# Visualization of F(.)
p <- ggplot (
data = FX_long ,
- aes ( x = `Time period` , y = F , linetype = `Neocortex area` , colour = `Neocortex area` , group = `Neocortex area` )
+ aes (
+ x = `Time period` , y = F ,
+ linetype = `Neocortex area` , colour = `Neocortex area` , group = `Neocortex area`
+ )
) +
geom_line ( linewidth = 0.5 ) +
ylab ( NULL ) +
@@ -228,12 +231,12 @@
-# Inferred gene factors (corresponding to the W matrix in the MFAI paper)
-gene_factors <- mfairObject @ W
-rownames ( gene_factors ) <- colnames ( mfairObject @ Y ) # Assign gene symbols
-colnames ( gene_factors ) <- paste ( "Factor" , c ( 1 : 3 ) )
-head ( gene_factors )
-#> Factor 1 Factor 2 Factor 3
+# Inferred gene loadings (corresponding to the W matrix in the MFAI paper)
+gene_loadings <- mfairObject @ W
+rownames ( gene_loadings ) <- colnames ( mfairObject @ Y ) # Assign gene symbols
+colnames ( gene_loadings ) <- paste ( "Loading" , c ( 1 : 3 ) )
+head ( gene_loadings )
+#> Loading 1 Loading 2 Loading 3
#> DCUN1D2 -1.0973545 0.4754917 -0.4656467
#> ARRB1 3.7897678 0.5357535 0.3688403
#> PDE1B 0.1807732 3.1537712 -0.5730507
@@ -241,8 +244,8 @@
-# Heatmap of the inferred gene factors
-pheatmap :: pheatmap ( t ( gene_factors ) ,
+# Heatmap of the inferred gene loadings
+pheatmap :: pheatmap ( t ( gene_loadings ) ,
scale = "column" ,
clustering_method = "complete" ,
cluster_row = FALSE , cluster_col = TRUE ,
@@ -253,14 +256,14 @@ Gene set enrichment analysis= 12
)
-We first calculated the relative weight of the \(k\) -th factor for the \(m\) -th gene by \(\left| W_{mk} \right| / \sum_{k^{\prime}=1}^{3}
+We first calculated the relative weight of the \(k\) -th loading for the \(m\) -th gene by \(\left| W_{mk} \right| / \sum_{k^{\prime}=1}^{3}
\left| W_{mk^{\prime}} \right|\) , where \(W_{m \cdot} \in \mathbb{R}^{3 \times 1}\)
-is the \(m\) -th row of gene factors,
-and then selected the top 300 weighted genes in each factor to form the
+is the \(m\) -th row of gene loadings,
+and then selected the top 300 weighted genes in each loading to form the
gene sets.
+#> Loading 1 Loading 2 Loading 3
+#> [1,] "ARRB1" "PDE1B" "AJAP1"
+#> [2,] "LOXHD1" "PDE7B" "KCNA3"
+#> [3,] "TYRP1" "KCNA2" "ASTN2"
+#> [4,] "PRKG1" "PMP22" "EMID1"
+#> [5,] "MS4A8B" "GPR155" "GPR52"
+#> [6,] "FAM131B" "SMAD2" "SEC24D"
Then we can conduct the gene set enrichment analysis based on Gene Ontology for each factor.
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diff --git a/articles/neocortex_files/figure-html/unnamed-chunk-8-1.png b/articles/neocortex_files/figure-html/unnamed-chunk-8-1.png
index 06787e921d63a51217b84cec646d65b32956932f..033d9aad3e7367255f513e8d419c7fa56a81c0d0 100644
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