Access dimensional reduction results list
Examples
data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)
obj <- FindVariableFeatures(obj, nfeatures = 1000)
obj <- RunPCA(obj)
#> Calculating principal components from top 1000 HVGs
#> Percent variations:
#> PC1: 88.56 %
#> PC2: 5.9 %
#> PC3: 2.91 %
#> PC4: 1.55 %
#> PC5: 1.08 %
#> PC6: 0 %
pca <- DimReduction(obj)
names(pca)
#> [1] "Scores" "Eigenvectors" "percent_var"
head(pca$Scores[, 1:3])
#> PC1 PC2 PC3
#> ERR1736465 -26.51610 8.8812248 4.943114
#> ERR1736466 -29.45724 -14.1901804 1.709306
#> ERR1736467 -28.65501 5.8971266 -6.228636
#> ERR1736468 27.63783 -1.0411693 -5.907132
#> ERR1736469 27.39885 -0.8413095 -1.797155
#> ERR1736470 29.59167 1.2943077 7.280503