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Interested in the analysis of Spatial Omics data?
tidySpatialExperiment
SpatialExperiment
MoleculeExperiment
16 days ago
info
▴ 10
0
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0
replies
233
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Job:
Bioinformatics Spatial Omics and Proteomics Postdoc (Research Officer / Senior Research Officer) at WEHI (Melbourne, Australia)
Proteomics
JobPosting
SingleCell
Spatial
SpatialExperiment
9 weeks ago
Peter Hickey
▴ 740
0
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645
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Job:
Bioinformatics Spatial Omics Postdoc (Research Officer / Senior Research Officer) at WEHI (Melbourne, Australia)
SpatialExperiment
JobPosting
11 months ago
Peter Hickey
▴ 740
1
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Convert SpatialExperiment Object to Seurat Object
scuttle
scater
seurat
SpatialExperiment
15 months ago
cathalgking
▴ 10
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668
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cell_count column in SpatialExperiment Object
scuttle
scater
SpatialExperiment
updated 15 months ago by
alan.ocallaghan
▴ 160 • written 16 months ago by
cathalgking
▴ 10
5 results • Page
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Comment: deseq2 results
by
sajadahmad41454
• 0
thank you for your response, should i remove or discard that sample? since the red outlier on left represents one of healthy samples.
Comment: deseq2 results
by
swbarnes2
★ 1.4k
It looks like the PCA plot of a real RNASeq experiment. The red outlier on the left might be the mathematical reason why you have few vali…
Comment: Multi-factorial, longitudinal disease progression analysis with unbalanced patie
by
Dylan.Sheerin
• 0
Thank you very much, Gordon. I'll give voomLmFit a go!
Answer: Multi-factorial, longitudinal disease progression analysis with unbalanced patie
by
Gordon Smyth
50k
Including subjectID in the design matrix always accounts for unbalanced sampling and patient variation but subjects with incomplete records…
Comment: Log-cpm values from limma
by
Gordon Smyth
50k
No, it does not mean that. `voom()` uses the design matrix with the W covariates, to compute precision weights but not to adjust the log-cp…
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Comment: deseq2 results
Comment: deseq2 results
Answer: Multi-factorial, longitudinal disease progression analysis with unbalanced patie
Answer: Extremely small p-values using Limma for proteomic data
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