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PoonLab / vindels

Developing an empirical model of sequence insertion and deletion in virus genomes
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Thin log file so that step numbers match tree log #60

Closed ArtPoon closed 5 years ago

ArtPoon commented 5 years ago 
> log <- read.table('28467.log', header=T, comment.char='#')
> dim(log)
[1] 10001    40
> log[1,]
  state posterior     prior likelihood treeModel.rootHeight skyline.popSize1
1     0 -63409.69 -10219.26  -53190.43              827.585                1
  skyline.popSize2 skyline.popSize3 skyline.popSize4 skyline.popSize5
1                1                1                1                1
  skyline.popSize6 skyline.popSize7 skyline.popSize8 skyline.popSize9
1                1                1                1                1
  skyline.popSize10 skyline.groupSize1 skyline.groupSize2 skyline.groupSize3
1                 1                  6                  5                  5
  skyline.groupSize4 skyline.groupSize5 skyline.groupSize6 skyline.groupSize7
1                  5                  5                  5                  5
  skyline.groupSize8 skyline.groupSize9 skyline.groupSize10 kappa1 kappa2
1                  5                  5                   5      2      2
  frequencies1 frequencies2 frequencies3 frequencies4 alpha ucld.mean
1         0.25         0.25         0.25         0.25   0.5         1
  ucld.stdev meanRate coefficientOfVariation   covariance treeLikelihood
1  0.3333333 1.034891              0.3219494 -0.003427711      -53190.43
  branchRates   skyline
1   -471.7472 -4919.259
> log2 <- log[seq(1, nrow(log), 1001), ]
> dim(log2)
[1] 10 40
> log2 <- log[seq(1, nrow(log), length.out=1001), ]
> dim(log2)
[1] 1001   40
> log2[1,]
  state posterior     prior likelihood treeModel.rootHeight skyline.popSize1
1     0 -63409.69 -10219.26  -53190.43              827.585                1
  skyline.popSize2 skyline.popSize3 skyline.popSize4 skyline.popSize5
1                1                1                1                1
  skyline.popSize6 skyline.popSize7 skyline.popSize8 skyline.popSize9
1                1                1                1                1
  skyline.popSize10 skyline.groupSize1 skyline.groupSize2 skyline.groupSize3
1                 1                  6                  5                  5
  skyline.groupSize4 skyline.groupSize5 skyline.groupSize6 skyline.groupSize7
1                  5                  5                  5                  5
  skyline.groupSize8 skyline.groupSize9 skyline.groupSize10 kappa1 kappa2
1                  5                  5                   5      2      2
  frequencies1 frequencies2 frequencies3 frequencies4 alpha ucld.mean
1         0.25         0.25         0.25         0.25   0.5         1
  ucld.stdev meanRate coefficientOfVariation   covariance treeLikelihood
1  0.3333333 1.034891              0.3219494 -0.003427711      -53190.43
  branchRates   skyline
1   -471.7472 -4919.259
> tail(log2)
          state posterior     prior likelihood treeModel.rootHeight
9951   99500000 -5967.684 -3058.559  -2909.125             595.8296
9961   99600000 -6004.771 -3098.322  -2906.449             619.9427
9971   99700000 -5994.841 -3089.954  -2904.887             595.9836
9981   99800000 -6015.126 -3087.454  -2927.672             606.4756
9991   99900000 -5981.411 -3063.076  -2918.335             607.9006
10001 100000000 -5968.741 -3060.749  -2907.992             597.9549
      skyline.popSize1 skyline.popSize2 skyline.popSize3 skyline.popSize4
9951          361.8352        694.24670         18.76469        29.977883
9961          335.4437        537.63488        264.88775        69.617331
9971          276.1276        144.46751        219.75201       176.027739
9981          666.9793         31.14599         55.89602        19.070647
9991         1593.4224        442.79325        412.74945        45.899772
10001         396.5224        380.66288        266.51632         5.671572
      skyline.popSize5 skyline.popSize6 skyline.popSize7 skyline.popSize8
9951          58.21581       100.498314         2.789057         2.044690
9961          26.82285        29.638086       115.084115        56.900828
9971         470.33605       103.524940       101.480720        48.108153
9981          30.31274        36.727237        81.260858        60.588094
9991          29.93916         6.122359         3.891336         7.288111
10001         21.77147        26.249395        18.232590        49.522034
      skyline.popSize9 skyline.popSize10 skyline.groupSize1 skyline.groupSize2
9951          1.360791         1.7579874                 12                  2
9961         17.004628        28.4907909                  1                  7
9971          3.713107         0.6467948                  7                  1
9981          8.666943         3.8234620                 12                  3
9991         19.426274         9.1993900                  1                  8
10001        10.220474         7.2631493                 10                  4
      skyline.groupSize3 skyline.groupSize4 skyline.groupSize5
9951                   6                 11                 10
9961                   9                  2                 10
9971                   2                  2                  3
9981                   1                  8                  2
9991                   3                 19                  6
10001                  2                  5                  1
      skyline.groupSize6 skyline.groupSize7 skyline.groupSize8
9951                   4                  2                  2
9961                   1                  2                  6
9971                   1                 10                 21
9981                  12                  4                  5
9991                   2                  5                  2
10001                  6                  8                  5
      skyline.groupSize9 skyline.groupSize10    kappa1    kappa2 frequencies1
9951                   1                   1  4.030420  4.962840    0.3672668
9961                  11                   2  7.163937  5.049222    0.3726325
9971                   1                   3  5.671278  8.899290    0.3624253
9981                   1                   3  4.748575  5.603581    0.3476034
9991                   2                   3  6.171755  6.685988    0.3602663
10001                  7                   3 11.961481 10.736765    0.3570339
      frequencies2 frequencies3 frequencies4       alpha    ucld.mean
9951     0.1757972    0.2256664    0.2312696 0.019072310 3.677866e-05
9961     0.1493547    0.2263697    0.2516431 0.092555592 3.393505e-05
9971     0.1622222    0.2335000    0.2418525 0.003648157 3.632225e-05
9981     0.1751568    0.2446308    0.2326089 0.035697052 3.027913e-05
9991     0.1575727    0.2300344    0.2521265 0.090586958 4.573713e-05
10001    0.1723874    0.2238327    0.2467459 0.060721660 3.530217e-05
        ucld.stdev     meanRate coefficientOfVariation  covariance
9951  4.310939e-06 3.627788e-05           0.1110135261  0.06542983
9961  9.908434e-06 3.379512e-05           0.3027701603 -0.01740424
9971  5.772738e-06 3.654773e-05           0.1511855129 -0.03175182
9981  1.037722e-05 2.919618e-05           0.3283346036  0.04813477
9991  2.430427e-08 4.573139e-05           0.0004744421 -0.01534604
10001 8.963592e-06 3.541841e-05           0.2351818155 -0.07793663
      treeLikelihood branchRates   skyline
9951       -2909.125   -471.7472 -243.5211
9961       -2906.449   -471.7472 -271.4506
9971       -2904.887   -471.7472 -264.0880
9981       -2927.672   -471.7472 -267.9321
9991       -2918.335   -471.7472 -241.9591
10001      -2907.992   -471.7472 -236.1945
> write.table(log2, file='28467.thinned.log', sep='\t', quote=F, row.names=F)
>
jpalmer37 commented 5 years ago

Already implemented code into a new R script titled tree_thinning.R