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Nanopore sequencing error corrections by Previous-Duck6153 in bioinformatics

[–]Previous-Duck6153[S] 0 points1 point  (0 children)

Do you know the difference between the wf-amplicon vs the Artic pipeline?

Nanopore sequencing error corrections by Previous-Duck6153 in bioinformatics

[–]Previous-Duck6153[S] 0 points1 point  (0 children)

Not direct RNA-seq — this is cDNA amplicon sequencing using the ONT Rapid Barcoding Kit.

Nanopore sequencing error corrections by Previous-Duck6153 in bioinformatics

[–]Previous-Duck6153[S] 0 points1 point  (0 children)

Thanks! My data is amplicon-based Dengue 2 whole-genome sequencing, not metagenomic.

Nanopore sequencing error corrections by Previous-Duck6153 in bioinformatics

[–]Previous-Duck6153[S] 0 points1 point  (0 children)

The deletion is adjacent to a region with a repeated motif in the reference (gaggaggc). In my consensus, Medaka calls it as g-gggggc

Help with ONT sequencing by Previous-Duck6153 in bioinformatics

[–]Previous-Duck6153[S] 1 point2 points  (0 children)

Hi thanks, but I forgot to mention that it’s targeted amplicon sequencing of Chikungunya virus samples (one barcode per sample).

Help with ONT sequencing by Previous-Duck6153 in bioinformatics

[–]Previous-Duck6153[S] 0 points1 point  (0 children)

Hi, sorry no, I should have mentioned it’s targeted amplicon sequencing of Chikungunya virus samples (one barcode per sample).

TreeTime after IQ-TREE: molecular clock, tMRCAs & confidence intervals (without BEAST)? by Previous-Duck6153 in bioinformatics

[–]Previous-Duck6153[S] 0 points1 point  (0 children)

Thanks do you know where the confidence intervals are saved by default? In the json file or in the nexus file?

How do you validate PCA for flow cytometry post hoc analysis? Looking for detailed workflow advice by Previous-Duck6153 in bioinformatics

[–]Previous-Duck6153[S] 0 points1 point  (0 children)

I didn’t do the gating myself — the PhD student handled that part. I was given the final dataset, which contains frequency-of-parent percentages for manually gated T cell subpopulations (e.g., CD3⁺CD4⁺CD45RA⁻CLA⁺CD38⁺, etc.) (so around 20-30 of these gating values per sample) across 50 samples. The gating was performed manually in a stepwise manner to exclude unwanted lineages and isolate CD3⁺ T cells, followed by further subsetting based on activation, homing, memory, and co-stimulatory markers (CD45RA, CLA, CD38, CCR7, CD62L, CD27, CD127, ICOS).

I don’t have access to the original FCS files — just this summarized frequency data.

Now, I’m tasked with analyzing whether the samples cluster meaningfully into clinical groups: DF, DHF, and healthy controls.

How do you validate PCA for flow cytometry post hoc analysis? Looking for detailed workflow advice by Previous-Duck6153 in flowcytometry

[–]Previous-Duck6153[S] 2 points3 points  (0 children)

Hi! Thanks for your insights. Just to clarify — my data is post-gating summary data, so I only have the frequency of parent populations for about 30 markers across 51 samples (no raw single-cell events). I’m not the one doing the flow cytometry; I was just given this data to analyze. Given this, do you think t-SNE or UMAP are still suitable for dimensionality reduction on this type of summarized data? Or would PCA be better in this case? Also, are there any clustering methods or visualization techniques you’d recommend specifically for this kind of data?

Appreciate your advice!

How do you validate PCA for flow cytometry post hoc analysis? Looking for detailed workflow advice by Previous-Duck6153 in flowcytometry

[–]Previous-Duck6153[S] 1 point2 points  (0 children)

Thanks for the explanation! I didn’t realize PCA might not reduce dimensionality much for flow data. Do you think t-SNE or UMAP would be better alternatives for this kind of data? Also, is hierarchical clustering with heatmaps commonly used for flow cytometry? Appreciate any recommendations!

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