Source_SimulatedData/SimI-1.R

In cyhsuTN/scKWARN: Single-cell RNA Sequencing Normalization Using A Local Average Technique

rm(list=ls())

library(quantreg)
library(SCnorm)
library(scran)
library(sctransform)
library(Matrix)
library(scKWARN)
source('../NormalizationMethods.R')


G <- 3000
n1 <- n3 <- 200
n2 <- 200
n <- n1 + n2 + n3
G1 <- 0.05 * G

set.seed(2605)
sdg    <- rgamma(G, shape = 2, rate = 4)
beta0g <- - 7 * rgamma(G, shape = 6, rate = 6) - 8
beta1g <- runif(G, min = 0.9, max = 1.1)
DRg    <- runif(G, min = 0.1, max = 0.3)



maxiter <- 100
fc_matrix <- matrix(NA, maxiter, 25)
for (iter in 1:100) {
      set.seed(1004 + iter)

      highg <- sample(1:G, 0.05*G)

      ref_data1 <- t(sapply(1:G, function(g) {
          dropout <- rbinom(n1, size = 1, prob = 0.4)
          log_ygj <- (1 - dropout) * (beta0g[g] +  beta1g[g] * log(1000000) + rnorm(n1, 0, sdg[g]))
          ygj <- round(exp(log_ygj)-1, 0)
          ygj[ygj < 0] <- 0
          ygj
      }))
      ref_data2 <- t(sapply(1:G, function(g) {
          dropout <- rbinom(n2, size = 1, prob = 0.4)
          log_ygj <- (1 - dropout) * (beta0g[g] +  beta1g[g] * log(1000000) + rnorm(n2, 0, sdg[g]))
          ygj <- round(exp(log_ygj)-1, 0)
          ygj[ygj < 0] <- 0
          ygj
      }))
      ref_data3 <- t(sapply(1:G, function(g) {
          dropout <- rbinom(n3, size = 1, prob = 0.4) # basic zero
          log_ygj <- (1 - dropout) * (beta0g[g] +  beta1g[g] * log(1000000) + rnorm(n3, 0, sdg[g]))
          ygj <- round(exp(log_ygj)-1, 0)
          ygj[ygj < 0] <- 0
          ygj
      }))


      DEg <- sample((1:G)[-highg], 3*G1, replace = FALSE)
      Tri_DE_len <- length(DEg)/3
      amp_g1 <- sample(2:3, Tri_DE_len, replace = TRUE, prob = c(0.8, 0.2))
      amp_g2 <- sample(2:3, Tri_DE_len, replace = TRUE, prob = c(0.8, 0.2))
      amp_g3 <- sample(2:3, Tri_DE_len, replace = TRUE, prob = c(0.8, 0.2))
      ref_data1[DEg[1:Tri_DE_len], ] <- ref_data1[DEg[1:Tri_DE_len], ] * amp_g1
      ref_data2[DEg[(Tri_DE_len+1):(2*Tri_DE_len)], ] <- ref_data2[DEg[(Tri_DE_len+1):(2*Tri_DE_len)], ] * amp_g2
      ref_data3[DEg[(2*Tri_DE_len+1):length(DEg)], ]  <- ref_data3[DEg[(2*Tri_DE_len+1):length(DEg)], ]  * amp_g3

      DEg12 <- DEg[1:(2*Tri_DE_len)]

      ######## s1
      X1 <- runif(n = n1, min = 0.5, max = 5)
      X2 <- runif(n = n2, min = 0.5, max = 5)
      X3 <- runif(n = n3, min = 0.5, max = 5)
      data1 <- t(sapply(1:G, function(g) {
        round(ref_data1[g, ] * X1)
      }))
      data2 <- t(sapply(1:G, function(g) {
        round(ref_data2[g, ] * X2)
      }))
      data3 <- t(sapply(1:G, function(g) {
        round(ref_data3[g, ] * X3)
      }))

      data <- cbind(data1, data2, data3)


      ### QC
      use_genes <- rowSums(data1 > 0) >= 10 & rowSums(data2 > 0) >= 10 & rowSums(data3 > 0) >= 10
      id_use_genes <- which(use_genes == TRUE)
      data <- data[use_genes, ]
      no.genes <- sum(use_genes)

      ### compare the first two groups
      c1 <- c(rep(TRUE, n1),  rep(FALSE, n2),  rep(FALSE, n3))
      c2 <- c(rep(FALSE, n1),  rep(TRUE, n2),  rep(FALSE, n3))
      true_data <- cbind(ref_data1, ref_data2, ref_data3)
      true_data <- true_data[use_genes, ]
      true_fc <- sapply(1:no.genes, function(g) {
        id_c  <- true_data[g, ] > 0
        id_c1 <- id_c & c1
        id_c2 <- id_c & c2
        mean(log2(true_data[g, id_c2])) - mean(log2(true_data[g, id_c1]))
      })

    if (iter >= 1) {
      tryCatch({

      colnames(data) <- paste("Cell_",1:ncol(data), sep="")
      rownames(data) <- paste("Gene_",1:nrow(data), sep="")

      aa0 <- naiveN(as.matrix(data), conditions = rep(1,n))
      aa1 <- LocASN(as.matrix(data), conditions = rep(1,n), gene_num_gezero = 0, bw.method = c("SJ","RoT")[1])
      aa2 <- scranN2(as.matrix(data), clusters = NULL)
      aa3 <- scnormN(as.matrix(data), conditions = rep(1,n))
      aa4 <- sctrnN(as.matrix(data))
      #aa4 <- list(NormalizedData=PsiNorm::PsiNorm(as.matrix(data)))

      bias0 <- biasFC(x = aa0$NormalizedData[,1:n1], y = aa0$NormalizedData[,(n1+1):(n1+n2)], truefc = true_fc)
      bias1 <- biasFC(x = aa1$NormalizedData[,1:n1], y = aa1$NormalizedData[,(n1+1):(n1+n2)], truefc = true_fc)
      bias2 <- biasFC(x = aa2$NormalizedData[,1:n1], y = aa2$NormalizedData[,(n1+1):(n1+n2)], truefc = true_fc)
      bias3 <- biasFC(x = aa3$NormalizedData[,1:n1], y = aa3$NormalizedData[,(n1+1):(n1+n2)], truefc = true_fc)
      bias4 <- biasFC(x = aa4$NormalizedData[,1:n1], y = aa4$NormalizedData[,(n1+1):(n1+n2)], truefc = true_fc)

      mse0 <- mseFC(x = aa0$NormalizedData[,1:n1], y = aa0$NormalizedData[,(n1+1):(n1+n2)], truefc = true_fc)
      mse1 <- mseFC(x = aa1$NormalizedData[,1:n1], y = aa1$NormalizedData[,(n1+1):(n1+n2)], truefc = true_fc)
      mse2 <- mseFC(x = aa2$NormalizedData[,1:n1], y = aa2$NormalizedData[,(n1+1):(n1+n2)], truefc = true_fc)
      mse3 <- mseFC(x = aa3$NormalizedData[,1:n1], y = aa3$NormalizedData[,(n1+1):(n1+n2)], truefc = true_fc)
      mse4 <- mseFC(x = aa4$NormalizedData[,1:n1], y = aa4$NormalizedData[,(n1+1):(n1+n2)], truefc = true_fc)

      ### mast
      deg0 <- mast(counts = aa0$NormalizedData[,1:(n1+n2)], n0 = n1, n1 = n2)
      deg1 <- mast(counts = aa1$NormalizedData[,1:(n1+n2)], n0 = n1, n1 = n2)
      deg2 <- mast(counts = aa2$NormalizedData[,1:(n1+n2)], n0 = n1, n1 = n2)
      deg3 <- mast(counts = aa3$NormalizedData[,1:(n1+n2)], n0 = n1, n1 = n2)
      deg4 <- mast(counts = aa4$NormalizedData[,1:(n1+n2)], n0 = n1, n1 = n2)

      true_deg <- which(id_use_genes %in% DEg12)
      res0 <- prob.TPR.FPR(deg = deg0, fdr_alpha = 0.05, fc = 1.5, true_deg = true_deg)
      res1 <- prob.TPR.FPR(deg = deg1, fdr_alpha = 0.05, fc = 1.5, true_deg = true_deg)
      res2 <- prob.TPR.FPR(deg = deg2, fdr_alpha = 0.05, fc = 1.5, true_deg = true_deg)
      res3 <- prob.TPR.FPR(deg = deg3, fdr_alpha = 0.05, fc = 1.5, true_deg = true_deg)
      res4 <- prob.TPR.FPR(deg = deg4, fdr_alpha = 0.05, fc = 1.5, true_deg = true_deg)

      Naive <- c(Sen = res0$TPR1, Spe = 1 - res0$FPR1, F1 = res0$F1score, bias = bias0, mse = mse0)
      scL   <- c(Sen = res1$TPR1, Spe = 1 - res1$FPR1, F1 = res1$F1score, bias = bias1, mse = mse1)
      scran <- c(Sen = res2$TPR1, Spe = 1 - res2$FPR1, F1 = res2$F1score, bias = bias2, mse = mse2)
      SCn   <- c(Sen = res3$TPR1, Spe = 1 - res3$FPR1, F1 = res3$F1score, bias = bias3, mse = mse3)
      SCT   <- c(Sen = res4$TPR1, Spe = 1 - res4$FPR1, F1 = res4$F1score, bias = bias4, mse = mse4)

      fc_matrix[iter,] <- round(c(Naive, scL, scran, SCn, SCT), 4)

      }, error=function(e){cat("ERROR :",conditionMessage(e), "iter =", iter, "\n")} )
    }


}
colnames(fc_matrix) <- c("Naive_Sen", "Naive_Spe", "Naive_F1", "Naive_bias", "Naive_mse",
                         "scL_Sen", "scL_Spe", "scL_F1", "scL_bias", "scL_mse",
                         "scran_Sen", "scran_Spe", "scran_F1", "scran_bias", "scran_mse",
                         "SCn_Sen", "SCn_Spe", "SCn_F1", "SCn_bias", "SCn_mse",
                         "SCT_Sen", "SCT_Spe", "SCT_F1", "SCT_bias", "SCT_mse")
round(colMeans(fc_matrix, na.rm = TRUE), 4)
round(apply(fc_matrix, 2, sd, na.rm = TRUE), 4)

apply(fc_matrix, 2, median, na.rm=T)

fc_matrix[1:100,]