diff -Nru r-bioc-mofa-1.2.0+dfsg/debian/changelog r-bioc-mofa-1.4.0+dfsg/debian/changelog --- r-bioc-mofa-1.2.0+dfsg/debian/changelog 2020-03-22 07:09:15.000000000 +0000 +++ r-bioc-mofa-1.4.0+dfsg/debian/changelog 2020-05-31 06:09:04.000000000 +0000 @@ -1,3 +1,16 @@ +r-bioc-mofa (1.4.0+dfsg-1build1) groovy; urgency=medium + + * No-change rebuild against r-api-4.0 + + -- Steve Langasek Sun, 31 May 2020 06:09:04 +0000 + +r-bioc-mofa (1.4.0+dfsg-1) unstable; urgency=medium + + * Team upload. + * New upstream version + + -- Dylan Aïssi Fri, 22 May 2020 09:36:50 +0200 + r-bioc-mofa (1.2.0+dfsg-2) unstable; urgency=medium * Team upload. diff -Nru r-bioc-mofa-1.2.0+dfsg/debian/control r-bioc-mofa-1.4.0+dfsg/debian/control --- r-bioc-mofa-1.2.0+dfsg/debian/control 2020-03-22 07:09:15.000000000 +0000 +++ r-bioc-mofa-1.4.0+dfsg/debian/control 2020-05-31 06:09:04.000000000 +0000 @@ -1,5 +1,6 @@ Source: r-bioc-mofa -Maintainer: Debian R Packages Maintainers +Maintainer: Ubuntu Developers +XSBC-Original-Maintainer: Debian R Packages Maintainers Uploaders: Steffen Moeller Section: gnu-r Testsuite: autopkgtest-pkg-r @@ -43,6 +44,7 @@ Package: r-bioc-mofa Architecture: all Depends: ${R:Depends}, + ${shlibs:Depends}, ${misc:Depends}, python3-mofapy Recommends: ${R:Recommends} diff -Nru r-bioc-mofa-1.2.0+dfsg/debian/r-bioc-mofa.install r-bioc-mofa-1.4.0+dfsg/debian/r-bioc-mofa.install --- r-bioc-mofa-1.2.0+dfsg/debian/r-bioc-mofa.install 1970-01-01 00:00:00.000000000 +0000 +++ r-bioc-mofa-1.4.0+dfsg/debian/r-bioc-mofa.install 2020-05-22 07:36:50.000000000 +0000 @@ -0,0 +1 @@ +tmp-install/* / diff -Nru r-bioc-mofa-1.2.0+dfsg/debian/rules r-bioc-mofa-1.4.0+dfsg/debian/rules --- r-bioc-mofa-1.2.0+dfsg/debian/rules 2020-03-22 07:09:15.000000000 +0000 +++ r-bioc-mofa-1.4.0+dfsg/debian/rules 2020-05-22 07:36:50.000000000 +0000 @@ -7,11 +7,11 @@ dh $@ --with python3 --buildsystem pybuild override_dh_auto_install: - dh_auto_install - mkdir -p $(CURDIR)/debian/r-bioc-mofa/usr/lib/R/site-library - xvfb-run --auto-servernum --server-num=20 -s "-screen 0 1024x768x24 -ac +extension GLX +render -noreset" R CMD INSTALL -l $(CURDIR)/debian/r-bioc-mofa/usr/lib/R/site-library --clean . "--built-timestamp='$(shell date -R)'" + dh_auto_install -O--buildsystem=R --sourcedirectory=$(CURDIR) find $(CURDIR)/debian -name "*.Rmd" | xargs -r chmod -x + mv debian/tmp tmp-install + dh_auto_install override_dh_auto_clean: dh_auto_clean - rm -rf debian/python3-mofa.substvars debian/r-bioc-mofa.install mofapy.egg-info/ + rm -rf debian/python3-mofa.substvars mofapy.egg-info/ diff -Nru r-bioc-mofa-1.2.0+dfsg/DESCRIPTION r-bioc-mofa-1.4.0+dfsg/DESCRIPTION --- r-bioc-mofa-1.2.0+dfsg/DESCRIPTION 2019-10-30 03:36:40.000000000 +0000 +++ r-bioc-mofa-1.4.0+dfsg/DESCRIPTION 2020-04-28 04:03:11.000000000 +0000 @@ -1,8 +1,8 @@ Package: MOFA Type: Package Title: Multi-Omics Factor Analysis (MOFA) -Version: 1.2.0 -Maintainer: Britta Velten +Version: 1.4.0 +Maintainer: Britta Velten Author: Ricard Argelaguet, Britta Velten, Damien Arnol, Florian Buettner, Wolfgang Huber, Oliver Stegle Date: 2018-06-26 License: LGPL-3 | file LICENSE @@ -22,8 +22,8 @@ SystemRequirements: Python (>=2.7.0), numpy, pandas, h5py, scipy, sklearn, mofapy git_url: https://git.bioconductor.org/packages/MOFA -git_branch: RELEASE_3_10 -git_last_commit: cbc3ced -git_last_commit_date: 2019-10-29 -Date/Publication: 2019-10-29 -Packaged: 2019-10-30 03:36:40 UTC; biocbuild +git_branch: RELEASE_3_11 +git_last_commit: baf2eef +git_last_commit_date: 2020-04-27 +Date/Publication: 2020-04-27 +Packaged: 2020-04-28 04:03:11 UTC; biocbuild diff -Nru r-bioc-mofa-1.2.0+dfsg/inst/doc/MOFA_example_CLL.R r-bioc-mofa-1.4.0+dfsg/inst/doc/MOFA_example_CLL.R --- r-bioc-mofa-1.2.0+dfsg/inst/doc/MOFA_example_CLL.R 2019-10-30 03:35:31.000000000 +0000 +++ r-bioc-mofa-1.4.0+dfsg/inst/doc/MOFA_example_CLL.R 2020-04-28 04:02:25.000000000 +0000 @@ -1,14 +1,14 @@ -## ---- warning=FALSE, message=FALSE----------------------------------------- +## ---- warning=FALSE, message=FALSE-------------------------------------------- library(MultiAssayExperiment) library(MOFA) library(MOFAdata) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- data("CLL_data") MOFAobject <- createMOFAobject(CLL_data) MOFAobject -## ---- warning=FALSE, message=FALSE----------------------------------------- +## ---- warning=FALSE, message=FALSE-------------------------------------------- # Load data # import list with mRNA, Methylation, Drug Response and Mutation data. @@ -32,19 +32,19 @@ MOFAobject -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotDataOverview(MOFAobject) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- DataOptions <- getDefaultDataOptions() DataOptions -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- ModelOptions <- getDefaultModelOptions(MOFAobject) ModelOptions$numFactors <- 25 ModelOptions -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- TrainOptions <- getDefaultTrainOptions() # Automatically drop factors that explain less than 2% of variance in all omics @@ -54,7 +54,7 @@ TrainOptions -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- MOFAobject <- prepareMOFA( MOFAobject, DataOptions = DataOptions, @@ -62,24 +62,24 @@ TrainOptions = TrainOptions ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- # MOFAobject <- regressCovariates( # object = MOFAobject, # views = c("Drugs","Methylation","mRNA"), # covariates = MOFAobject@InputData$Gender # ) -## ---- eval=FALSE----------------------------------------------------------- +## ---- eval=FALSE-------------------------------------------------------------- # MOFAobject <- runMOFA(MOFAobject) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- # Loading an existing trained model filepath <- system.file("extdata", "CLL_model.hdf5", package = "MOFAdata") MOFAobject <- loadModel(filepath, MOFAobject) MOFAobject -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- # Calculate the variance explained (R2) per factor in each view r2 <- calculateVarianceExplained(MOFAobject) r2$R2Total @@ -90,7 +90,7 @@ # Plot it plotVarianceExplained(MOFAobject) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotWeightsHeatmap( MOFAobject, view = "Mutations", @@ -98,7 +98,7 @@ show_colnames = FALSE ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotWeights( MOFAobject, view = "Mutations", @@ -116,21 +116,21 @@ ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotTopWeights( MOFAobject, view="Mutations", factor=1 ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotTopWeights( MOFAobject, view = "mRNA", factor = 1 ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotDataHeatmap( MOFAobject, view = "mRNA", @@ -139,7 +139,7 @@ show_rownames = FALSE ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- # Load reactome annotations data("reactomeGS") # binary matrix with feature sets in rows and features in columns @@ -151,10 +151,10 @@ alpha = 0.01 ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotEnrichmentBars(gsea, alpha=0.01) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- interestingFactors <- 4:5 fseaplots <- lapply(interestingFactors, function(factor) { @@ -170,7 +170,7 @@ cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]], ncol = 1, labels = paste("Factor", interestingFactors)) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotFactorScatter( MOFAobject, factors = 1:2, @@ -178,21 +178,21 @@ shape_by = "trisomy12" # shape by the trisomy12 values that are part of the training data ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotFactorScatters( MOFAobject, factors = 1:3, color_by = "IGHV" ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotFactorBeeswarm( MOFAobject, factors = 1, color_by = "IGHV" ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- MOFAweights <- getWeights( MOFAobject, views = "all", @@ -201,7 +201,7 @@ ) head(MOFAweights) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- MOFAfactors <- getFactors( MOFAobject, factors = c(1,2), @@ -209,7 +209,7 @@ ) head(MOFAfactors) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- MOFAtrainData <- getTrainData( MOFAobject, as.data.frame = TRUE, @@ -217,7 +217,7 @@ ) head(MOFAtrainData) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- predictedDrugs <- predict( MOFAobject, view = "Drugs", @@ -236,7 +236,7 @@ cluster_rows = FALSE, cluster_cols = FALSE, show_rownames = FALSE, show_colnames = FALSE) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- MOFAobject <- impute(MOFAobject) imputedDrugs <- getImputedData(MOFAobject, view="Drugs")[[1]] @@ -250,7 +250,7 @@ cluster_rows = FALSE, cluster_cols = FALSE, show_rownames = FALSE, show_colnames = FALSE) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- set.seed(1234) clusters <- clusterSamples( MOFAobject, @@ -264,6 +264,6 @@ color_by = clusters ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- sessionInfo() diff -Nru r-bioc-mofa-1.2.0+dfsg/inst/doc/MOFA_example_scMT.R r-bioc-mofa-1.4.0+dfsg/inst/doc/MOFA_example_scMT.R --- r-bioc-mofa-1.2.0+dfsg/inst/doc/MOFA_example_scMT.R 2019-10-30 03:36:05.000000000 +0000 +++ r-bioc-mofa-1.4.0+dfsg/inst/doc/MOFA_example_scMT.R 2020-04-28 04:02:45.000000000 +0000 @@ -1,29 +1,29 @@ -## ---- warning=FALSE, message=FALSE----------------------------------------- +## ---- warning=FALSE, message=FALSE-------------------------------------------- library(MultiAssayExperiment) library(MOFA) library(MOFAdata) library(ggplot2) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- data("scMT_data") scMT_data -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- MOFAobject <- createMOFAobject(scMT_data) MOFAobject -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotDataOverview(MOFAobject, colors=c("#31A354","#377EB8","#377EB8","#377EB8")) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- DataOptions <- getDefaultDataOptions() DataOptions -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- ModelOptions <- getDefaultModelOptions(MOFAobject) ModelOptions -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- TrainOptions <- getDefaultTrainOptions() TrainOptions$seed <- 2018 @@ -32,7 +32,7 @@ TrainOptions -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- MOFAobject <- prepareMOFA( MOFAobject, DataOptions = DataOptions, @@ -40,15 +40,15 @@ TrainOptions = TrainOptions ) -## ---- eval=FALSE----------------------------------------------------------- +## ---- eval=FALSE-------------------------------------------------------------- # MOFAobject <- runMOFA(MOFAobject) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- filepath <- system.file("extdata", "scMT_model.hdf5", package = "MOFAdata") MOFAobject <- loadModel(filepath, MOFAobject) MOFAobject -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- # Calculate the variance explained (R2) per factor in each view r2 <- calculateVarianceExplained(MOFAobject) r2$R2Total @@ -59,7 +59,7 @@ # Plot it plotVarianceExplained(MOFAobject) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- # Plot all weights and highlight specific gene markers plotWeights( object = MOFAobject, @@ -79,7 +79,7 @@ nfeatures = 10 ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- # Add metadata to the plot factor1 <- sort(getFactors(MOFAobject,"LF1")[,1]) order_samples <- names(factor1) @@ -99,7 +99,7 @@ cluster_cols = FALSE, annotation_col=df # pheatmap options ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotWeights( object = MOFAobject, view = "Met Enhancers", @@ -109,7 +109,7 @@ scale = FALSE ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotDataHeatmap( object = MOFAobject, view = "Met Enhancers", @@ -121,7 +121,7 @@ annotation_col=df # pheatmap options ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- # Plot all weights and highlight specific gene markers plotWeights( object = MOFAobject, @@ -142,7 +142,7 @@ nfeatures = 10 ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotWeights( object = MOFAobject, view = "Met Enhancers", @@ -152,7 +152,7 @@ scale = FALSE ) -## ---- message=FALSE-------------------------------------------------------- +## ---- message=FALSE----------------------------------------------------------- factor2 <- sort(getFactors(MOFAobject,"LF2")[,1]) order_samples <- names(factor2) df <- data.frame( @@ -171,7 +171,7 @@ annotation_col=df # pheatmap options ) -## ---- message=FALSE-------------------------------------------------------- +## ---- message=FALSE----------------------------------------------------------- p <- plotFactorScatter( object = MOFAobject, factors=1:2, @@ -179,14 +179,14 @@ p + scale_color_manual(values=c("lightsalmon","orangered3")) -## ---- message=FALSE-------------------------------------------------------- +## ---- message=FALSE----------------------------------------------------------- plotFactorBeeswarm( object = MOFAobject, factors = 3, color_by = "cellular_detection_rate" ) -## ---- message=FALSE-------------------------------------------------------- +## ---- message=FALSE----------------------------------------------------------- cdr <- colMeans(getTrainData(MOFAobject)$`RNA expression`>0,na.rm=TRUE) factor3 <- getFactors(MOFAobject, factors=3) @@ -198,14 +198,14 @@ stat_smooth(method="lm") + theme_bw() -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- MOFAobject <- impute(MOFAobject) nonImputedMethylation <- getTrainData(MOFAobject, view="Met CpG Islands")[[1]] imputedMethylation <- getImputedData(MOFAobject, view="Met CpG Islands")[[1]] -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- # non-imputed data pheatmap::pheatmap(nonImputedMethylation[1:100,1:20], cluster_rows = FALSE, cluster_cols = FALSE, @@ -216,7 +216,7 @@ cluster_rows = FALSE, cluster_cols = FALSE, show_rownames = FALSE, show_colnames = FALSE) -## ---- message=FALSE-------------------------------------------------------- +## ---- message=FALSE----------------------------------------------------------- # kmeans clustering with K=3 using Factor 1 set.seed(1234) clusters <- clusterSamples(MOFAobject, k=3, factors=1) @@ -230,6 +230,6 @@ shape_by = "culture" ) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- sessionInfo() diff -Nru r-bioc-mofa-1.2.0+dfsg/inst/doc/MOFA_example_simulated.R r-bioc-mofa-1.4.0+dfsg/inst/doc/MOFA_example_simulated.R --- r-bioc-mofa-1.2.0+dfsg/inst/doc/MOFA_example_simulated.R 2019-10-30 03:36:39.000000000 +0000 +++ r-bioc-mofa-1.4.0+dfsg/inst/doc/MOFA_example_simulated.R 2020-04-28 04:03:10.000000000 +0000 @@ -1,20 +1,20 @@ -## ---- message=FALSE-------------------------------------------------------- +## ---- message=FALSE----------------------------------------------------------- library(MOFA) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- set.seed(1234) data <- makeExampleData() MOFAobject <- createMOFAobject(data) MOFAobject -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- TrainOptions <- getDefaultTrainOptions() ModelOptions <- getDefaultModelOptions(MOFAobject) DataOptions <- getDefaultDataOptions() TrainOptions$DropFactorThreshold <- 0.01 -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- n_inits <- 3 MOFAlist <- lapply(seq_len(n_inits), function(it) { @@ -30,19 +30,19 @@ runMOFA(MOFAobject) }) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- compareModels(MOFAlist) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- compareFactors(MOFAlist) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- MOFAobject <- selectModel(MOFAlist, plotit = FALSE) MOFAobject -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- plotVarianceExplained(MOFAobject) -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- sessionInfo() diff -Nru r-bioc-mofa-1.2.0+dfsg/inst/doc/MOFA.R r-bioc-mofa-1.4.0+dfsg/inst/doc/MOFA.R --- r-bioc-mofa-1.2.0+dfsg/inst/doc/MOFA.R 2019-10-30 03:34:55.000000000 +0000 +++ r-bioc-mofa-1.4.0+dfsg/inst/doc/MOFA.R 2020-04-28 04:01:53.000000000 +0000 @@ -1,20 +1,20 @@ -## ---- eval = FALSE--------------------------------------------------------- +## ---- eval = FALSE------------------------------------------------------------ # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install("MOFA", version = "3.9") -## ---- eval = FALSE--------------------------------------------------------- +## ---- eval = FALSE------------------------------------------------------------ # devtools::install_github("bioFAM/MOFA", build_opts = c("--no-resave-data")) -## ---- eval = FALSE--------------------------------------------------------- +## ---- eval = FALSE------------------------------------------------------------ # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install("MOFAdata", version = "3.9") -## ---- eval = FALSE--------------------------------------------------------- +## ---- eval = FALSE------------------------------------------------------------ # devtools::install_github("bioFAM/MOFAdata", build_opts = c("--no-resave-data")) -## ---- eval = FALSE--------------------------------------------------------- +## ---- eval = FALSE------------------------------------------------------------ # library(reticulate) # # # Using a specific python binary @@ -26,28 +26,28 @@ # # Using a virtual environment called "r-reticulate" # use_virtualenv("r-reticulate", required = TRUE) -## ---- eval=FALSE----------------------------------------------------------- +## ---- eval=FALSE-------------------------------------------------------------- # library(MOFA) # library(MOFAdata) -## ---- eval = FALSE--------------------------------------------------------- +## ---- eval = FALSE------------------------------------------------------------ # vignette("MOFA_example_simulated") -## ---- eval = FALSE--------------------------------------------------------- +## ---- eval = FALSE------------------------------------------------------------ # vignette("MOFA_example_CLL") -## ---- eval=FALSE----------------------------------------------------------- +## ---- eval=FALSE-------------------------------------------------------------- # vignette("MOFA_example_scMT") -## ---- eval = FALSE--------------------------------------------------------- +## ---- eval = FALSE------------------------------------------------------------ # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install(c("MultiAssayExperiment", "pcaMethods")) -## ---- eval = FALSE--------------------------------------------------------- +## ---- eval = FALSE------------------------------------------------------------ # library(reticulate) # use_python("YOUR_PYTHON_PATH", required=TRUE) # fill in YOUR_PYTHON_PATH -## -------------------------------------------------------------------------- +## ----------------------------------------------------------------------------- sessionInfo() diff -Nru r-bioc-mofa-1.2.0+dfsg/R/prepareMOFA.R r-bioc-mofa-1.4.0+dfsg/R/prepareMOFA.R --- r-bioc-mofa-1.2.0+dfsg/R/prepareMOFA.R 2019-10-29 20:42:12.000000000 +0000 +++ r-bioc-mofa-1.4.0+dfsg/R/prepareMOFA.R 2020-04-27 21:02:30.000000000 +0000 @@ -307,7 +307,7 @@ # Define default model options ModelOptions <- list( likelihood = likelihood, # (character vector) likelihood per view [gaussian/bernoulli/poisson] - numFactors = ceiling(nsamples/2), # (numeric) initial number of latent factors + numFactors = 10, # (numeric) initial number of latent factors sparsity = TRUE # (logical) use feature-wise sparsity? ) diff -Nru r-bioc-mofa-1.2.0+dfsg/README.md r-bioc-mofa-1.4.0+dfsg/README.md --- r-bioc-mofa-1.2.0+dfsg/README.md 2019-10-29 20:42:12.000000000 +0000 +++ r-bioc-mofa-1.4.0+dfsg/README.md 2020-04-27 21:02:30.000000000 +0000 @@ -17,7 +17,7 @@ - 10/01/2019 Python package uploaded to PyPy - 21/06/2018 Beta version released - 20/06/2018 Paper published: http://msb.embopress.org/content/14/6/e8124 -- 10/11/2017 We created a Slack group to provide personalised help on running and interpreting MOFA, [this is the link](https://join.slack.com/t/mofahelp/shared_invite/enQtMjcxNzM3OTE3NjcxLTkyZmE5YzNiMDc4OTkxYWExYWNlZTRhMWI2OWNkNzhmYmNlZjJiMjA4MjNiYjI2YTc4NjExNzU2ZTZiYzQyNjY) +- 10/11/2017 We created a Slack group to provide personalised help on running and interpreting MOFA, [this is the link](https://join.slack.com/t/mofahelp/shared_invite/enQtMjcxNzM3OTE3NjcxLWNhZmM1MDRlMTZjZWRmYWJjMGFmMDkzNDBmMDhjYmJmMzdlYzU4Y2EzYTI1OGExNzM2MmUwMzJkZmVjNDkxNGI) ## Installation @@ -220,6 +220,6 @@ - [Multi-Omics Factor Analysis: a framework for unsupervised integration of multi‐omics data sets](http://msb.embopress.org/content/14/6/e8124) ## Contact -The package is maintained by Ricard Argelaguet (ricard@ebi.ac.uk) and Britta Velten (britta.velten@embl.de). Please, reach us for problems, comments or suggestions. You can also contact us via a Slack group where we provide quick and personalised help, [this is the link](https://join.slack.com/t/mofahelp/shared_invite/enQtMjcxNzM3OTE3NjcxLTkyZmE5YzNiMDc4OTkxYWExYWNlZTRhMWI2OWNkNzhmYmNlZjJiMjA4MjNiYjI2YTc4NjExNzU2ZTZiYzQyNjY). +The package is maintained by Ricard Argelaguet (ricard@ebi.ac.uk) and Britta Velten (britta.velten@embl.de). Please, reach us for problems, comments or suggestions. You can also contact us via a Slack group where we provide quick and personalised help, [this is the link](https://join.slack.com/t/mofahelp/shared_invite/enQtMjcxNzM3OTE3NjcxLWNhZmM1MDRlMTZjZWRmYWJjMGFmMDkzNDBmMDhjYmJmMzdlYzU4Y2EzYTI1OGExNzM2MmUwMzJkZmVjNDkxNGI).