User: dsull

gravatar for dsull
dsull900
Reputation:
900
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Location:
UCLA
Last seen:
9 hours ago
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3 years, 6 months ago
Email:
d****@stanford.edu

Name: Delaney Sullivan

Currently at UCLA (formerly at Stanford)

Posts by dsull

<prev • 80 results • page 1 of 8 • next >
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Comment: C: Please Recommend Potential Graphs to Use For Gene Expression Survival Analysis
... I'm not familiar with RMA, what does the distribution of your data look like? Maybe try log2-transforming your final data to reduce skew? I'd recommend just trying to fix the distribution of the data -- otherwise, I'm going to believe that the statistical assumptions of your survival analysis have ...
written 12 hours ago by dsull900
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Answer: A: Downstream RNA seq analysis
... 1) Try `EnrichR`: https://amp.pharm.mssm.edu/Enrichr/ Lots of features -- pathways, gene ontology, etc. Simply copy and paste the list of differentially expressed genes into the text box. Would recommend running it on the upregulated and downregulated genes separately, but this depends on your expe ...
written 19 hours ago by dsull900
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Comment: C: feature selection using random forest
... Here's an example: https://topepo.github.io/caret/recursive-feature-elimination.html If you're new, unfortunately, it's going to take some effort for you to read tutorials and write code. Using advanced supervised machine learning methods properly is not trivial (e.g. you'll need to understand hyp ...
written 23 hours ago by dsull900
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Answer: A: feature selection using random forest
... You have four classes. Why are you using a t-test? You should be using ANOVA. Second, as random forest can tell you feature importances, you can use randomforest with recursive feature elimination (Look up: Recursive feature elimination with cross validation) to figure out a set of features with th ...
written 1 day ago by dsull900
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Comment: C: cuffdiff output table,
... Oh, I see -- with gene names (e.g. Myc, Gata1, Pou5f1), you should do some sort of enrichment analysis (e.g. gene ontology analysis). Check out https://amp.pharm.mssm.edu/Enrichr/ Enter your list of genes there. (If you download the results table, it tells you exactly which genes are associated wi ...
written 1 day ago by dsull900
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Comment: C: Kallisto: Impact of chosen reference transcriptome on the resulting TPMs
... @Lior Pachter -- we'll see if Lior drops by and provides an answer. As for my two cents -- in your first table, the TPMs will sum up to 1000000 (as always is the case). Yet two transcripts (ENST00000364315.1 and ENST00000362545.1), representing 5S ribosomal pseudogenes, are already amounting to > ...
written 1 day ago by dsull900
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Comment: C: cuffdiff output table,
... Please don't use cuffdiff. It's an antiquated tool and there are far better ones out there. Differential gene expression packages such as DESeq2 or sleuth should be used nowadays. As for getting the function of a gene, can't you just do a simple search of the gene name and read the literature asso ...
written 1 day ago by dsull900
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Answer: A: Please Recommend Potential Graphs to Use For Gene Expression Survival Analysis
... I sometimes obtain z scores from Cox regression and then just plot those using whatever graph is convenient. Each gene gets a z score (because you run Cox regression on each gene you're interested in). For example, say there are 100 genes out of 20000 genes that you are interested in. I might make ...
written 1 day ago by dsull900
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Answer: A: Cox Regression Survival Analysis with Only Tumor Data?
... If you're assessing how gene expression in tumor affects survival (e.g. tumors with high expression of gene X are those that have worse prognosis), then just tumor data is fine. You only need normal if you're actually interested in the normal tissue gene expression (e.g. if you want normal tissue g ...
written 2 days ago by dsull900
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Comment: C: RNA Seq data with GSEA
... Ah, for the prerank algorithm, you still use ALL your genes, not just your DE ones. So if 200 out of 40000 genes are DE, you still use all 40000 genes. Basically, you just supply a list of ALL genes ranked by their log fold change. That's the power of GSEA -- you use every single gene and it's not ...
written 25 days ago by dsull900

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