Commit bc325de9 authored by Bernd Klaus's avatar Bernd Klaus

first final version, without slides

parent 80c7cb31
......@@ -6,3 +6,14 @@ SRP022054/
rse_gene.Rdata
stockori_tmp.Rmd
factanal.R
compare_to_scLVM.R
scran_offset_zinbawave.R
clustering.pdf
clustering_org.pdf
clustering_cl_10.pdf
data/cl_mtec.RData
data/genes_for_clustering.RData
data/nomarkerCellsClustering.RData
data/zinb.RData
get_clustering_aljeandro.R
norm.pdf
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......@@ -2,6 +2,8 @@ library(knitcitations)
library(bibtex)
# Kyewski and Klein, 2006
citep("10.1146/annurev.immunol.23.021704.115601")
......@@ -41,8 +43,6 @@ citep("10.1101/125112")
# Perraudeau et. al. , 2017
citep("10.12688/f1000research.12122.1")
write.bibtex(file = "stat_methods_bioinf.bib")
knitcitations::cleanbib()
# Nygaard et. al, 2015
citep("10.1093/biostatistics/kxv027")
......@@ -50,10 +50,41 @@ citep("10.1093/biostatistics/kxv027")
# Jaffe et. al., 2015
citep("10.1186/s12859-015-0808-5")
# Julia et. al. 2015
citep("10.1093/bioinformatics/btv368")
# Novembre and Stephens, 2008
citep("10.1038/ng.139")
# Shaffer, 1995
citep("10.1146/annurev.ps.46.020195.003021")
# Schäfer and Strimmer, 2005
citep("10.2202/1544-6115.1175")
# Ahdesmäki and Strimmer, 2010
citep("10.1214/09-AOAS277")
write.bibtex(file = "stat_methods_bioinf.bib")
knitcitations::cleanbib()
add_manually <- function(entry){
write("\n", file = "stat_methods_bioinf.bib", append = TRUE)
write(entry, file = "stat_methods_bioinf.bib", append = TRUE)
write("\n", file = "stat_methods_bioinf.bib", append = TRUE)
}
# van der Maaten and Hinton, 2008
write("\n", file = "stat_methods_bioinf.bib", append = TRUE)
write("@article{vanDerMaaten_2008,
add_manually("@article{vanDerMaaten_2008,
author = {van der Maaten, Laurens and Hinton, Geoffrey},
interhash = {370ba8b9e1909b61880a6f47c93bcd49},
intrahash = {8b9aebb404ad4a4c6a436ea413550b30},
......@@ -63,13 +94,10 @@ write("@article{vanDerMaaten_2008,
url = {http://www.jmlr.org/papers/v9/vandermaaten08a.html},
volume = 9,
year = 2008
}", file = "stat_methods_bioinf.bib", append = TRUE)
write("\n", file = "stat_methods_bioinf.bib", append = TRUE)
}")
# Risso et. al., 2017
write("\n", file = "stat_methods_bioinf.bib", append = TRUE)
write("@article {Risso_2017,
add_manually("@article {Risso_2017,
author = {Risso, Davide and Perraudeau, Fanny and Gribkova, Svetlana and Dudoit, Sandrine and Vert, Jean-Philippe},
title = {ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data},
year = {2017},
......@@ -79,12 +107,10 @@ write("@article {Risso_2017,
URL = {https://www.biorxiv.org/content/early/2017/11/02/125112},
eprint = {https://www.biorxiv.org/content/early/2017/11/02/125112.full.pdf},
journal = {bioRxiv}
}", file = "stat_methods_bioinf.bib", append = TRUE)
write("\n", file = "stat_methods_bioinf.bib", append = TRUE)
}")
# Benjamini and Hochberg, 1995
write("\n", file = "stat_methods_bioinf.bib", append = TRUE)
write("@article{Benjamini_1995,
add_manually("@article{Benjamini_1995,
ISSN = {00359246},
URL = {http://www.jstor.org/stable/2346101},
abstract = {The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses-the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferroni-type procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.},
......@@ -96,5 +122,30 @@ write("@article{Benjamini_1995,
title = {Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing},
volume = {57},
year = {1995}
}", file = "stat_methods_bioinf.bib", append = TRUE)
write("\n", file = "stat_methods_bioinf.bib", append = TRUE)
\ No newline at end of file
}")
# Street et. al., 2017
add_manually("@article {Street_2017,
author = {Street, Kelly and Risso, Davide and Fletcher, Russell B and Das, Diya and Ngai, John and Yosef, Nir and Purdom, Elizabeth and Dudoit, Sandrine},
title = {Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics},
year = {2017},
doi = {10.1101/128843},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. These methods can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a number of statistical and computational methods have been proposed for analyzing cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. Here, we introduce a novel method, Slingshot, for inferring multiple developmental lineages from single-cell gene expression data. Slingshot is a uniquely robust and flexible tool for inferring developmental lineages and ordering cells to reflect continuous, branching processes.},
URL = {https://www.biorxiv.org/content/early/2017/04/19/128843},
eprint = {https://www.biorxiv.org/content/early/2017/04/19/128843.full.pdf},
journal = {bioRxiv}
}")
# Delgado et. al. . 2014
add_manually("@article {Delgado_14,
author = {Manuel Fern\'{a}ndez-Delgado and Eva Cernadas and Sen\'{e}n Barro and Dinani Amorim},
title = {Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?},
journal = {Journal of Machine Learning Research},
year = {2014},
volume = {15},
pages = {3133-3181},
url = {http://jmlr.org/papers/v15/delgado14a.html}
}")
@Misc{1,
doi = {10.1163/1872-9037_afco_asc_558},
url = {https://doi.org/10.1163/1872-9037_afco_asc_558},
publisher = {Brill Academic Publishers},
title = {{JSTOR}},
year = {2017},
@Article{Ahdesm_ki_2010,
doi = {10.1214/09-aoas277},
url = {https://doi.org/10.1214/09-aoas277},
year = {2010},
month = {mar},
publisher = {Institute of Mathematical Statistics},
volume = {4},
number = {1},
pages = {503--519},
author = {Miika Ahdesm{\"a}ki and Korbinian Strimmer},
title = {Feature selection in omics prediction problems using cat scores and false nondiscovery rate control},
journal = {The Annals of Applied Statistics},
}
@Article{Brennecke_2013,
......@@ -89,6 +95,20 @@
journal = {{BMC} Bioinformatics},
}
@Article{Juli__2015,
doi = {10.1093/bioinformatics/btv368},
url = {https://doi.org/10.1093/bioinformatics/btv368},
year = {2015},
month = {jun},
publisher = {Oxford University Press ({OUP})},
volume = {31},
number = {20},
pages = {3380--3382},
author = {Miguel Jul {\a'a} and Amalio Telenti and Antonio Rausell},
title = {Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell {RNA}-seq: Fig. 1.},
journal = {Bioinformatics},
}
@Article{Kruskal_1964,
doi = {10.1007/bf02289565},
url = {https://doi.org/10.1007/bf02289565},
......@@ -144,6 +164,20 @@
journal = {Genome Biology},
}
@Article{Novembre_2008,
doi = {10.1038/ng.139},
url = {https://doi.org/10.1038/ng.139},
year = {2008},
month = {apr},
publisher = {Springer Nature},
volume = {40},
number = {5},
pages = {646--649},
author = {John Novembre and Matthew Stephens},
title = {Interpreting principal component analyses of spatial population genetic variation},
journal = {Nature Genetics},
}
@Article{Nygaard_2015,
doi = {10.1093/biostatistics/kxv027},
url = {https://doi.org/10.1093/biostatistics/kxv027},
......@@ -198,6 +232,33 @@
journal = {{PLoS} {ONE}},
}
@Article{Sch_fer_2005,
doi = {10.2202/1544-6115.1175},
url = {https://doi.org/10.2202/1544-6115.1175},
year = {2005},
month = {jan},
publisher = {Walter de Gruyter {GmbH}},
volume = {4},
number = {1},
author = {Juliane Sch{\"a}fer and Korbinian Strimmer},
title = {A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics},
journal = {Statistical Applications in Genetics and Molecular Biology},
}
@Article{Shaffer_1995,
doi = {10.1146/annurev.ps.46.020195.003021},
url = {https://doi.org/10.1146/annurev.ps.46.020195.003021},
year = {1995},
month = {jan},
publisher = {Annual Reviews},
volume = {46},
number = {1},
pages = {561--584},
author = {J P Shaffer},
title = {Multiple Hypothesis Testing},
journal = {Annual Review of Psychology},
}
@article{vanDerMaaten_2008,
author = {van der Maaten, Laurens and Hinton, Geoffrey},
......@@ -244,3 +305,31 @@
}
@article {Street_2017,
author = {Street, Kelly and Risso, Davide and Fletcher, Russell B and Das, Diya and Ngai, John and Yosef, Nir and Purdom, Elizabeth and Dudoit, Sandrine},
title = {Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics},
year = {2017},
doi = {10.1101/128843},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. These methods can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a number of statistical and computational methods have been proposed for analyzing cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. Here, we introduce a novel method, Slingshot, for inferring multiple developmental lineages from single-cell gene expression data. Slingshot is a uniquely robust and flexible tool for inferring developmental lineages and ordering cells to reflect continuous, branching processes.},
URL = {https://www.biorxiv.org/content/early/2017/04/19/128843},
eprint = {https://www.biorxiv.org/content/early/2017/04/19/128843.full.pdf},
journal = {bioRxiv}
}
@article {Delgado_14,
author = {Manuel Fern'{a}ndez-Delgado and Eva Cernadas and Sen'{e}n Barro and Dinani Amorim},
title = {Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?},
journal = {Journal of Machine Learning Research},
year = {2014},
volume = {15},
pages = {3133-3181},
url = {http://jmlr.org/papers/v15/delgado14a.html}
}
......@@ -8,6 +8,7 @@ stat_pkgs <- c("readxl", "scran", "BiocStyle", "knitr", "MASS", "RColorBrewer",
"Rtsne", "devtools", "readxl", "BiocStyle", "knitr", "tidyverse", "RColorBrewer",
"stringr", "pheatmap", "matrixStats", "purrr", "fdrtool", "readr", "gtools",
"factoextra", "magrittr", "entropy", "forcats", "plotly", "corrplot", "car", "forcats",
"openxlsx", "readxl", "limma","Single.mTEC.Transcriptomes")
"openxlsx", "readxl", "limma", "ggthemes","corpcor","sva","zinbwave","clusterExperiment",
"clue","sda","crossval","randomForest")
biocLite(unique(stat_pkgs))
\ No newline at end of file
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