Add metric QC function #78
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lcolladotor
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May 17, 2024
R/metrics_qc.R
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| low_n_features = scater::isOutlier(qc_df$log2detected, type = "lower", log = TRUE, batch = qc_df$sample_id), | ||
| high_subsets_Mito_percent = scater::isOutlier(qc_df$subsets_Mito_percent, type = "higher", batch = qc_df$sample_id) | ||
| ) |> | ||
| dplyr::mutate(discard = (low_lib_size | low_n_features) | high_subsets_Mito_percent) |
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I'd rather not add dplyr as a dependency just for this mutate() function call.
R/metrics_qc.R
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| spot_coords <- colData(spe_in) |> | ||
| as.data.frame() |> | ||
| select(in_tissue, sample_id, array_row, array_col) |> | ||
| group_by(sample_id, array_row) |> |
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Ok, I see more dplyr function calls here. So maybe we can end up adding it then.
If that's the case, it has to be added to the DESCRIPTION file too.
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Adding a function to aid/standardize the QC process of new
speobjects based off of work in Habenula_Visium, spatial_NAc, and Visium_SPG_AD.Utilizes
scater::isOutlieron three metricslog2(sum_umi),log2(sum_gene), andexpr_chrM_ratio*100.Added functionality for handling spots
in_tissue=FALSE.