Showing : data integration • reset
2
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4
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964
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Joint pathway Analysis using both gene and metabolite data sets
data integration gene metabolite
4.1 years ago emisecherre • 0
1
vote
0
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784
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Job: Metabolomics Bioinformatics Postdoc Position at Steno Diabetes Center Copenhagen
metabolomics lipidomics clinical research data integration biostatistics Job
4.2 years ago Tommi Suvitaival ▴ 10
0
votes
0
replies
927
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Job: Bionformatician Position / Heidelberg
neuroscience data integration transcriptomics Job
5.1 years ago • updated 5.0 years ago slezak • 0
0
votes
0
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1.0k
views
Job: Research Fellow, Computational Multi-Omics of Cancer (University College London)
cancer multi-omics genomic instability machine learning data integration Job
5.1 years ago msecrier ▴ 20
2
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0
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994
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Job: Research Fellow, Computational Cancer Genomics (University College London)
cancer genomics machine learning data integration multi-omics Job
5.3 years ago msecrier ▴ 20
0
votes
28
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3.2k
views
Job: Postdoc on integrating and analyzing new transcriptome types in the Bgee database
rnaseq data integration single-cell pacbio across species Job
6.1 years ago bgee • 0
0
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0
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1.4k
views
News: Pathview Web server published in NAR
pathway analysis visualization data integration omics pathview News
7.0 years ago Luo Weijun ★ 1.6k
0
votes
0
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1.2k
views
Job: PhD Position in Bioinformatics
bioinformatics protein variantannotation data integration Job
7.5 years ago stephan.pabinger • 0
0
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0
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1.8k
views
Job: Data scientist/bioinformatician - Paris
Genomics metabolomics biostatistics data integration Job
8.5 years ago David Enot ▴ 30
0
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0
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1.7k
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Job: Research Scientist in Bioinformatics/Statistical Genomics, Max Planck Institute, Nijmegen, Netherlands
job rnaseq rna-seq genomics data integration Job
8.6 years ago clyde.francks • 0
0
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0
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1.2k
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Possible(validated) methodology implemented in R for integrating and analyzing clinical data with gene expression data in R
data integration clinicaldata gene expression data PET
8.6 years ago svlachavas ▴ 830
1
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0
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2.2k
views
Job: Postdoctoral/Senior Scientist Position in New York City- Mount Sinai School of Medicine
genomics data integration job ngs computational biology Job
8.9 years ago supindab ▴ 10
0
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0
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1.6k
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Machine learning methods for feature selection and possible correlation with clinical data
machinelearning clinicaldata biomarkers data integration bioconductor
9.0 years ago svlachavas ▴ 830
1
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0
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1.9k
views
Job: PhD position - Heidelberg: Bioinformatic analysis of high-throughput and image-based RNAi screening data
high-throughput screening RNAi image analysis data integration visualization Job
9.1 years ago e.schmidt ▴ 10
0
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2
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1.6k
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Possible Data integration of molecular data(transcriptomic) and imaging data from the same patients in R
data integration microarray bioconductor affymetrix biomarkers
updated 9.1 years ago by ★ 13k • written 9.2 years ago by ▴ 830
15 results • Page 1 of 1
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Answer: limma Intercept vs No-intercept models completely changing DMR results? by Gordon Smyth 50k
You seem to have the misunderstanding that you can change the design matrix but keep all the other code unchanged. The contrasts are comput…
Answer: package goseq seems to be not available on the latest version of R by Gordon Smyth 50k
It is true that goseq isn't available for Bioc 3.19. That is because goseq depends on txbmaker, which is itself not yet available for Bioc…
Answer: limma Intercept vs No-intercept models completely changing DMR results? by James W. MacDonald 65k
This part: ``` design_intercept <- model.matrix(~Alt+Sex+Age+PC1, data=targets_Sherpa) colnames(design_intercept) <- c("AltSHP_LA","Al…
Comment: CombineArrays for EPIC and EPIC V2 by Kim • 0
Thank you Tim, this is a great help in getting me started!
Comment: Too many significant genes when integrating gtex and tcga by Michael Love 41k
Yes, this was one of the aspects we highlighted in the 2014 paper, and it's also in the workflow. Check these places first. Also take a st…
Votes
Answer: limma Intercept vs No-intercept models completely changing DMR results?
Answer: CombineArrays for EPIC and EPIC V2
Answer: Too many significant genes when integrating gtex and tcga
Comment: Too many significant genes when integrating gtex and tcga
A: Print Differentially Expressed Exons From Dexseq Results
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