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neurodegeneration
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Job:
Statistical Geneticist at Denali Therapeutics, South San Francisco, CA, USA
job
genetics
drug development
neurodegeneration
Job
7.4 years ago
sandmann.t
▴ 70
0
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1.5k
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Job:
Job: Computational Biologists - Expanding the impact of genomic data on drug development (Immuneering Corporation, Cambridge MA and New York NY)
rna-seq
computational biology
cns disease
gene expression data
neurodegeneration
Job
8.0 years ago
mmartin
▴ 20
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1.5k
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Job:
Seeking computational biologists with expertise in RNA-Seq and/or statistics at Immuneering Corporation, Cambridge MA and New York NY
rna-seq
CNS
Neurodegeneration
statistics
computational biology
Job
8.9 years ago
mmartin
▴ 20
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Answer: DiffBind - question on the use of filtered or unfiltered input data
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vejogos850
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DiffBind - question on the use of filtered or unfiltered input data is the best to deal with, and it provides us the solutions that we are …
Answer: Help with defining groups
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Gordon Smyth
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Defining sample groups is separate to reading in the DIA-NN data, because the sample conditions and covariates are not necessarily coded in…
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Peter Hickey
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The [**scRNAseq**](https://bioconductor.org/packages/scRNAseq/) package ([source on GitHub](https://github.com/LTLA/scRNAseq)) might serve …
Comment: Using DESeq2 on single cell RNA Seq data of same cell type but different conditi
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ReinholdGaylord99
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Thank you so much! https://www.nature.com/articles/s41467-021-25960-2/ [geometry dash lite](https://geometrydashlite.online)
Comment: Guidance on limma for pseudobulk scRNA-seq with multi-factor design
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Gordon Smyth
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I'm not sure that I understand. You have a dataset that, even after pseudo-bulking, still has 6000 samples? That is surprising. Retaining …
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Comment: readDNAStringSet with non DNA characters
Comment: Guidance on limma for pseudobulk scRNA-seq with multi-factor design
Answer: Guidance on limma for pseudobulk scRNA-seq with multi-factor design
Answer: Guidance on limma for pseudobulk scRNA-seq with multi-factor design
Comment: Guidance on limma for pseudobulk scRNA-seq with multi-factor design
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