Moderator: jared.andrews07

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jared.andrews075.0k
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Ph.D. candidate at Washington University in St. Louis. I have a strong interest in the development of high-performance, flexible bioinformatic tools that can integrate multiple -omics datasets to yield interesting and plausible conclusions that can then be experimentally validated.

Avid Python user, fledgling developer, hater of Perl. 

Posts by jared.andrews07

<prev • 611 results • page 2 of 62 • next >
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Comment: C: Downsampling one of the sample on the UMAP clustering to match the number of cel
... So your collaborator wants equal numbers of cells *per cluster*? Seems silly to me. Presumably you've already done what I suggested by performing your manual subsetting - your UMAP `DimPlot` should only contain the subsetted cells. My suggestion was just to use the parameters from the plotting func ...
written 7 days ago by jared.andrews075.0k
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Comment: C: Downsampling one of the sample on the UMAP clustering to match the number of cel
... I mean, you're downsampling to a specific number of cells, so yes? Clusters 1, 4, 5, 6, 7 had closer to equal ratios of KO to WT cells, so losing cells from those groups means you're going to end up with fewer KO cells than WT cells. ...
written 7 days ago by jared.andrews075.0k
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Answer: A: Downsampling one of the sample on the UMAP clustering to match the number of cel
... Approach 1 is a poor idea. Downsampling removes information, lowering the power of your differential expression analysis. This could result in marker genes being lost. If they just want to visualize them equally, I would just grab equal numbers of cells from each condition for visualization and feed ...
written 8 days ago by jared.andrews075.0k
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Answer: A: How many PCs should be considered for downstream analyses?
... When using `SCTransform`, this matters somewhat less, as it tends to be more robust and handle noise better. As such, you can provide a lot of PCs without introducing undue variation. I generally start with 30, but have gone up to 50 and noticed little difference. The authors generally recommend usi ...
written 8 days ago by jared.andrews075.0k
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Comment: C: Identifying tumor cells in single cell RNAseq data
... I'm not familiar with that cancer type, so I'm afraid you're on your own there. The suggestions in my answer might be helpful, but I don't know enough about the data/cancer to say which is your best bet. ...
written 9 days ago by jared.andrews075.0k
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Comment: C: Identifying tumor cells in single cell RNAseq data
... Oh, it can totally be valuable. I'm just not sure it's the best tool to differentiate malignant and normal cells, but again, that's highly cancer-type dependent. It's also not terribly difficult to do, so I'd say the upside is strong - just trying to make sure you're aware of some of the caveats. ...
written 9 days ago by jared.andrews075.0k
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Comment: C: Identifying tumor cells in single cell RNAseq data
... This is generally true, but does depend somewhat on the tumor type. Certain leukemias have "progenitor" or "poised" populations that may still harbor significant genetic variation despite not being truly malignant. This is where your biological expertise is going to have to come into play. ...
written 9 days ago by jared.andrews075.0k
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Comment: C: Identifying tumor cells in single cell RNAseq data
... It really helps if you know what to look for. If you have any clinical karyotype data, it can make your life a lot easier. scRNA CNV calling is coarse - you aren't going to pick up many focal changes (< 1MB). If you have a clinical collaborator that provided you the samples, bug them to give you ...
written 9 days ago by jared.andrews075.0k
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Answer: A: How do I change size of gene names displayed on plotHeatmap from scater R Packag
... Pass the `fontsize_row` parameter to your `plotHeatmap` call and adjust accordingly. May have to play around with it to get the right size. That function uses [pheatmap](https://cran.r-project.org/web/packages/pheatmap/pheatmap.pdf) on the backend, and passes extra parameters not recognized by `plo ...
written 9 days ago by jared.andrews075.0k
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Comment: C: How to choose method to integrate different patient sample together in Seurat
... 1. I have also ran into this. I generally scale data **only for the RNA assay** after integration so that the heatmap works properly. I've tried scaling before integration (but after merging all samples into a single Seurat object), but I *think* it gets removed for some reason during integration, i ...
written 10 days ago by jared.andrews075.0k

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Good Answer 1 day ago, created an answer that was upvoted at least 5 times. For A: Significance in gene ontology terms
Teacher 4 days ago, created an answer with at least 3 up-votes. For A: Visualization for ChIP-seq analysis
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Good Answer 19 days ago, created an answer that was upvoted at least 5 times. For A: Significance in gene ontology terms
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Teacher 21 days ago, created an answer with at least 3 up-votes. For A: Visualization for ChIP-seq analysis
Scholar 24 days ago, created an answer that has been accepted. For A: How to make variant calling run faster?
Teacher 25 days ago, created an answer with at least 3 up-votes. For A: Visualization for ChIP-seq analysis
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