However, iSEE applications can be extensively reconfigured using a number of optional arguments to the iSEE function. In its simplest form, the iSEE function only requires the input object. To begin the exploration, we create an iSEE app with the SingleCellExperiment object generated above. That said, it is straightforward to iteratively explore a precomputed object, take notes of new metrics to compute, close the app, store new results in the SummarizedExperiment object, and launch a new app using the updated object. This allows users to visualize any metrics of interest, but also requires these to be calculated and added to the object before the initialization of the app.
It is important to note that iSEE relies primarily on precomputed values stored in the various slots of objects derived from the SummarizedExperiment class 2 2 2 Except when dealing with custom panels. RowData(sce)$var_log <- apply(logcounts(sce), 1, var) rowData(sce)$mean_log <- rowMeans(logcounts(sce)) Thus, to prepare a fully-featured example application, we also add some gene metadata to the rowData related to the mean-variance relationship in the data. library(scater)Īt this point, the sce object does not contain any annotations for the rows (i.e., features) in the data set. Then, we normalize the expression values with scater. #> "MEDIAN_5PRIME_BIAS" "MEDIAN_3PRIME_BIAS" #> "PCT_INTRONIC_BASES" "PCT_INTERGENIC_BASES" Animal.ID passes_qc_checks_sĪs provided, the sce object contains raw data and a number of quality control and experimental cell annotations, all available in colData(sce). To begin with, we assign the output of this call to an sce object and inspect it. 2016), and can be loaded directly by calling ReprocessedAllenData() and specifying the value for the assays parameter. The allen data set contains expression values for 379 cells from the mouse visual cortex (Tasic et al.