Ryan C. Thompson 5 lat temu
rodzic
commit
65ab19ae6e
2 zmienionych plików z 8 dodań i 29 usunięć
  1. 3 3
      code-refs.bib
  2. 5 26
      refs.bib

+ 3 - 3
code-refs.bib

@@ -1,7 +1,7 @@
 %% This BibTeX bibliography file was created using BibDesk.
 %% http://bibdesk.sourceforge.net/
 
-%% Created for Ryan C. Thompson at 2019-09-12 00:38:41 -0700 
+%% Created for Ryan C. Thompson at 2019-09-12 00:43:35 -0700 
 
 
 %% Saved with string encoding Unicode (UTF-8) 
@@ -11,8 +11,8 @@
 @misc{gh-idr,
 	Author = {Nathan Boley},
 	Date-Added = {2019-09-12 00:06:36 -0700},
-	Date-Modified = {2019-09-12 00:07:34 -0700},
-	Howpublished = {\url{https://github.com/nboley/idr/}},
+	Date-Modified = {2019-09-12 00:43:32 -0700},
+	Howpublished = {\url{https://github.com/nboley/idr}},
 	Month = {jun},
 	Title = {Irreproducible Discovery Rate (IDR)},
 	Year = {2017}}

+ 5 - 26
refs.bib

@@ -3950,23 +3950,6 @@ url = {http://www.ncbi.nlm.nih.gov/pubmed/18042555},
 volume = {24},
 year = {2008}
 }
-@article{Argelaguet2018,
-abstract = {Multi-omic studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous datasets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omic datasets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation, and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex-vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multiomics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.},
-author = {Argelaguet, Ricard and Velten, Britta and Arnol, Damien and Dietrich, Sascha and Zenz, Thorsten and Marioni, John C. and Buettner, Florian and Huber, Wolfgang and Stegle, Oliver},
-doi = {10.15252/msb.20178124},
-file = {:Users/ryan/Documents/Mendeley Desktop/Argelaguet et al. - 2018 - Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets.pdf:pdf},
-issn = {1744-4292},
-journal = {Molecular Systems Biology},
-keywords = {biology,data integration,dimensionality reduction,genome-scale,integrative,methods,multi-omics,personalized medicine,resources,single-cell omics,subject categories computational biology},
-month = {jun},
-number = {6},
-pages = {e8124},
-pmid = {29925568},
-title = {{Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets}},
-url = {https://onlinelibrary.wiley.com/doi/abs/10.15252/msb.20178124},
-volume = {14},
-year = {2018}
-}
 @article{Robinson2012,
 author = {Chen, Yunshun and Mccarthy, Davis and Robinson, Mark and Smyth, Gordon K},
 file = {:Users/ryan/Documents/Mendeley Desktop/Chen et al. - 2015 - edgeR differential expression analysis of digital gene expression data User ' s Guide.pdf:pdf},
@@ -4469,12 +4452,6 @@ title = {{Flexible statistical methods for estimating and testing effects in gen
 url = {http://www.biorxiv.org/content/biorxiv/early/2017/05/09/096552.full.pdf},
 year = {2016}
 }
-@misc{,
-abstract = {s41564-019-0480-z.pdf},
-file = {:Users/ryan/Documents/Mendeley Desktop/Unknown - Unknown - 801F42Df5010665E1392612B2B80D7607D43C650.Pdf.pdf:pdf},
-title = {{801F42Df5010665E1392612B2B80D7607D43C650.Pdf}},
-url = {https://www.nature.com/articles/s41571-019-0187-3.pdf}
-}
 @article{Anders2012,
 author = {Anders, Simon and Reyes, Alejandro and Huber, Wolfgang},
 file = {:Users/ryan/Documents/Mendeley Desktop/Anders, Reyes, Huber - 2012 - Detecting differential usage of exons from RNA-seq data.pdf:pdf},
@@ -7212,17 +7189,19 @@ url = {https://watermark.silverchair.com/gkv1191.pdf?token=AQECAHi208BE49Ooan9kk
 volume = {44},
 year = {2015}
 }
-@article{Argelaguet2018a,
-abstract = {Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy-chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multi-omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.},
+@article{Argelaguet2018,
+abstract = {Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy-chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multi-omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.},
 author = {Argelaguet, Ricard and Velten, Britta and Arnol, Damien and Dietrich, Sascha and Zenz, Thorsten and Marioni, John C and Buettner, Florian and Huber, Wolfgang and Stegle, Oliver},
 doi = {10.15252/msb.20178124},
-file = {:Users/ryan/Documents/Mendeley Desktop/Argelaguet et al. - 2018 - Multi-Omics Factor Analysis — a framework for unsupervised integration of multi-omics data sets.pdf:pdf},
+file = {:Users/ryan/Documents/Mendeley Desktop/Argelaguet et al. - 2018 - Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets(2).pdf:pdf},
 issn = {1744-4292},
 journal = {Molecular Systems Biology},
 keywords = {biology,data integration,dimensionality reduction,genome-scale,integrative,methods,multi-omics,personalized medicine,resources,single-cell omics,subject categories computational biology},
+month = {jun},
 number = {6},
 pages = {1--13},
 title = {{Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets}},
+url = {https://onlinelibrary.wiley.com/doi/abs/10.15252/msb.20178124},
 volume = {14},
 year = {2018}
 }