reviewed up to exploration of results #82
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Note: The link to the source dataset is broken. Questions: Is this dataset still the one used for the main scRNA-seq lecture? Any specific reason to start from sce object rather than seurat object?
Suggested simplification of "count aggregation" code; Added comments for clarification and images (still need to upload). Swapped "counts aggregation" and "handling metadata" sections (to enable generating more "custom" metadata per cell type, including a covariate with cell count per sample for this cell type instead of overall, and to handle the case where one sample may have zero cells for that cell type)
Added sub-headers to data wrangling sections. Replaced used of grep() (which I usually use in my own studies but fails in that case since some cell type names contain the character "+")
Used list and loop to create metadata for each cell type, matching list of counts matrices generated before. Code should work for case when one sample has zero cell for one given cell type.
PCA plot looks quite different (same global clustering pattern and conclusion, but spread within sample group not the same). Assuming this is a R/DESeq2 version effect, but will double-check previous steps in my updated code... Assuming you didn't want to enter into this level of detail, but why not run paired analyses here, since we have one stim and one control for each patient (i.e. adding patient_id in the design formula?)
Amelie-TGHN
changed the title
covers only data wrangling section
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Thanks @Amelie-TGHN !! |
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Note: The link to the source dataset is broken.
Questions: Any specific reason to start from sce object rather than seurat object? (assuming this is what was available from the authors)