, n_samples) + data matrix + + alpha : np.ndarray of shape (n_samples,) + sample biases + + L : np.ndarray of shape (dim_embeddings, n_signatures) + signature embeddings + + U : np.ndarray of shape (dim_embeddings, n_samples) + sample embeddings + + Returns + ------- + beta : np.ndarray of shape (n_signatures,) + The new signature biases + """ + n_features, n_signatures, n_samples = p.shape + first_term = np.zeros(n_signatures) + + for k in range(n_signatures): + for v in range(n_features): + for d in range(n_samples): + first_term[k] += X[v, d] * p[v, k, d] + + second_term = np.exp(alpha + L.T @ U) + beta = np.log(first_term) - np.log(np.sum(second_term, axis=1)) + return beta + + @njit def update_p_unnormalized(W: np.ndarray, H: np.ndarray) -> np.ndarray: """ @@ -65,10 +106,16 @@ def update_p_unnormalized(W: np.ndarray, H: np.ndarray) -> np.ndarray: @njit def objective_function_embedding( - embedding, embeddings_other, alpha, sigma_sq, aux_vec, add_penalty=True + embedding, + embeddings_other, + alpha, + beta, + sigma_sq, + aux_vec, + add_penalty=True, ): r""" - The objective function of a signature or sample embedding in CorrNMF. + The negative objective function of a signature or sample embedding in CorrNMF. Parameters ---------- @@ -85,6 +132,11 @@ def objective_function_embedding( all sample biases. If 'embedding' is a sample embedding, 'alpha' is the bias of the corresponding sample. + beta : np.array of shape (n_signatures,) | float + If 'embedding' is a signature embedding, 'beta' is + the corresponding signature bias. If 'embedding' is a sample embedding, + 'beta' are all signature biases. + sigma_sq : float model variance @@ -108,8 +160,8 @@ def objective_function_embedding( for i in range(n_embeddings_other): of_value += scalar_products[i] * aux_vec[i] - # works for alpha being a scalar or vector - of_value -= np.sum(np.exp(alpha + scalar_products)) + # works for alpha and beta being a scalar or vector + of_value -= np.sum(np.exp(alpha + beta + scalar_products)) if add_penalty: of_value -= np.dot(embedding, embedding) / (2 * sigma_sq) @@ -119,11 +171,17 @@ def objective_function_embedding( @njit def gradient_embedding( - embedding, embeddings_other, alpha, sigma_sq, summand_grad, add_penalty=True + embedding, + embeddings_other, + alpha, + beta, + sigma_sq, + summand_grad, + add_penalty=True, ): r""" - The gradient of the objective function w.r.t. a signature or sample embedding - in CorrNMF. + The negative gradient of the objective function w.r.t. a signature or + sample embedding in CorrNMF. Parameters ---------- @@ -140,6 +198,11 @@ def gradient_embedding( all sample biases. If 'embedding' is a sample embedding, 'alpha' is the bias of the corresponding sample. + beta : np.array of shape (n_signatures,) | float + If 'embedding' is a signature embedding, 'beta' is + the corresponding signature bias. If 'embedding' is a sample embedding, + 'beta' are all signature biases. + sigma_sq : float model variance @@ -151,7 +214,10 @@ def gradient_embedding( This argument is useful for the implementation of multimodal CorrNMF. """ scalar_products = embeddings_other.T.dot(embedding) - gradient = -np.sum(np.exp(alpha + scalar_products) * embeddings_other, axis=1) + # works for alpha and beta being a scalar or vector + gradient = -np.sum( + np.exp(alpha + beta + scalar_products) * embeddings_other, axis=1 + ) gradient += summand_grad if add_penalty: @@ -165,13 +231,14 @@ def hessian_embedding( embedding, embeddings_other, alpha, + beta, sigma_sq, outer_prods_embeddings_other, add_penalty=True, ): r""" - The Hessian of the objective function w.r.t. a signature or sample embedding - in CorrNMF. + The negative Hessian of the objective function w.r.t. a signature or + sample embedding in CorrNMF. Parameters ---------- @@ -188,6 +255,11 @@ def hessian_embedding( all sample biases. If 'embedding' is a sample embedding, 'alpha' is the bias of the corresponding sample. + beta : np.array of shape (n_signatures,) | float + If 'embedding' is a signature embedding, 'beta' is + the corresponding signature bias. If 'embedding' is a sample embedding, + 'beta' are all signature biases. + sigma_sq : float model variance @@ -203,7 +275,8 @@ def hessian_embedding( This argument is useful for the implementation of multimodal CorrNMF. """ dim_embeddings, n_embeddings_other = embeddings_other.shape - scalings = np.exp(alpha + embeddings_other.T.dot(embedding)) + scalar_products = embeddings_other.T.dot(embedding) + scalings = np.exp(alpha + beta + scalar_products) hessian = np.zeros((dim_embeddings, dim_embeddings)) for m1 in range(dim_embeddings): diff --git a/src/salamander/nmf_framework/corrnmf.py b/src/salamander/nmf_framework/corrnmf.py index 16e542b..a99ec8a 100644 --- a/src/salamander/nmf_framework/corrnmf.py +++ b/src/salamander/nmf_framework/corrnmf.py @@ -35,6 +35,9 @@ class CorrNMF(SignatureNMF): - _update_alpha: update the sample exposure biases \alpha + - _update_beta: + update the signature exposure biases \beta + - _update_sigma_sq: update the embedding distribution variance \sigma^2 @@ -162,9 +165,10 @@ def __init__( self.dim_embeddings = dim_embeddings - # initialize data/fitting dependent attributes + # initialize data/fitting-dependent attributes self.W = None self.alpha = None + self.beta = None self.L = None self.U = None self.sigma_sq = None @@ -180,10 +184,11 @@ def signatures(self) -> pd.DataFrame: def exposures(self) -> pd.DataFrame: """ In contrast to the classical NMF framework, the exposure matrix is - restructured and determined by the signature and sample embeddings. + restructured and determined by the signature & sample biases and + embeddings. """ exposures = pd.DataFrame( - np.exp(self.alpha + self.L.T @ self.U), + np.exp(self.alpha + self.beta[:, np.newaxis] + self.L.T @ self.U), index=self.signature_names, columns=self.sample_names, ) @@ -194,7 +199,7 @@ def _n_parameters(self): """ There are n_features * n_signatures parameters corresponding to the signature matrix, each embedding corresponds to dim_embeddings parameters, - and each sample has a bias parameter. + and each signature & sample has a bias. Finally, the model variance is a single positive real number. Note: We do not include the number of auxiliary parameters p. @@ -203,7 +208,7 @@ def _n_parameters(self): n_parameters_embeddings = self.dim_embeddings * ( self.n_signatures + self.n_samples ) - n_parameters_biases = self.n_samples + n_parameters_biases = self.n_samples + self.n_signatures n_parameters_exposures = n_parameters_embeddings + n_parameters_biases n_parameters = n_parameters_signatures + n_parameters_exposures + 1 @@ -357,14 +362,26 @@ def _initialize( init_kwargs=None, ): """ - Initialize the signature matrix W, sample biases alpha, the squared variance, - and the signature and sample embeddings. + Initialize the signature matrix W, sample biases alpha, signature biases beta, + the squared variance, and the signature and sample embeddings. The signatures or signature embeddings can also be provided by the user. - Input: - ------ - init_kwargs: dict - Any further arguments to be passed to the initialization method. + Parameters + ---------- + given_signatures: pd.DataFrame, default=None + A priori known signatures. The number of given signatures has + to be less or equal to the number of signatures of NMF + algorithm instance, and the mutation type names have to match + the mutation types of the count data. + + given_signature_embeddings : np.ndarray, default=None + A priori known signature embeddings of shape (dim_embeddings, n_signatures). + + given_sample_embeddings : np.ndarray, default=None + A priori known sample embeddings of shape (dim_embeddings, n_samples). + + init_kwargs : dict + Any further keyword arguments to pass to the initialization method. This includes, for example, a possible 'seed' keyword argument for all stochastic methods. """ @@ -417,7 +434,8 @@ def _initialize( self.W /= np.sum(self.W, axis=0) self.W = self.W.clip(EPSILON) - self.alpha = np.zeros(self.n_samples, dtype=float) + self.alpha = np.zeros(self.n_samples) + self.beta = np.zeros(self.n_signatures) self.sigma_sq = 1.0 self.L = np.random.multivariate_normal( np.zeros(self.dim_embeddings), @@ -477,6 +495,7 @@ def reorder(self, other_signatures, metric="cosine", keep_names=False): other_signatures, self.signatures, metric=metric ) self.W = self.W[:, reordered_indices] + self.beta = self.beta[reordered_indices] self.L = self.L[:, reordered_indices] if keep_names: diff --git a/src/salamander/nmf_framework/corrnmf_det.py b/src/salamander/nmf_framework/corrnmf_det.py index 6855456..64723da 100644 --- a/src/salamander/nmf_framework/corrnmf_det.py +++ b/src/salamander/nmf_framework/corrnmf_det.py @@ -26,6 +26,9 @@ class CorrNMFDet(CorrNMF): - _update_alpha: update the sample exposure biases \alpha + - _update_beta: + update the signature biases \beta + - _update_sigma_sq: update the variance \sigma^2 assumed in the generative model @@ -49,7 +52,10 @@ class CorrNMFDet(CorrNMF): """ def _update_alpha(self): - self.alpha = _utils_corrnmf.update_alpha(self.X, self.L, self.U) + self.alpha = _utils_corrnmf.update_alpha(self.X, self.beta, self.L, self.U) + + def _update_beta(self, p): + self.beta = _utils_corrnmf.update_beta(self.X, p, self.alpha, self.L, self.U) def _update_sigma_sq(self): embeddings = np.concatenate([self.L, self.U], axis=1) @@ -68,21 +74,23 @@ def _update_p(self): return p def _update_l(self, index, aux_row, outer_prods_U): + beta = self.beta[index] + def objective_fun(l): return _utils_corrnmf.objective_function_embedding( - l, self.U, self.alpha, self.sigma_sq, aux_row + l, self.U, self.alpha, beta, self.sigma_sq, aux_row ) summand_grad = np.sum(aux_row * self.U, axis=1) def gradient(l): return _utils_corrnmf.gradient_embedding( - l, self.U, self.alpha, self.sigma_sq, summand_grad + l, self.U, self.alpha, beta, self.sigma_sq, summand_grad ) def hessian(l): return _utils_corrnmf.hessian_embedding( - l, self.U, self.alpha, self.sigma_sq, outer_prods_U + l, self.U, self.alpha, beta, self.sigma_sq, outer_prods_U ) l = optimize.minimize( @@ -117,19 +125,19 @@ def _update_u(self, index, aux_col, outer_prods_L): def objective_fun(u): return _utils_corrnmf.objective_function_embedding( - u, self.L, alpha, self.sigma_sq, aux_col + u, self.L, alpha, self.beta, self.sigma_sq, aux_col ) summand_grad = np.sum(aux_col * self.L, axis=1) def gradient(u): return _utils_corrnmf.gradient_embedding( - u, self.L, alpha, self.sigma_sq, summand_grad + u, self.L, alpha, self.beta, self.sigma_sq, summand_grad ) def hessian(u): return _utils_corrnmf.hessian_embedding( - u, self.L, alpha, self.sigma_sq, outer_prods_L + u, self.L, alpha, self.beta, self.sigma_sq, outer_prods_L ) u = optimize.minimize( @@ -227,10 +235,11 @@ def fit( n_iteration += 1 if verbose and n_iteration % 100 == 0: - print("iteration ", n_iteration) + print(f"iteration: {n_iteration}; objective: {of_values[-1]:.2f}") self._update_alpha() p = self._update_p() + self._update_beta(p) self._update_LU(p, given_signature_embeddings, given_sample_embeddings) self._update_sigma_sq() diff --git a/src/salamander/nmf_framework/klnmf.py b/src/salamander/nmf_framework/klnmf.py index 8d5e77f..7f419dc 100644 --- a/src/salamander/nmf_framework/klnmf.py +++ b/src/salamander/nmf_framework/klnmf.py @@ -144,7 +144,7 @@ def fit( n_iteration += 1 if verbose and n_iteration % 100 == 0: - print(f"iteration {n_iteration}") + print(f"iteration: {n_iteration}; objective: {of_values[-1]:.2f}") self._update_WH() prev_of_value = of_values[-1] diff --git a/src/salamander/nmf_framework/multimodal_corrnmf.py b/src/salamander/nmf_framework/multimodal_corrnmf.py index 49890ce..e081868 100755 --- a/src/salamander/nmf_framework/multimodal_corrnmf.py +++ b/src/salamander/nmf_framework/multimodal_corrnmf.py @@ -125,7 +125,6 @@ def objective_function(self) -> float: * np.log(2 * np.pi * self.models[0].sigma_sq) ) elbo -= np.sum(self.models[0].U ** 2) / (2 * self.models[0].sigma_sq) - return elbo @property @@ -150,7 +149,6 @@ def _surrogate_objective_function(self) -> float: * np.log(2 * np.pi * self.models[0].sigma_sq) ) sof_value -= np.sum(self.models[0].U ** 2) / (2 * self.models[0].sigma_sq) - return sof_value def loglikelihood(self) -> float: @@ -167,7 +165,9 @@ def _n_parameters(self) -> int: n_parameters_embeddings = self.dim_embeddings * ( np.sum(self.ns_signatures) + self.n_samples ) - n_parameters_biases = self.n_modalities * self.n_samples + n_parameters_biases = self.n_modalities * self.n_samples + np.sum( + self.ns_signatures + ) n_parameters_exposures = n_parameters_embeddings + n_parameters_biases n_parameters = n_parameters_signatures + n_parameters_exposures + 1 @@ -181,6 +181,10 @@ def _update_alphas(self): for model in self.models: model._update_alpha() + def _update_betas(self, ps): + for model, p in zip(self.models, ps): + model._update_beta(p) + def _update_sigma_sq(self): Ls = np.concatenate([model.L for model in self.models], axis=1) embeddings = np.concatenate([Ls, self.models[0].U], axis=1) @@ -206,6 +210,7 @@ def _objective_fun_u(self, u, index, aux_cols): u, model.L, model.alpha[index], + model.beta, sigma_sq, aux_col, add_penalty=False, @@ -214,7 +219,6 @@ def _objective_fun_u(self, u, index, aux_cols): ] ) s -= np.dot(u, u) / (2 * sigma_sq) - return -s def _gradient_u(self, u, index, s_grads): @@ -225,6 +229,7 @@ def _gradient_u(self, u, index, s_grads): u, model.L, model.alpha[index], + model.beta, sigma_sq, s_grad, add_penalty=False, @@ -234,7 +239,6 @@ def _gradient_u(self, u, index, s_grads): axis=0, ) s -= u / sigma_sq - return -s def _hessian_u(self, u, index, outer_prods_Ls): @@ -245,6 +249,7 @@ def _hessian_u(self, u, index, outer_prods_Ls): u, model.L, model.alpha[index], + model.beta, sigma_sq, outer_prods_L, add_penalty=False, @@ -254,7 +259,6 @@ def _hessian_u(self, u, index, outer_prods_Ls): axis=0, ) s -= np.diag(np.full(self.dim_embeddings, 1 / sigma_sq)) - return -s def _update_u(self, index, aux_cols, outer_prods_Ls): @@ -401,10 +405,11 @@ def fit( n_iteration += 1 if verbose and n_iteration % 100 == 0: - print("iteration ", n_iteration) + print(f"iteration: {n_iteration}; objective: {of_values[-1]:.2f}") self._update_alphas() ps = self._update_ps() + self._update_betas(ps) self._update_LsU(ps, given_signature_embeddings, given_sample_embeddings) self._update_sigma_sq() self._update_Ws() @@ -488,8 +493,11 @@ def plot_exposures( colors = [None for _ in range(self.n_modalities)] if sample_order is None: - all_exposures = pd.concat([model.exposures for model in self.models]) - sample_order = _get_sample_order(all_exposures) + all_exposures = [model.exposures for model in self.models] + all_exposures_normalized = pd.concat( + [df / df.sum(axis=0) for df in all_exposures] + ) + sample_order = _get_sample_order(all_exposures_normalized) for n, (ax, model, cols) in enumerate(zip(axes, self.models, colors)): if n < self.n_modalities - 1: diff --git a/src/salamander/nmf_framework/mvnmf.py b/src/salamander/nmf_framework/mvnmf.py index 9c9bee0..988b0de 100644 --- a/src/salamander/nmf_framework/mvnmf.py +++ b/src/salamander/nmf_framework/mvnmf.py @@ -227,7 +227,7 @@ def fit( n_iteration += 1 if verbose and n_iteration % 100 == 0: - print(f"iteration {n_iteration}") + print(f"iteration: {n_iteration}; objective: {of_values[-1]:.2f}") self._update_H() diff --git a/src/salamander/nmf_framework/signature_nmf.py b/src/salamander/nmf_framework/signature_nmf.py index 56f54f9..5a0032c 100644 --- a/src/salamander/nmf_framework/signature_nmf.py +++ b/src/salamander/nmf_framework/signature_nmf.py @@ -13,6 +13,8 @@ ) from ..utils import type_checker, value_checker +EPSILON = np.finfo(np.float32).eps + class SignatureNMF(ABC): """ @@ -286,7 +288,7 @@ def _setup_data_parameters(self, data: pd.DataFrame): Samples are expected to corresponding to columns. """ type_checker("data", data, pd.DataFrame) - self.X = data.values + self.X = data.values.clip(EPSILON) self.n_features, self.n_samples = data.shape self.mutation_types = data.index.values.astype(str) self.sample_names = data.columns.values.astype(str) diff --git a/tests/test_corrnmf.py b/tests/test_corrnmf.py index bba71e9..1b03efd 100644 --- a/tests/test_corrnmf.py +++ b/tests/test_corrnmf.py @@ -38,6 +38,11 @@ def alpha_init(path_suffix): return np.load(f"{PATH_TEST_DATA}/alpha_init_{path_suffix}") +@pytest.fixture +def beta_init(path_suffix): + return np.load(f"{PATH_TEST_DATA}/beta_init_{path_suffix}") + + @pytest.fixture def _p(path_suffix): return np.load(f"{PATH_TEST_DATA}/p_{path_suffix}") @@ -64,7 +69,7 @@ def sigma_sq_init(path_suffix): @pytest.fixture -def model_init(counts, W_init, alpha_init, L_init, U_init, sigma_sq_init): +def model_init(counts, W_init, alpha_init, beta_init, L_init, U_init, sigma_sq_init): n_signatures, dim_embeddings = L_init.shape model = corrnmf_det.CorrNMFDet( n_signatures=n_signatures, dim_embeddings=dim_embeddings @@ -72,6 +77,7 @@ def model_init(counts, W_init, alpha_init, L_init, U_init, sigma_sq_init): model.X = counts.values model.W = W_init model.alpha = alpha_init + model.beta = beta_init model.L = L_init model.U = U_init model.sigma_sq = sigma_sq_init @@ -113,6 +119,11 @@ def alpha_updated(path_suffix): return np.load(f"{PATH_TEST_DATA}/alpha_updated_{path_suffix}") +@pytest.fixture +def beta_updated(path_suffix): + return np.load(f"{PATH_TEST_DATA}/beta_updated_{path_suffix}") + + @pytest.fixture def L_updated(path_suffix): return np.load(f"{PATH_TEST_DATA}/L_updated_{path_suffix}") @@ -137,6 +148,10 @@ def test_update_alpha(self, model_init, alpha_updated): model_init._update_alpha() assert np.allclose(model_init.alpha, alpha_updated) + def test_update_beta(self, model_init, _p, beta_updated): + model_init._update_beta(_p) + assert np.allclose(model_init.beta, beta_updated) + def test_p(self, model_init, _p): p_computed = model_init._update_p() assert np.allclose(p_computed, _p) diff --git a/tests/test_data/nmf_framework/corrnmf/beta_init_nsigs1_dim1.npy b/tests/test_data/nmf_framework/corrnmf/beta_init_nsigs1_dim1.npy new file mode 100644 index 0000000..f07f1cd Binary files /dev/null and b/tests/test_data/nmf_framework/corrnmf/beta_init_nsigs1_dim1.npy differ diff --git a/tests/test_data/nmf_framework/corrnmf/beta_init_nsigs2_dim2.npy b/tests/test_data/nmf_framework/corrnmf/beta_init_nsigs2_dim2.npy new file mode 100644 index 0000000..6d8806c Binary files /dev/null and b/tests/test_data/nmf_framework/corrnmf/beta_init_nsigs2_dim2.npy differ diff --git a/tests/test_data/nmf_framework/corrnmf/beta_updated_nsigs1_dim1.npy b/tests/test_data/nmf_framework/corrnmf/beta_updated_nsigs1_dim1.npy new file mode 100644 index 0000000..0256105 Binary files /dev/null and b/tests/test_data/nmf_framework/corrnmf/beta_updated_nsigs1_dim1.npy differ diff --git a/tests/test_data/nmf_framework/corrnmf/beta_updated_nsigs2_dim2.npy b/tests/test_data/nmf_framework/corrnmf/beta_updated_nsigs2_dim2.npy new file mode 100644 index 0000000..8410c6e Binary files /dev/null and b/tests/test_data/nmf_framework/corrnmf/beta_updated_nsigs2_dim2.npy differ diff --git a/tests/test_data/nmf_framework/multimodal_corrnmf/model0_beta_init.npy b/tests/test_data/nmf_framework/multimodal_corrnmf/model0_beta_init.npy new file mode 100644 index 0000000..6d8806c Binary files /dev/null and b/tests/test_data/nmf_framework/multimodal_corrnmf/model0_beta_init.npy differ diff --git a/tests/test_data/nmf_framework/multimodal_corrnmf/model0_beta_updated.npy b/tests/test_data/nmf_framework/multimodal_corrnmf/model0_beta_updated.npy new file mode 100644 index 0000000..5e6d3b0 Binary files /dev/null and b/tests/test_data/nmf_framework/multimodal_corrnmf/model0_beta_updated.npy differ diff --git a/tests/test_data/nmf_framework/multimodal_corrnmf/model1_beta_init.npy b/tests/test_data/nmf_framework/multimodal_corrnmf/model1_beta_init.npy new file mode 100644 index 0000000..047c0d9 Binary files /dev/null and b/tests/test_data/nmf_framework/multimodal_corrnmf/model1_beta_init.npy differ diff --git a/tests/test_data/nmf_framework/multimodal_corrnmf/model1_beta_updated.npy b/tests/test_data/nmf_framework/multimodal_corrnmf/model1_beta_updated.npy new file mode 100644 index 0000000..67c7f96 Binary files /dev/null and b/tests/test_data/nmf_framework/multimodal_corrnmf/model1_beta_updated.npy differ diff --git a/tests/test_multimodal_corrnmf.py b/tests/test_multimodal_corrnmf.py index ecbbae7..10d3a3b 100644 --- a/tests/test_multimodal_corrnmf.py +++ b/tests/test_multimodal_corrnmf.py @@ -37,6 +37,13 @@ def alphas_init(): ] +@pytest.fixture +def betas_init(): + return [ + np.load(f"{PATH_TEST_DATA}/model{n}_beta_init.npy") for n in range(N_MODALITIES) + ] + + @pytest.fixture def _ps(): return [np.load(f"{PATH_TEST_DATA}/model{n}_p.npy") for n in range(N_MODALITIES)] @@ -71,7 +78,9 @@ def sigma_sq_init(): @pytest.fixture -def multi_model_init(counts, Ws_init, alphas_init, Ls_init, U_init, sigma_sq_init): +def multi_model_init( + counts, Ws_init, alphas_init, betas_init, Ls_init, U_init, sigma_sq_init +): models = [] for n, n_signatures in enumerate(NS_SIGNATURES): @@ -81,6 +90,7 @@ def multi_model_init(counts, Ws_init, alphas_init, Ls_init, U_init, sigma_sq_ini model.X = counts[n].values model.W = Ws_init[n] model.alpha = alphas_init[n] + model.beta = betas_init[n] model.L = Ls_init[n] model.U = U_init model.sigma_sq = sigma_sq_init @@ -135,6 +145,14 @@ def alphas_updated(): ] +@pytest.fixture +def betas_updated(): + return [ + np.load(f"{PATH_TEST_DATA}/model{n}_beta_updated.npy") + for n in range(N_MODALITIES) + ] + + @pytest.fixture def Ls_updated(): return [ @@ -165,6 +183,12 @@ def test_update_alpha(self, multi_model_init, alphas_updated): for model, alpha_updated in zip(multi_model_init.models, alphas_updated): assert np.allclose(model.alpha, alpha_updated) + def test_update_beta(self, multi_model_init, _ps, betas_updated): + multi_model_init._update_betas(_ps) + + for model, beta_updated in zip(multi_model_init.models, betas_updated): + assert np.allclose(model.beta, beta_updated) + def test_p(self, multi_model_init, _ps): ps_computed = multi_model_init._update_ps()