cellTree with gibbs
File: /home/rcannood/Workspace/dynverse/dynbenchmark//derived/04-method_characterisation/method_testing/suite/celltree_gibbs/r2gridengine/20180803_183119_celltree_gibbs_qr7Pj7sr3v/log/log.1.e.txt
File: /home/rcannood/Workspace/dynverse/dynbenchmark//derived/04-method_characterisation/method_testing/suite/celltree_gibbs/r2gridengine/20180803_183119_celltree_gibbs_qr7Pj7sr3v/log/log.2.e.txt
File: /home/rcannood/Workspace/dynverse/dynbenchmark//derived/04-method_characterisation/method_testing/suite/celltree_gibbs/r2gridengine/20180803_183119_celltree_gibbs_qr7Pj7sr3v/log/log.3.e.txt
File: /home/rcannood/Workspace/dynverse/dynbenchmark//derived/04-method_characterisation/method_testing/suite/celltree_gibbs/r2gridengine/20180803_183119_celltree_gibbs_qr7Pj7sr3v/log/log.4.e.txt
Input saved to /scratch/irc/personal/robrechtc/tmp//RtmpOoqdaC/input:
data.rds
params.json
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Attaching package: ‘purrr’
The following object is masked from ‘package:jsonlite’:
flatten
Loading required namespace: cellTree
Computing LDA model using: Gibbs
Filtering out rows with standard deviation < 0.5 (101 -> 101)...
Loading required namespace: topicmodels
K = 4; V = 101; M = 95
Sampling 40 iterations!
Iteration 10 ...
Iteration 20 ...
Iteration 30 ...
Iteration 40 ...
Gibbs sampling completed!
Model fit for k = 4 topics
Using rooting method: longest.path
Using root vertex: 16
Adding branch #1:
[1] 11 70 74 35 62 92 83 75 25 10 18 91 28 2 34 51 82 44 50 81 5 12 95 19 3
[26] 40 13 27 31
Using branch width: 0.453 (width.scale.factor: 1.5)
Outliers: 0
Total number of branches: 1 (forks: 0)
Backbone fork merge (width: 0.453): 30 -> 30
Ranking all cells...
Computing tree layout...output saved in /scratch/irc/personal/robrechtc/tmp//RtmpOoqdaC/output:
output.rds
Part of 