Lineage inference • lineaGT

library(lineaGT)
#> Warning: replacing previous import 'cli::num_ansi_colors' by
#> 'crayon::num_ansi_colors' when loading 'VIBER'
#> Warning: replacing previous import 'cli::num_ansi_colors' by
#> 'crayon::num_ansi_colors' when loading 'easypar'
#> ✔ Loading ctree, 'Clone trees in cancer'. Support : <https://caravagn.github.io/ctree/>
#> Warning: replacing previous import 'crayon::%+%' by 'ggplot2::%+%' when loading
#> 'VIBER'
#> ✔ Loading VIBER, 'Variational inference for multivariate Binomial mixtures'. Support : <https://caravagn.github.io/VIBER/>
#> ✔ Loading lineaGT, 'Lineage inference from gene therapy'. Support : <https://caravagnalab.github.io/lineaGT/>
#> ! The 'lineagt-env' environment is already loaded!
library(magrittr)

The coverage dataset can be filtered calling the filter_dataset() function.

data(cov.df.example)
data(vaf.df.example)

cov.example.filt = cov.df.example %>%
  filter_dataset(min_cov=5, min_frac=0.05)
#> ℹ Filtering the input dataset with minimum coverage 5 and minimum clusters frac…
#> ✔ Filtering the input dataset with minimum coverage 5 and minimum clusters frac…
#> 

cov.example.filt
#> # A tibble: 264 × 4
#>    IS    timepoints lineage coverage
#>    <chr> <chr>      <chr>      <int>
#>  1 IS100 t1         l1             0
#>  2 IS100 t2         l1           418
#>  3 IS100 t1         l2             0
#>  4 IS100 t2         l2            74
#>  5 IS101 t1         l1           502
#>  6 IS101 t2         l1           186
#>  7 IS101 t1         l2            62
#>  8 IS101 t2         l2           640
#>  9 IS11  t1         l1           128
#> 10 IS11  t2         l1           196
#> # ℹ 254 more rows

Fitting the model

x = fit(
  cov.df = cov.example.filt,
  vaf.df = vaf.df.example,
  steps = 500,
  # n_runs = 1,
  k_interval = c(5, 15),
  timepoints_to_int = unlist(list("t1"=60, "t2"=150))
  )
#>  [36mℹ [39m Starting lineaGT model selection to retrieve the optimal number of clones
#>  [32m✔ [39m Starting lineaGT model selection to retrieve the optimal number of clones ...…
#> 
#>  [36mℹ [39m Fitting model to cluster ISs
#>  [32m✔ [39m Found 8 clones of ISs!
#> 
#>  [36mℹ [39m Fitting model to cluster mutations
#>  [36mℹ [39m Starting clustering of clone C0 mutations
#>  [44m [37m [ VIBER - variational fit ] 
#>  [39m [49m
#>  [36mℹ [39m Input n = 3, with k < 3. Dirichlet concentration  [32m [32mα = 1e-06 [32m [39m.
#>  [36mℹ [39m Starting clustering of clone C0 mutations
 [36mℹ [39m Beta (a_0, b_0) = (1, 1); q_i = prior. Optimise: ε = 1e-10 or 5000 steps, r = 10 starts.
#>  [36mℹ [39m Starting clustering of clone C0 mutations
#>  [32m✔ [39m  [1mVIBER fit [22m completed in 0.03 mins (status:  [32mconverged [39m)
#>  [36mℹ [39m Starting clustering of clone C0 mutations
#> ──  [30m [43m[ VIBER ] My VIBER model [49m [39m n = 3 (w = 4 dimensions). Fit with  [32mk = 3 [39m clusters. ─
#>  [36mℹ [39m Starting clustering of clone C0 mutations
• Clusters: π = 67% [ [33mC1 [39m] and 33% [ [33mC2 [39m], with π > 0.
#>  [36mℹ [39m Starting clustering of clone C0 mutations
• Binomials: θ = <0.09, 0.19, 0.01, 0> [ [33mC1 [39m] and <0.01, 0.36, 0.03, 0.4> [ [33mC2 [39m].
#>  [36mℹ [39m Starting clustering of clone C0 mutations
 [36mℹ [39m Score(s): ELBO = -1461.846. Fit  [32mconverged [39m in 6 steps, ε = 1e-10.
#>  [36mℹ [39m Starting clustering of clone C0 mutations
 [32m✔ [39m Reduced to k = 2 (from 3) selecting VIBER cluster(s) with π > 0.166666666666667, and Binomial p > 0 in w > 0 dimension(s).
#>  [36mℹ [39m Starting clustering of clone C0 mutations
 [32m✔ [39m Starting clustering of clone C0 mutations ... done
#> 
#>  [36mℹ [39m Fitting model to cluster mutations
 [36mℹ [39m Starting phylogeny inference of clone C0
#>  [44m [37m [ ctree ~ clone trees generator for C0 ] 
#>  [39m [49m
#>  [38;5;246m# A tibble: 3 × 8 [39m
#>   cluster   t1.l1 t2.l1  t1.l2 t2.l2 nMuts is.clonal is.driver
#>    [3m [38;5;246m<chr> [39m [23m      [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m   [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<lgl> [39m [23m      [3m [38;5;246m<lgl> [39m [23m    
#>  [38;5;250m1 [39m S1      0.085 [4m6 [24m  0.190 0      0         2 FALSE     FALSE    
#>  [38;5;250m2 [39m S2      0.006 [4m8 [24m [4m6 [24m 0.362 0.028 [4m6 [24m 0.396     1 FALSE     TRUE     
#>  [38;5;250m3 [39m C0      1       1     1      1         1 TRUE      FALSE
#>  [32m✔ [39m Trees per region  [32m [32m1, 2, 1, 1 [32m [39m
#>  [36mℹ [39m Starting phylogeny inference of clone C0
 [36mℹ [39m Total  [32m [32m2 [32m [39m tree structures - search is  [1mexahustive [22m
#>  [36mℹ [39m Starting phylogeny inference of clone C0
#>  [36mℹ [39m Starting phylogeny inference of clone C0
── Ranking trees 
#>  [36mℹ [39m Starting phylogeny inference of clone C0
 [32m✔ [39m  [32m [32m2 [32m [39m  trees with non-zero score, storing  [1m2 [22m
#>  [36mℹ [39m Starting phylogeny inference of clone C0
 [32m✔ [39m Starting phylogeny inference of clone C0 ... done
#> 
#>  [36mℹ [39m Fitting model to cluster mutations
 [36mℹ [39m Starting clustering of clone C1 mutations
#>  [44m [37m [ VIBER - variational fit ] 
#>  [39m [49m
#>  [36mℹ [39m Input n = 8, with k < 8. Dirichlet concentration  [32m [32mα = 1e-06 [32m [39m.
#>  [36mℹ [39m Starting clustering of clone C1 mutations
 [36mℹ [39m Beta (a_0, b_0) = (1, 1); q_i = prior. Optimise: ε = 1e-10 or 5000 steps, r = 10 starts.
#>  [36mℹ [39m Starting clustering of clone C1 mutations
#>  [32m✔ [39m  [1mVIBER fit [22m completed in 0.04 mins (status:  [32mconverged [39m)
#>  [36mℹ [39m Starting clustering of clone C1 mutations
#> ──  [30m [43m[ VIBER ] My VIBER model [49m [39m n = 8 (w = 4 dimensions). Fit with  [32mk = 8 [39m clusters. ─
#>  [36mℹ [39m Starting clustering of clone C1 mutations
• Clusters: π = 50% [ [33mC3 [39m], 25% [ [33mC8 [39m], 13% [ [33mC6 [39m], and 13% [ [33mC7 [39m], with π > 0.
#>  [36mℹ [39m Starting clustering of clone C1 mutations
• Binomials: θ = <0, 0.1, 0.01, 0.1> [ [33mC3 [39m], <0.18, 0, 0.01, 0> [ [33mC8 [39m], <0.2, 0.01,
#> 0.02, 0.23> [ [33mC6 [39m], and <0.43, 0.01, 0.02, 0.32> [ [33mC7 [39m].
#>  [36mℹ [39m Starting clustering of clone C1 mutations
 [36mℹ [39m Score(s): ELBO = -4630.119. Fit  [32mconverged [39m in 9 steps, ε = 1e-10.
#>  [36mℹ [39m Starting clustering of clone C1 mutations
 [32m✔ [39m Reduced to k = 4 (from 8) selecting VIBER cluster(s) with π > 0.0625, and Binomial p > 0 in w > 0 dimension(s).
#>  [36mℹ [39m Starting clustering of clone C1 mutations
 [32m✔ [39m Starting clustering of clone C1 mutations ... done
#> 
#>  [36mℹ [39m Fitting model to cluster mutations
 [36mℹ [39m Starting phylogeny inference of clone C1
#>  [44m [37m [ ctree ~ clone trees generator for C1 ] 
#>  [39m [49m
#>  [38;5;246m# A tibble: 5 × 8 [39m
#>   cluster    t1.l1   t2.l1   t1.l2  t2.l2 nMuts is.clonal is.driver
#>    [3m [38;5;246m<chr> [39m [23m       [3m [38;5;246m<dbl> [39m [23m    [3m [38;5;246m<dbl> [39m [23m    [3m [38;5;246m<dbl> [39m [23m   [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<lgl> [39m [23m      [3m [38;5;246m<lgl> [39m [23m    
#>  [38;5;250m1 [39m S1      0.000 [4m8 [24m [4m0 [24m [4m8 [24m 0.102   0.006 [4m1 [24m [4m9 [24m 0.098 [4m9 [24m     4 FALSE     TRUE     
#>  [38;5;250m2 [39m S2      0.198    0.005 [4m5 [24m [4m7 [24m 0.017 [4m0 [24m  0.225      1 FALSE     FALSE    
#>  [38;5;250m3 [39m S3      0.430    0.005 [4m6 [24m [4m0 [24m 0.017 [4m1 [24m  0.318      1 FALSE     FALSE    
#>  [38;5;250m4 [39m S4      0.184    0.002 [4m8 [24m [4m1 [24m 0       0          2 FALSE     FALSE    
#>  [38;5;250m5 [39m C1      1        1       1       1          1 TRUE      FALSE
#>  [32m✔ [39m Trees per region  [32m [32m6, 1, 2, 5 [32m [39m
#>  [36mℹ [39m Starting phylogeny inference of clone C1
 [36mℹ [39m Total  [32m [32m18 [32m [39m tree structures - search is  [1mexahustive [22m
#>  [36mℹ [39m Starting phylogeny inference of clone C1
#>  [36mℹ [39m Starting phylogeny inference of clone C1
── Ranking trees 
#>  [36mℹ [39m Starting phylogeny inference of clone C1
 [32m✔ [39m  [32m [32m18 [32m [39m  trees with non-zero score, storing  [1m18 [22m
#>  [36mℹ [39m Starting phylogeny inference of clone C1
 [32m✔ [39m Starting phylogeny inference of clone C1 ... done
#> 
#>  [36mℹ [39m Fitting model to cluster mutations
 [36mℹ [39m Starting clustering of clone C4 mutations
#>  [44m [37m [ VIBER - variational fit ] 
#>  [39m [49m
#>  [36mℹ [39m Input n = 6, with k < 6. Dirichlet concentration  [32m [32mα = 1e-06 [32m [39m.
#>  [36mℹ [39m Starting clustering of clone C4 mutations
 [36mℹ [39m Beta (a_0, b_0) = (1, 1); q_i = prior. Optimise: ε = 1e-10 or 5000 steps, r = 10 starts.
#>  [36mℹ [39m Starting clustering of clone C4 mutations
#>  [32m✔ [39m  [1mVIBER fit [22m completed in 0.04 mins (status:  [32mconverged [39m)
#>  [36mℹ [39m Starting clustering of clone C4 mutations
#> ──  [30m [43m[ VIBER ] My VIBER model [49m [39m n = 6 (w = 4 dimensions). Fit with  [32mk = 6 [39m clusters. ─
#>  [36mℹ [39m Starting clustering of clone C4 mutations
• Clusters: π = 33% [ [33mC3 [39m], 17% [ [33mC1 [39m], 17% [ [33mC4 [39m], 17% [ [33mC5 [39m], and 17% [ [33mC6 [39m], with π >
#> 0.
#>  [36mℹ [39m Starting clustering of clone C4 mutations
• Binomials: θ = <0.22, 0, 0.01, 0> [ [33mC3 [39m], <0.36, 0, 0.02, 0.28> [ [33mC1 [39m], <0, 0,
#> 0.02, 0.29> [ [33mC4 [39m], <0.19, 0.22, 0.3, 0.2> [ [33mC5 [39m], and <0, 0, 0.02, 0> [ [33mC6 [39m].
#>  [36mℹ [39m Starting clustering of clone C4 mutations
 [36mℹ [39m Score(s): ELBO = -3580.958. Fit  [32mconverged [39m in 6 steps, ε = 1e-10.
#>  [36mℹ [39m Starting clustering of clone C4 mutations
 [32m✔ [39m Reduced to k = 5 (from 6) selecting VIBER cluster(s) with π > 0.0833333333333333, and Binomial p > 0 in w > 0 dimension(s).
#>  [36mℹ [39m Starting clustering of clone C4 mutations
 [32m✔ [39m Starting clustering of clone C4 mutations ... done
#> 
#>  [36mℹ [39m Fitting model to cluster mutations
 [36mℹ [39m Starting phylogeny inference of clone C4
#>  [44m [37m [ ctree ~ clone trees generator for C4 ] 
#>  [39m [49m
#>  [38;5;246m# A tibble: 6 × 8 [39m
#>   cluster t1.l1   t2.l1  t1.l2 t2.l2 nMuts is.clonal is.driver
#>    [3m [38;5;246m<chr> [39m [23m    [3m [38;5;246m<dbl> [39m [23m    [3m [38;5;246m<dbl> [39m [23m   [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<lgl> [39m [23m      [3m [38;5;246m<lgl> [39m [23m    
#>  [38;5;250m1 [39m S1      0.358 0.004 [4m7 [24m [4m3 [24m 0.017 [4m0 [24m 0.275     1 FALSE     FALSE    
#>  [38;5;250m2 [39m S2      0.219 0.002 [4m3 [24m [4m9 [24m 0      0         2 FALSE     FALSE    
#>  [38;5;250m3 [39m S3      0     0       0.018 [4m6 [24m 0.292     1 FALSE     FALSE    
#>  [38;5;250m4 [39m S4      0.192 0.222   0.296  0.198     1 FALSE     FALSE    
#>  [38;5;250m5 [39m S5      0     0       0      0         1 FALSE     TRUE     
#>  [38;5;250m6 [39m C4      1     1       1      1         1 TRUE      FALSE
#>  [32m✔ [39m Trees per region  [32m [32m5, 1, 6, 5 [32m [39m
#>  [36mℹ [39m Starting phylogeny inference of clone C4
 [36mℹ [39m Total  [32m [32m48 [32m [39m tree structures - search is  [1mexahustive [22m
#>  [36mℹ [39m Starting phylogeny inference of clone C4
 [31m✖ [39m Starting phylogeny inference of clone C4 ... failed
#> 
#>  [36mℹ [39m Fitting model to cluster mutations
#> <subscriptOutOfBoundsError in model[var, ]: subscript out of bounds>
#>  [36mℹ [39m Starting clustering of clone C7 mutations
#>  [44m [37m [ VIBER - variational fit ] 
#>  [39m [49m
#>  [36mℹ [39m Input n = 2, with k < 2. Dirichlet concentration  [32m [32mα = 1e-06 [32m [39m.
#>  [36mℹ [39m Starting clustering of clone C7 mutations
 [36mℹ [39m Beta (a_0, b_0) = (1, 1); q_i = prior. Optimise: ε = 1e-10 or 5000 steps, r = 10 starts.
#>  [36mℹ [39m Starting clustering of clone C7 mutations
#>  [32m✔ [39m  [1mVIBER fit [22m completed in 0.03 mins (status:  [32mconverged [39m)
#>  [36mℹ [39m Starting clustering of clone C7 mutations
#> ──  [30m [43m[ VIBER ] My VIBER model [49m [39m n = 2 (w = 4 dimensions). Fit with  [32mk = 2 [39m clusters. ─
#>  [36mℹ [39m Starting clustering of clone C7 mutations
• Clusters: π = 50% [ [33mC1 [39m] and 50% [ [33mC2 [39m], with π > 0.
#>  [36mℹ [39m Starting clustering of clone C7 mutations
• Binomials: θ = <0.4, 0, 0.31, 0.35> [ [33mC1 [39m] and <0.01, 0.11, 0, 0> [ [33mC2 [39m].
#>  [36mℹ [39m Starting clustering of clone C7 mutations
 [36mℹ [39m Score(s): ELBO = -1584.479. Fit  [32mconverged [39m in 5 steps, ε = 1e-10.
#>  [36mℹ [39m Starting clustering of clone C7 mutations
 [32m✔ [39m Starting clustering of clone C7 mutations ... done
#> 
#>  [36mℹ [39m Fitting model to cluster mutations
 [36mℹ [39m Starting phylogeny inference of clone C7
#>  [44m [37m [ ctree ~ clone trees generator for C7 ] 
#>  [39m [49m
#>  [38;5;246m# A tibble: 3 × 8 [39m
#>   cluster  t1.l1   t2.l1 t1.l2 t2.l2 nMuts is.clonal is.driver
#>    [3m [38;5;246m<chr> [39m [23m     [3m [38;5;246m<dbl> [39m [23m    [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<lgl> [39m [23m      [3m [38;5;246m<lgl> [39m [23m    
#>  [38;5;250m1 [39m S1      0.396  0.002 [4m3 [24m [4m4 [24m 0.308 0.348     1 FALSE     TRUE     
#>  [38;5;250m2 [39m S2      0.010 [4m4 [24m 0.112   0     0         1 FALSE     FALSE    
#>  [38;5;250m3 [39m C7      1      1       1     1         1 TRUE      FALSE
#>  [32m✔ [39m Trees per region  [32m [32m2, 1, 1, 1 [32m [39m
#>  [36mℹ [39m Starting phylogeny inference of clone C7
 [36mℹ [39m Total  [32m [32m2 [32m [39m tree structures - search is  [1mexahustive [22m
#>  [36mℹ [39m Starting phylogeny inference of clone C7
#>  [36mℹ [39m Starting phylogeny inference of clone C7
── Ranking trees 
#>  [36mℹ [39m Starting phylogeny inference of clone C7
 [32m✔ [39m  [32m [32m2 [32m [39m  trees with non-zero score, storing  [1m2 [22m
#>  [36mℹ [39m Starting phylogeny inference of clone C7
 [32m✔ [39m Starting phylogeny inference of clone C7 ... done
#> 
#>  [36mℹ [39m Fitting model to cluster mutations
 [32m✔ [39m Fitting model to cluster mutations ... done
#> 
#>  [36mℹ [39m Fitting model to estimate population growth rates
#>  [36mℹ [39m Starting growth models inference of clone C0
#>  [32m✔ [39m Starting growth models inference of clone C0 ... done
#> 
#>  [36mℹ [39m Fitting model to estimate population growth rates
 [36mℹ [39m Starting growth models inference of clone C1
#>  [32m✔ [39m Starting growth models inference of clone C1 ... done
#> 
#>  [36mℹ [39m Fitting model to estimate population growth rates
 [36mℹ [39m Starting growth models inference of clone C2
#>  [32m✔ [39m Starting growth models inference of clone C2 ... done
#> 
#>  [36mℹ [39m Fitting model to estimate population growth rates
 [36mℹ [39m Starting growth models inference of clone C3
#>  [32m✔ [39m Starting growth models inference of clone C3 ... done
#> 
#>  [36mℹ [39m Fitting model to estimate population growth rates
 [36mℹ [39m Starting growth models inference of clone C4
#>  [32m✔ [39m Starting growth models inference of clone C4 ... done
#> 
#>  [36mℹ [39m Fitting model to estimate population growth rates
 [36mℹ [39m Starting growth models inference of clone C5
#>  [32m✔ [39m Starting growth models inference of clone C5 ... done
#> 
#>  [36mℹ [39m Fitting model to estimate population growth rates
 [36mℹ [39m Starting growth models inference of clone C6
#>  [32m✔ [39m Starting growth models inference of clone C6 ... done
#> 
#>  [36mℹ [39m Fitting model to estimate population growth rates
 [36mℹ [39m Starting growth models inference of clone C7
#>  [32m✔ [39m Starting growth models inference of clone C7 ... done
#> 
#>  [36mℹ [39m Fitting model to estimate population growth rates
 [32m✔ [39m Fitting model to estimate population growth rates ... done

Printing the fitted object information regarding the data:

lineages and timpoints present in the data,
number of integration sites,
number of inferred clones of ISs, estimated via model selection on the input range of number of clusters,
for each clone, the number of assigned ISs and the mean coverage, per timepoint and lineage.

data(x.example)
x.example
#> ── [ lineaGT ]  ──── Python: /usr/share/miniconda/envs/lineagt-env/bin/python ──
#> → Lineages: l1 and l2.
#> → Timepoints: t1 and t2.
#> → Number of Insertion Sites: 66.
#> 
#> ── Optimal IS model with k = 8.
#> 
#>     C4 (19 ISs) : l1 [285, 209]; l2 [ 51, 492] 
#>     C1 (15 ISs) : l1 [245, 177]; l2 [ 23, 289] 
#>      C0 (6 ISs) : l1 [145, 240]; l2 [ 32, 373] 
#>      C2 (6 ISs) : l1 [  1, 547]; l2 [  1, 388] 
#>      C3 (6 ISs) : l1 [ 92, 109]; l2 [245, 751] 
#>      C5 (6 ISs) : l1 [  0, 551]; l2 [  1, 828] 
#>      C6 (4 ISs) : l1 [330,  16]; l2 [ 17,  38] 
#>      C7 (4 ISs) : l1 [  0, 426]; l2 [  1, 198]