Closed luqingan closed 2 years ago
Hi @luqingan
Thanks for submitting your package. We are taking a quick look at it and you will hear back from us soon.
The DESCRIPTION file for this package is:
Package: IMPACC
Type: Package
Title: Fast and Interpretable Consensus Clustering via Minipatch Learning
Version: 0.99.0
Authors@R: person("Luqin", "Gan", email = "glq.gan@gmail.com",role = c("aut", "cre"))
Description: This package provides powerful methodologies for consensus clustering using minipatch learning with random or adaptive schemes. The methods provide interpretability in terms of feature importance and are particularly applicable to sparse, high-dimensional data sets common in bioinformatics.
License: Artistic-2.0
Encoding: UTF-8
LazyData: false
Imports: mvtnorm, matrixStats, stats, cluster,SNFtool,knitr,nnet
LazyDataCompression:xz
Depends: R(>= 4.1.0)
VignetteBuilder: knitr
biocViews: SingleCell, Software, Clustering, RNASeq, DifferentialExpression,KEGG, Regression, FeatureExtraction
The vignette has long list of optimization trace. Should suppress. You should use SummarizedExperiment or other integrative containers in Bioconductor. Please revise. Do not use a list like
library(IMPACC)
data(yan)
yan$raw[1:3, 1:3]
## GSM896803 GSM896804 GSM896805
## A1BG 8 6 2
## A1BGAS 24 18 4
## A1CF 1306 1072 828
yan$sc_cnt[1:3, 1:3]
## GSM896803 GSM896804 GSM896805
## A1BG 3.169925 2.807355 1.584963
## A1BGAS 4.643856 4.247928 2.321928
## A1CF 10.352043 10.067434 9.695228
head(yan$sc_label)
## [1] "Oocyte " "Oocyte " "Oocyte " "Zygote " "Zygote " "Zygote "
Notice the trailing blanks in the cell type strings. Intended? Accidental?
Does the vignette reader need to see
impacc_multinomial = IMPACC(d=yan$sc_cnt,K = K,reps = 1,feature_evaluation = 'multinomial', verbose=FALSE)
## Burn-in stage
## # weights: 3 (2 variable)
## initial value 15.249238
## final value 12.759554
## converged
## # weights: 3 (2 variable)
## initial value 15.249238
## iter 10 value 10.231735
## iter 10 value 10.231735
## iter 10 value 10.231735
## final value 10.231735
## converged
## # weights: 3 (2 variable)
## initial value 15.249238
## final value 12.092815
## converged
## # weights: 3 (2 variable)
## initial value 15.249238
## final value 10.908304
## converged
## # weights: 3 (2 variable)
## initial value 15.249238
## final value 9.906160
## converged
## # weights: 3 (2 variable)
## initial value 15.249238
## iter 10 value 0.035134
## iter 20 value 0.005524
## iter 30 value 0.001722
## iter 40 value 0.000603
## iter 50 value 0.000226
## final value 0.000098
## converged
## # weights: 3 (2 variable)
## initial value 15.249238
## final value 11.907750
## converged
## # weights: 3 (2 variable)
## initial value 15.249238
## iter 10 value 4.080592
## final value 4.078276
## converged
## # weights: 3 (2 variable)
## initial value 15.249238
## iter 10 value 3.546823
## iter 20 value 3.460980
## iter 30 value 3.458233
## final value 3.458233
## converged
## # weights: 3 (2 variable)
## initial value 15.249238
## iter 10 value 1.243145
## iter 20 value 0.727943
## iter 30 value 0.586823
## iter 40 value 0.462228
## iter 50 value 0.368125
## iter 60 value 0.306748
## iter 70 value 0.264036
## iter 80 value 0.232274
## iter 90 value 0.207367
## iter 100 value 0.187116
## final value 0.187116
## stopped after 100 iterations
## # weights: 3 (2 variable)
## initial value 15.249238
## final value 12.837630
## converged
## # weights: 3 (2 variable)
## initial value 15.249238
## iter 10 value 10.021199
## iter 20 value 10.010808
## iter 30 value 10.009744
## iter 40 value 10.009324
## iter 50 value 10.009074
## iter 60 value 10.008905
## iter 70 value 10.008784
## iter 80 value 10.008693
## iter 90 value 10.008622
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