complete slides for omics 3 and add overview LO

omics/crib/cont-fgf-s30.R

@@ -458,50 +458,20 @@ s30_log2_trans <- s30_results |>
 colnames(s30_log2_trans) <- s30_results$xenbase_gene_id
 
 # just for indep study before
-# s30_log2_trans$sample <- row.names(s30_log2_trans)
-# a <- s30_log2_trans |> ggplot(aes(x = `XB-GENE-1000007`, 
+# sample_id <- row.names(s30_log2_trans) |> str_remove("log2_")
+# fig <- s30_log2_trans |> ggplot(aes(x = `XB-GENE-1000007`,
 #                              y = `XB-GENE-1000023`)) +
 #   geom_point() +
-#   geom_text(aes(label = sample), 
+#   geom_text(aes(label = sample_id),
 #             vjust = -1, size = 3) +
 #   scale_x_continuous(expand = c(0.05,0.05)) +
 #   scale_y_continuous(expand = c(0.05,0.05)) +
 #   theme_classic()
 # 
 # 
-# b <- s30_log2_trans |> ggplot(aes(x = `XB-GENE-1000062`, 
-#                                   y = `XB-GENE-1000072`)) +
-#   geom_point() +
-#   geom_text(aes(label = sample), 
-#             vjust = -1, size = 3) +
-#   scale_x_continuous(expand = c(0.05,0.05)) +
-#   scale_y_continuous(expand = c(0.05,0.05)) +
-#   theme_classic()
-# 
-# c <- s30_log2_trans |> ggplot(aes(x = `XB-GENE-1000113`, 
-#                                   y = `XB-GENE-1000132`)) +
-#   geom_point() +
-#   geom_text(aes(label = sample), 
-#             vjust = -1, size = 3) +
-#   scale_x_continuous(expand = c(0.05,0.05)) +
-#   scale_y_continuous(expand = c(0.05,0.05)) +
-#   theme_classic()
-# 
-# d <- s30_log2_trans |> ggplot(aes(x = `XB-GENE-1000149`, 
-#                                   y = `XB-GENE-1000251`)) +
-#   geom_point() +
-#   geom_text(aes(label = sample), 
-#             vjust = -1, size = 3) +
-#   scale_x_continuous(expand = c(0.05,0.05)) +
-#   scale_y_continuous(expand = c(0.05,0.05)) +
-#   theme_classic()
-# 
-# library(patchwork)
-# fig <- (a + b) / (c + d)
-# 
-# ggsave("omics/week-5/images/why_pca.png", 
+# ggsave("omics/week-5/images/why_pca_frog.png",
 #        plot = fig,
-#        width = 6, height = 6)
+#        width = 4, height = 4)
 
 # perform PCA using standard functions
 pca <- s30_log2_trans |>

omics/crib/hspc-prog.R

@@ -509,6 +509,30 @@ prog_hspc_trans <- prog_hspc_results |>
 
 colnames(prog_hspc_trans) <- prog_hspc_results$ensembl_gene_id
 
+# just for indep study before
+# prog_hspc_trans$cell_id <- row.names(prog_hspc_trans)
+# prog_hspc_trans <- prog_hspc_trans |> 
+#   extract(cell_id, 
+#           remove = FALSE,
+#           c("cell_type", "cell_number"),
+#           "([a-zA-Z]{4})_([0-9]{3})")
+# 
+# fig <- prog_hspc_trans |> ggplot(aes(x = ENSMUSG00000028639,
+#                              y = ENSMUSG00000024053, colour = cell_type)) +
+#   geom_point() +
+#   # geom_text(aes(label = cell_id),
+#   #           vjust = -1, size = 3) +
+#   scale_x_continuous(expand = c(0.05,0.05)) +
+#   scale_y_continuous(expand = c(0.05,0.05)) +
+#   theme_classic() +
+# theme(legend.position = "none")
+# 
+# 
+# ggsave("omics/week-5/images/why_pca_mouse.png",
+#        plot = fig,
+#        width = 4, height = 4)
+
+
 # perform PCA using standard functions
 pca <- prog_hspc_trans |>
   prcomp(scale. = TRUE) 
@@ -625,3 +649,16 @@ ggsave("omics/week-5/figures/prog-hspc-volcano.png",
        units = "in",
        device = "png")
 
+
+# # just for the independent study slides
+# vol <- prog_hspc_results |> 
+#   ggplot(aes(x = summary.logFC, 
+#              y = FDR)) +
+#   geom_point() +
+#   theme_classic() 
+# ggsave("omics/week-5/images/volcano-why.png",
+#        plot = vol,
+#        height = 4.5, 
+#        width = 4.5,
+#        units = "in",
+#        device = "png")

omics/week-5/overview.qmd

@@ -14,12 +14,11 @@ We suggest you sit together with your group in the workshop.
 The successful student will be able to:
 
 -   verify they have the required RStudio Project set up and the data and code files from the previous Workshop and Consolidation study
--   explain 
--   
--   
--   
--   
--   ,
+-   explain where gene information came from and add it to their results
+-   perform a PCA and understand how to interpret them
+-   create a heatmap and understand how to interpret them
+-   create a volcano plot and understand how to interpret them
+
 
 ### Instructions
 
@@ -29,11 +28,13 @@ The successful student will be able to:
 
 2.  [Workshop](workshop.qmd)
 
-    i.  💻 ....
+    i.  💻 Add gene information to the results of DE
 
-    ii. 💻 ...
+    ii. 💻 Perform and plot a PCA
 
-    iii. 💻 ....
+    iii. 💻 Visualise results with a heatmap
+
+    iv. 💻 Visualise all the results with a volcano plot
 
     iv. Look after future you!