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2 Data imputation de noising (coverage correction)

Alireza Khodadadi-Jamayran edited this page Oct 30, 2019 · 1 revision
  • Run data imputation
my.obj <- run.impute(my.obj, dims = 1:10, cell.ratio = 2, data.type = "pca")

# more examples
# my.obj <- run.impute(my.obj, cell.ratio = 2, data.type = "tsne")
# my.obj <- run.impute(my.obj, cell.ratio = 2, data.type = "umap")

# save after imputation 
save(my.obj, file = "my.obj.Robj")

# some more plots from another analysis 
A=heatmap.gg.plot(my.obj, gene = MyGenes, interactive = F, cluster.by = "clusters", cell.sort = TRUE)
B=heatmap.gg.plot(my.obj, gene = MyGenes, interactive = F, cluster.by = "clusters", data.type = "imputed", cell.sort = TRUE)
C=heatmap.gg.plot(my.obj, gene = MyGenes, interactive = F, cluster.by = "conditions", cell.sort = TRUE)
D=heatmap.gg.plot(my.obj, gene = MyGenes, interactive = F, cluster.by = "none", data.type = "imputed", cell.sort = TRUE)

# If cluster.by = "none", your heamap would have like a Pseudotime effect.
# It calculates the distance between the cells based on the genes in the heatmap. 

library(gridExtra)
grid.arrange(A,B,C,D)

# main data 
gene.plot(my.obj, gene = "MS4A1", 
    plot.type = "scatterplot",
    interactive = F,
    data.type = "main")

# imputed data 
gene.plot(my.obj, gene = "MS4A1", 
    plot.type = "scatterplot",
    interactive = F,
    data.type = "imputed")		

  • Plotting conditions and clusters for genes
A <- gene.plot(my.obj, gene = "MS4A1", 
   plot.type = "scatterplot",
   interactive = F,
   cell.transparency = 1,
   scaleValue = TRUE,
   min.scale = 0,
   max.scale = 2.5,
   back.col = "white",
   cond.shape = TRUE)
B <- gene.plot(my.obj, gene = "MS4A1", 
   plot.type = "scatterplot",
   interactive = F,
   cell.transparency = 1,
   scaleValue = TRUE,
   min.scale = 0,
   max.scale = 2.5,
   back.col = "white",
   cond.shape = TRUE,
   conds.to.plot = c("KO","WT"))

C <- gene.plot(my.obj, gene = "MS4A1", 
   plot.type = "boxplot",
   interactive = F,
   back.col = "white",
   cond.shape = TRUE,
   conds.to.plot = c("KO"))

D <- gene.plot(my.obj, gene = "MS4A1", 
   plot.type = "barplot",
   interactive = F,
   cell.transparency = 1,
   back.col = "white",
   cond.shape = TRUE,
   conds.to.plot = c("KO","WT"))

library(gridExtra)
grid.arrange(A,B,C,D)

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