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plots are generated with plotly now and some minor changes
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fawaz-dabbaghieh
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Feb 2, 2018
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,53 @@ | ||
| plotly_pca <- function(experiments_info_meta,filtered_score_matrix, project,type_of_score, | ||
| color_by = "biosource_name", | ||
| epigenetic_mark = "No Epigenetic mark selected", | ||
| first_pc="1", | ||
| second_pc="2", | ||
| show_legend = T){ | ||
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|
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| #calculating PCA | ||
| pca <- prcomp(filtered_score_matrix, center = TRUE, scale. = TRUE) | ||
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| #preparing the plot data by taking the PCAs and adding metadata | ||
| plot.data <- as.data.frame(pca$rotation) %>% | ||
| tibble::rownames_to_column(var = "experiment") %>% | ||
| dplyr::left_join(experiments_info_meta, by=c("experiment")) | ||
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| # #Getting colour pallet | ||
| # colourCount <- 9 | ||
| # getPalette <- colorRampPalette(brewer.pal(colourCount, "Set1")) | ||
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| if(project == "DEEP"){ | ||
| label <- "DEEP_SAMPLE_ID" | ||
| hover = ~paste("Sample ID: ", DEEP_SAMPLE_ID, | ||
| '</br>Biosource Name: ', biosource_name) | ||
| }else{ | ||
| label <- "experiment" | ||
| hover = ~paste("Sample ID: ", experiment, | ||
| '</br>Biosource Name: ', biosource_name) | ||
| } | ||
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| x_lab <- paste0(paste0("PC", first_pc," ", "("), | ||
| round(pca$sdev[as.integer(first_pc)]^2/sum(pca$sdev^2), 2) * 100, "%)") | ||
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| y_lab <- paste0(paste0("PC", second_pc," ", "("), | ||
| round(pca$sdev[as.integer(second_pc)]^2/sum(pca$sdev^2), 2) * 100, "%)") | ||
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| browser() | ||
| p <- | ||
| plot.data %>% | ||
| arrange(plot.data[,color_by]) %>% | ||
| plot_ly(x = as.formula(paste0("~","PC", first_pc)), | ||
| text = hover, | ||
| color = as.formula(paste0("~",color_by)), | ||
| legendgroup = as.formula(paste0("~",color_by)), | ||
| colors = brewer.pal(9, "Set1"), | ||
| marker = list(size = 17.5)) %>% | ||
| add_markers(y = as.formula(paste0("~","PC", second_pc)), showlegend = show_legend) %>% | ||
| layout(title = paste("2 PCs plot", type_of_score, epigenetic_mark), | ||
| yaxis = list(title = y_lab, zeroline = FALSE), | ||
| xaxis = list(title = x_lab, zeroline = FALSE)) | ||
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| return(p) | ||
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||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,68 @@ | ||
| supervised_sva_batch_effect <- function(filtered_score_matrix, | ||
| adjustment_var, | ||
| interest_var){ | ||
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| # filtered_matrix <- filtered_score_matrix$data | ||
| metadata <- attr(filtered_score_matrix, "meta") | ||
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| #validation of the inptus | ||
| #The variable selected should have more than 1 level | ||
| if(adjustment_var == ""){ | ||
| adjustment_var = NULL | ||
| }else{ | ||
| for (adj_var in adjustment_var){ | ||
|
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| validate( | ||
| need(!anyNA(metadata[,adj_var]), message = paste(adj_var, "has NAs and cannot be used to make the model")) | ||
| ) | ||
| validate( | ||
| need(nlevels(metadata[,adj_var]) > 1, message = paste(adj_var,"has less than 2 level", | ||
| "check levels using the pie chart")) | ||
| ) | ||
| } | ||
| } | ||
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| if(interest_var == ""){ | ||
| validate( | ||
| need(FALSE, message = "You need to choose a variable of interest for the full model in SVA") | ||
| ) | ||
| }else{ | ||
| for (inter_var in interest_var){ | ||
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| validate( | ||
| need(!anyNA(metadata[,inter_var]), message = paste(inter_var, "has NAs and cannot be used for the model")) | ||
| ) | ||
| validate( | ||
| need(nlevels(metadata[,inter_var]) > 1, message = paste(inter_var,"has less than 2 level", | ||
| "check levels using the pie chart")) | ||
| ) | ||
| } | ||
| } | ||
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| if(is.null(adjustment_var)){ | ||
| #No interest variable, mod0 is the intercept, full mod is the interest_var | ||
| mod0 <- model.matrix(~1, data = metadata) | ||
| mod <- model.matrix(as.formula(paste0("~", paste(interest_var, collapse = "+"))), | ||
| data = metadata) | ||
|
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||
| }else{ | ||
| mod0 <- model.matrix(as.formula(paste0("~", paste(adjustment_var, collapse = "+"))), | ||
| data = metadata) | ||
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| mod <- model.matrix(as.formula(paste0("~", paste( | ||
| paste(interest_var, collapse = " + "),"+", paste(adjustment_var, collapse = " + ") | ||
| ) | ||
| )),data = metadata) | ||
| } | ||
| n.sv <- num.sv(filtered_score_matrix, mod, method = "leek") | ||
| showNotification(paste("The number of latent factors estimated is", n.sv), duration = 3) | ||
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| sva_object <- sva(filtered_score_matrix, mod, mod0, n.sv = n.sv) | ||
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| batch_adjusted_matrix <- sva_object$sv | ||
|
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| attr(batch_adjusted_matrix, "meta") <- metadata | ||
|
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| return(batch_adjusted_matrix) | ||
| } |
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