-
Notifications
You must be signed in to change notification settings - Fork 0
/
ref.bib
97 lines (91 loc) · 10.3 KB
/
ref.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
@article{zhu_heavy-tailed_2019,
title = {Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences},
volume = {35},
issn = {1367-4803},
shorttitle = {Heavy-tailed prior distributions for sequence count data},
url = {https://doi.org/10.1093/bioinformatics/bty895},
doi = {10.1093/bioinformatics/bty895},
abstract = {In RNA-seq differential expression analysis, investigators aim to detect those genes with changes in expression level across conditions, despite technical and biological variability in the observations. A common task is to accurately estimate the effect size, often in terms of a logarithmic fold change (LFC).When the read counts are low or highly variable, the maximum likelihood estimates for the LFCs has high variance, leading to large estimates not representative of true differences, and poor ranking of genes by effect size. One approach is to introduce filtering thresholds and pseudocounts to exclude or moderate estimated LFCs. Filtering may result in a loss of genes from the analysis with true differences in expression, while pseudocounts provide a limited solution that must be adapted per dataset. Here, we propose the use of a heavy-tailed Cauchy prior distribution for effect sizes, which avoids the use of filter thresholds or pseudocounts. The proposed method, Approximate Posterior Estimation for generalized linear model, apeglm, has lower bias than previously proposed shrinkage estimators, while still reducing variance for those genes with little information for statistical inference.The apeglm package is available as an R/Bioconductor package at https://bioconductor.org/packages/apeglm, and the methods can be called from within the DESeq2 software.Supplementary data are available at Bioinformatics online.},
number = {12},
urldate = {2022-11-19},
journal = {Bioinformatics},
author = {Zhu, Anqi and Ibrahim, Joseph G and Love, Michael I},
month = jun,
year = {2019},
pages = {2084--2092},
file = {Full Text PDF:/home/arvin/Zotero/storage/DIS935XY/Zhu et al. - 2019 - Heavy-tailed prior distributions for sequence coun.pdf:application/pdf;Snapshot:/home/arvin/Zotero/storage/XFNW4LBU/5159452.html:text/html},
}
@article{love_moderated_2014,
title = {Moderated estimation of fold change and dispersion for {RNA}-seq data with {DESeq2}},
volume = {15},
issn = {1474-760X},
url = {http://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8},
doi = {10.1186/s13059-014-0550-8},
abstract = {In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www. bioconductor.org/packages/release/bioc/html/DESeq2.html.},
language = {en},
number = {12},
urldate = {2022-11-19},
journal = {Genome Biology},
author = {Love, Michael I and Huber, Wolfgang and Anders, Simon},
month = dec,
year = {2014},
pages = {550},
file = {Love et al. - 2014 - Moderated estimation of fold change and dispersion.pdf:/home/arvin/Zotero/storage/74SGCIGI/Love et al. - 2014 - Moderated estimation of fold change and dispersion.pdf:application/pdf},
}
@misc{noauthor_deseq2_walkthrough_nodate,
title = {{DESeq2}\_walkthrough - {TMM4910}[{H}] {RNA}-seq analysis [{LEC}] 20229},
url = {https://uottawa.brightspace.com/d2l/le/content/343911/viewContent/4900030/View},
urldate = {2022-11-22},
file = {DESeq2_walkthrough - TMM4910[H] RNA-seq analysis [LEC] 20229:/home/arvin/Zotero/storage/P6R77268/View.html:text/html},
}
@article{vinas_sex_2020,
title = {Sex diversity in proximal tubule and endothelial gene expression in mice with ischemic acute kidney injury},
volume = {134},
issn = {1470-8736},
doi = {10.1042/CS20200168},
abstract = {Female sex protects against development of acute kidney injury (AKI). While sex hormones may be involved in protection, the role of differential gene expression is unknown. We conducted gene profiling in male and female mice with or without kidney ischemia-reperfusion injury (IRI). Mice underwent bilateral renal pedicle clamping (30 min), and tissues were collected 24 h after reperfusion. RNA-sequencing (RNA-Seq) was performed on proximal tubules (PTs) and kidney endothelial cells. Female mice were resistant to ischemic injury compared with males, determined by plasma creatinine and neutrophil gelatinase-associated lipocalin (NGAL), histologic scores, neutrophil infiltration, and extent of apoptosis. Sham mice had sex-specific gene disparities in PT and endothelium, and male mice showed profound gene dysregulation with ischemia-reperfusion compared with females. After ischemia PTs from females exhibited smaller increases compared with males in injury-associated genes lipocalin-2 (Lcn2), hepatitis A virus cellular receptor 1 (Havcr1), and keratin 18 (Krt18), and no up-regulation of SRY-Box transcription factor 9 (Sox9) or keratin 20 (Krt20). Endothelial up-regulation of adhesion molecules and cytokines/chemokines occurred in males, but not females. Up-regulated genes in male ischemic PTs were linked to tumor necrosis factor (TNF) and Toll-like receptor (TLR) pathways, while female ischemic PTs showed up-regulated genes in pathways related to transport. The data highlight sex-specific gene expression differences in male and female PTs and endothelium before and after ischemic injury that may underlie disparities in susceptibility to AKI.},
language = {eng},
number = {14},
journal = {Clinical Science (London, England: 1979)},
author = {Viñas, Jose L. and Porter, Christopher J. and Douvris, Adrianna and Spence, Matthew and Gutsol, Alex and Zimpelmann, Joseph A. and Tailor, Karishma and Campbell, Pearl A. and Burns, Kevin D.},
month = jul,
year = {2020},
pmid = {32662516},
keywords = {acute kidney injury, Acute Kidney Injury, Animals, Endothelial Cells, Female, Gene Expression Profiling, Kidney Tubules, Proximal, Male, Mice, Reperfusion Injury, Sequence Analysis, RNA, Sex Characteristics, sexual dimorphism, transcriptome},
pages = {1887--1909},
file = {Full Text:/home/arvin/Zotero/storage/9HMPFGL5/Viñas et al. - 2020 - Sex diversity in proximal tubule and endothelial g.pdf:application/pdf},
}
@article{gu_complex_2016,
title = {Complex heatmaps reveal patterns and correlations in multidimensional genomic data},
volume = {32},
issn = {1367-4803},
url = {https://doi.org/10.1093/bioinformatics/btw313},
doi = {10.1093/bioinformatics/btw313},
abstract = {Summary: Parallel heatmaps with carefully designed annotation graphics are powerful for efficient visualization of patterns and relationships among high dimensional genomic data. Here we present the ComplexHeatmap package that provides rich functionalities for customizing heatmaps, arranging multiple parallel heatmaps and including user-defined annotation graphics. We demonstrate the power of ComplexHeatmap to easily reveal patterns and correlations among multiple sources of information with four real-world datasets.Availability and Implementation: The ComplexHeatmap package and documentation are freely available from the Bioconductor project: http://www.bioconductor.org/packages/devel/bioc/html/ComplexHeatmap.html.Contact:[email protected] information:Supplementary data are available at Bioinformatics online.},
number = {18},
urldate = {2022-12-02},
journal = {Bioinformatics},
author = {Gu, Zuguang and Eils, Roland and Schlesner, Matthias},
month = sep,
year = {2016},
pages = {2847--2849},
file = {Full Text PDF:/home/arvin/Zotero/storage/D5NDIWG3/Gu et al. - 2016 - Complex heatmaps reveal patterns and correlations .pdf:application/pdf;Snapshot:/home/arvin/Zotero/storage/VNP5RP2Z/1743594.html:text/html},
}
@misc{korotkevich_fast_2021,
title = {Fast gene set enrichment analysis},
copyright = {© 2021, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
url = {https://www.biorxiv.org/content/10.1101/060012v3},
doi = {10.1101/060012},
abstract = {Gene set enrichment analysis (GSEA) is an ubiquitously used tool for evaluating pathway enrichment in transcriptional data. Typical experimental design consists in comparing two conditions with several replicates using a differential gene expression test followed by preranked GSEA performed against a collection of hundreds and thousands of pathways. However, the reference implementation of this method cannot accurately estimate small P-values, which significantly limits its sensitivity due to multiple hypotheses correction procedure.
Here we present FGSEA (Fast Gene Set Enrichment Analysis) method that is able to estimate arbitrarily low GSEA P-values with a high accuracy in a matter of minutes or even seconds. To confirm the accuracy of the method, we also developed an exact algorithm for GSEA P-values calculation for integer gene-level statistics. Using the exact algorithm as a reference we show that FGSEA is able to routinely estimate P-values up to 10−100 with a small and predictable estimation error. We systematically evaluate FGSEA on a collection of 605 datasets and show that FGSEA recovers much more statistically significant pathways compared to other implementations.
FGSEA is open source and available as an R package in Bioconductor (http://bioconductor.org/packages/fgsea/) and on GitHub (https://github.com/ctlab/fgsea/).},
language = {en},
urldate = {2022-12-02},
publisher = {bioRxiv},
author = {Korotkevich, Gennady and Sukhov, Vladimir and Budin, Nikolay and Shpak, Boris and Artyomov, Maxim N. and Sergushichev, Alexey},
month = feb,
year = {2021},
note = {Pages: 060012
Section: New Results},
file = {Full Text PDF:/home/arvin/Zotero/storage/5LMMSFYT/Korotkevich et al. - 2021 - Fast gene set enrichment analysis.pdf:application/pdf;Snapshot:/home/arvin/Zotero/storage/H8IXXS7A/060012v3.html:text/html},
}