Entering data into multiple dataframes - r

Question

I have two loops, the first looks at a specific protein then the second looks at specific cells. In addition I have 16 tables (all named the protein then "_table"). If I specify the cell type I can get the data to enter into the correct row but if I try paste0(temp_TSPAN, "_table") I get the error incorrect number of subscripts on matrix
Any ideas how I can get my loop to specify the correct table?
Here is the second loop which codes for putting data into the tables:
temp_cell <- xCell_cells[i]
print(temp_TSPAN)
print(temp_cell)
temp_means <- c(mean(xCell_Lum_A_Q1[,temp_cell]),mean(xCell_Lum_A_Q2[,temp_cell]),mean(xCell_Lum_A_Q3[,temp_cell]),mean(xCell_Lum_A_Q4[,temp_cell]),
mean(xCell_Lum_B_Q1[,temp_cell]),mean(xCell_Lum_B_Q2[,temp_cell]),mean(xCell_Lum_B_Q3[,temp_cell]),mean(xCell_Lum_B_Q4[,temp_cell]),
mean(xCell_Her_2_Q1[,temp_cell]),mean(xCell_Her_2_Q2[,temp_cell]),mean(xCell_Her_2_Q3[,temp_cell]),mean(xCell_Her_2_Q4[,temp_cell]),
mean(xCell_Basal_Q1[,temp_cell]),mean(xCell_Basal_Q2[,temp_cell]),mean(xCell_Basal_Q3[,temp_cell]),mean(xCell_Basal_Q4[,temp_cell]),
mean(xCell_Normal_Q1[,temp_cell]),mean(xCell_Normal_Q2[,temp_cell]),mean(xCell_Normal_Q3[,temp_cell]),mean(xCell_Normal_Q4[,temp_cell]))
print(temp_means)
paste0(temp_TSPAN, "_table")[temp_cell,] <- temp_means
temp_means <- c()
}
The entire code
library(dplyr)
library(RColorBrewer)
RNA_seq <- read.table("RNASeq2Norm_expr_BCRA.txt", stringsAsFactors = F)
xCell <- read.table("..../xCell_ES_RNAseq.txt", stringsAsFactors = F)
PAM50 <- read.table("..../PAM50_subtypes.txt", stringsAsFactors = F)
TSPANS <- read.table("..../TSPANS.txt", stringsAsFactors = F)
len_TSPAN <- length(TSPANS$V1)
col <- brewer.pal(4, "Pastel1")
xCell_cells <- rownames(xCell)
#Create table for quartile means to be entered
for (i in seq(1,len_TSPAN)){
temp_TSPAN <- TSPANS$V1[i]
print(temp_TSPAN)
assign(as.character((temp_TSPAN)), value = data.frame(Lum_A_Q1_means = rep(NA, 67), Lum_A_Q2_means = rep(NA,67),
Lum_A_Q3_means = rep(NA, 67), Lum_A_Q4_means = rep(NA,67),
Lum_B_Q1_means = rep(NA, 67), Lum_B_Q2_means = rep(NA,67),
Lum_B_Q3_means = rep(NA, 67), Lum_B_Q4_means = rep(NA,67),
Her_2_Q1_means = rep(NA, 67), Her_2_Q2_means = rep(NA,67),
Her_2_Q3_means = rep(NA, 67), Her_2_Q4_means = rep(NA,67),
Basal_Q1_means = rep(NA, 67), Basal_Q2_means = rep(NA,67),
Basal_Q3_means = rep(NA, 67), Basal_Q4_means = rep(NA,67),
Normal_Q1_means = rep(NA, 67), Normal_Q2_means = rep(NA,67),
Normal_Q3_means = rep(NA, 67), Normal_Q4_means = rep(NA,67),
row.names = xCell_cells))
}
temp_TSPAN <- c()
temp_cell <- c()
#Determine which samples belong to each quartile
for (T in seq(1,len_TSPAN)) {
temp_TSPAN <- TSPANS$V1[T]
print(temp_TSPAN)
Lum_A <- RNA_seq[temp_TSPAN, PAM50$subtype == "LumA"]
Lum_A_Quartiles <- quantile(Lum_A[temp_TSPAN,])
Q1_Lum_A <- Lum_A[,(Lum_A[temp_TSPAN,]) <= Lum_A_Quartiles$`25%`]
Q2_Lum_A <- Lum_A[,(Lum_A[temp_TSPAN,]) > Lum_A_Quartiles$`25%`]
Q2_Lum_A <- Q2_Lum_A[,(Q2_Lum_A[temp_TSPAN,]) <= Lum_A_Quartiles$`50%`]
Q3_Lum_A <- Lum_A[,(Lum_A[temp_TSPAN,]) > Lum_A_Quartiles$`50%`]
Q3_Lum_A <- Q3_Lum_A[,(Q3_Lum_A[temp_TSPAN,]) <= Lum_A_Quartiles$`75%`]
Q4_Lum_A <- Lum_A[,(Lum_A[temp_TSPAN,]) > Lum_A_Quartiles$`75%`]
Lum_B <- RNA_seq[temp_TSPAN, PAM50$subtype == "LumB"]
Lum_B_Quartiles <- quantile(Lum_B[temp_TSPAN,])
Q1_Lum_B <- Lum_B[,(Lum_B[temp_TSPAN,]) <= Lum_B_Quartiles$`25%`]
Q2_Lum_B <- Lum_B[,(Lum_B[temp_TSPAN,]) > Lum_B_Quartiles$`25%`]
Q2_Lum_B <- Q2_Lum_B[,(Q2_Lum_B[temp_TSPAN,]) <= Lum_B_Quartiles$`50%`]
Q3_Lum_B <- Lum_B[,(Lum_B[temp_TSPAN,]) > Lum_B_Quartiles$`50%`]
Q3_Lum_B <- Q3_Lum_B[,(Q3_Lum_B[temp_TSPAN,]) <= Lum_B_Quartiles$`75%`]
Q4_Lum_B <- Lum_B[,(Lum_B[temp_TSPAN,]) > Lum_B_Quartiles$`75%`]
Her_2 <- RNA_seq[temp_TSPAN, PAM50$subtype == "Her2"]
Her_2_Quartiles <- quantile(Her_2[temp_TSPAN,])
Q1_Her_2 <- Her_2[,(Her_2[temp_TSPAN,]) <= Her_2_Quartiles$`25%`]
Q2_Her_2 <- Her_2[,(Her_2[temp_TSPAN,]) > Her_2_Quartiles$`25%`]
Q2_Her_2 <- Q2_Her_2[,(Q2_Her_2[temp_TSPAN,]) <= Her_2_Quartiles$`50%`]
Q3_Her_2 <- Her_2[,(Her_2[temp_TSPAN,]) > Her_2_Quartiles$`50%`]
Q3_Her_2 <- Q3_Her_2[,(Q3_Her_2[temp_TSPAN,]) <= Her_2_Quartiles$`75%`]
Q4_Her_2 <- Her_2[,(Her_2[temp_TSPAN,]) > Her_2_Quartiles$`75%`]
Basal <- RNA_seq[temp_TSPAN, PAM50$subtype == "Basal"]
Basal_Quartiles <- quantile(Basal[temp_TSPAN,])
Q1_Basal <- Basal[,(Basal[temp_TSPAN,]) <= Basal_Quartiles$`25%`]
Q2_Basal <- Basal[,(Basal[temp_TSPAN,]) > Basal_Quartiles$`25%`]
Q2_Basal <- Q2_Basal[,(Q2_Basal[temp_TSPAN,]) <= Basal_Quartiles$`50%`]
Q3_Basal <- Basal[,(Basal[temp_TSPAN,]) > Basal_Quartiles$`50%`]
Q3_Basal <- Q3_Basal[,(Q3_Basal[temp_TSPAN,]) <= Basal_Quartiles$`75%`]
Q4_Basal <- Basal[,(Basal[temp_TSPAN,]) > Basal_Quartiles$`75%`]
Normal <- RNA_seq[temp_TSPAN, PAM50$subtype == "Normal"]
Normal_Quartiles <- quantile(Normal[temp_TSPAN,])
Q1_Normal <- Normal[,(Normal[temp_TSPAN,]) <= Normal_Quartiles$`25%`]
Q2_Normal <- Normal[,(Normal[temp_TSPAN,]) > Normal_Quartiles$`25%`]
Q2_Normal <- Q2_Normal[,(Q2_Normal[temp_TSPAN,]) <= Normal_Quartiles$`50%`]
Q3_Normal <- Normal[,(Normal[temp_TSPAN,]) > Normal_Quartiles$`50%`]
Q3_Normal <- Q3_Normal[,(Q3_Normal[temp_TSPAN,]) <= Normal_Quartiles$`75%`]
Q4_Normal <- Normal[,(Normal[temp_TSPAN,]) > Normal_Quartiles$`75%`]
Lum_A_Q1_samples <- colnames(Q1_Lum_A)
Lum_A_Q2_samples <- colnames(Q2_Lum_A)
Lum_A_Q3_samples <- colnames(Q3_Lum_A)
Lum_A_Q4_samples <- colnames(Q4_Lum_A)
Lum_B_Q1_samples <- colnames(Q1_Lum_B)
Lum_B_Q2_samples <- colnames(Q2_Lum_B)
Lum_B_Q3_samples <- colnames(Q3_Lum_B)
Lum_B_Q4_samples <- colnames(Q4_Lum_B)
Her_2_Q1_samples <- colnames(Q1_Her_2)
Her_2_Q2_samples <- colnames(Q2_Her_2)
Her_2_Q3_samples <- colnames(Q3_Her_2)
Her_2_Q4_samples <- colnames(Q4_Her_2)
Basal_Q1_samples <- colnames(Q1_Basal)
Basal_Q2_samples <- colnames(Q2_Basal)
Basal_Q3_samples <- colnames(Q3_Basal)
Basal_Q4_samples <- colnames(Q4_Basal)
Normal_Q1_samples <- colnames(Q1_Normal)
Normal_Q2_samples <- colnames(Q2_Normal)
Normal_Q3_samples <- colnames(Q3_Normal)
Normal_Q4_samples <- colnames(Q4_Normal)
#Finding enrichment scores for the samples in each quartile
xCell_Lum_A_Q1 <- t(xCell[,Lum_A_Q1_samples])
xCell_Lum_A_Q2 <- t(xCell[,Lum_A_Q2_samples])
xCell_Lum_A_Q3 <- t(xCell[,Lum_A_Q3_samples])
xCell_Lum_A_Q4 <- t(xCell[,Lum_A_Q4_samples])
xCell_Lum_B_Q1 <- t(xCell[,Lum_B_Q1_samples])
xCell_Lum_B_Q2 <- t(xCell[,Lum_B_Q2_samples])
xCell_Lum_B_Q3 <- t(xCell[,Lum_B_Q3_samples])
xCell_Lum_B_Q4 <- t(xCell[,Lum_B_Q4_samples])
xCell_Her_2_Q1 <- t(xCell[,Her_2_Q1_samples])
xCell_Her_2_Q2 <- t(xCell[,Her_2_Q2_samples])
xCell_Her_2_Q3 <- t(xCell[,Her_2_Q3_samples])
xCell_Her_2_Q4 <- t(xCell[,Her_2_Q4_samples])
xCell_Basal_Q1 <- t(xCell[,Basal_Q1_samples])
xCell_Basal_Q2 <- t(xCell[,Basal_Q2_samples])
xCell_Basal_Q3 <- t(xCell[,Basal_Q3_samples])
xCell_Basal_Q4 <- t(xCell[,Basal_Q4_samples])
xCell_Normal_Q1 <- t(xCell[,Normal_Q1_samples])
xCell_Normal_Q2 <- t(xCell[,Normal_Q2_samples])
xCell_Normal_Q3 <- t(xCell[,Normal_Q3_samples])
xCell_Normal_Q4 <- t(xCell[,Normal_Q4_samples])
len_xCell <- length(xCell_cells)
temp_means <- c()
for (i in seq(1, len_xCell)){
temp_cell <- xCell_cells[i]
print(temp_TSPAN)
print(temp_cell)
temp_means <- c(mean(xCell_Lum_A_Q1[,temp_cell]),mean(xCell_Lum_A_Q2[,temp_cell]),mean(xCell_Lum_A_Q3[,temp_cell]),mean(xCell_Lum_A_Q4[,temp_cell]),
mean(xCell_Lum_B_Q1[,temp_cell]),mean(xCell_Lum_B_Q2[,temp_cell]),mean(xCell_Lum_B_Q3[,temp_cell]),mean(xCell_Lum_B_Q4[,temp_cell]),
mean(xCell_Her_2_Q1[,temp_cell]),mean(xCell_Her_2_Q2[,temp_cell]),mean(xCell_Her_2_Q3[,temp_cell]),mean(xCell_Her_2_Q4[,temp_cell]),
mean(xCell_Basal_Q1[,temp_cell]),mean(xCell_Basal_Q2[,temp_cell]),mean(xCell_Basal_Q3[,temp_cell]),mean(xCell_Basal_Q4[,temp_cell]),
mean(xCell_Normal_Q1[,temp_cell]),mean(xCell_Normal_Q2[,temp_cell]),mean(xCell_Normal_Q3[,temp_cell]),mean(xCell_Normal_Q4[,temp_cell]))
print(temp_means)
nm1 <- temp_TSPAN, "_table")
assign(nm1, `[<-`(get(nm1), get(nm1)[temp_cell,], temp_means))
temp_means <- c()
}
}
Sample data
> dput(head(TSPANS))
structure(list(V1 = c("TSPAN1", "TSPAN3", "TSPAN4", "TSPAN6",
"TSPAN8", "TSPAN9")), row.names = c(NA, 6L), class = "data.frame")
> dput(head(PAM50))
structure(list(Sample_ID = c("TCGA.3C.AAAU.01A.11R.A41B.07",
"TCGA.3C.AALI.01A.11R.A41B.07", "TCGA.3C.AALJ.01A.31R.A41B.07",
"TCGA.3C.AALK.01A.11R.A41B.07", "TCGA.4H.AAAK.01A.12R.A41B.07",
"TCGA.5L.AAT0.01A.12R.A41B.07"), subtype = c("LumB", "Her2",
"LumB", "Her2", "LumB", "LumA")), row.names = c("1", "2", "3",
"4", "5", "6"), class = "data.frame")
> dput(xCell[1:5, 1:5])
structure(list(TCGA.3C.AAAU.01A.11R.A41B.07 = c(0.0182278777214451,
0, 0, 0.00312390016077943, 0.136068543973221), TCGA.3C.AALI.01A.11R.A41B.07 = c(0.282595778602895,
0, 0.0600603500818251, 0.0589537608635649, 0.205506668589802),
TCGA.3C.AALJ.01A.31R.A41B.07 = c(0.18283171431184, 0.0941680866198556,
0.146150110122777, 0.0304405814585031, 8.9658687089931e-20
), TCGA.3C.AALK.01A.11R.A41B.07 = c(0.134145304728982, 0.032112973032126,
0.154386799682783, 0, 4.17812708486922e-20), TCGA.4H.AAAK.01A.12R.A41B.07 = c(0.106111324096064,
0.0121130054841642, 0.191944288358642, 0, 0.125099426066817
)), row.names = c("aDC", "Adipocytes", "Astrocytes", "B-cells",
"Basophils"), class = "data.frame")
> dput(RNA_seq[1:5, 1:5])
structure(list(TCGA.3C.AAAU.01A.11R.A41B.07 = c(197.0897, 0,
0, 102.9634, 1.3786), TCGA.3C.AALI.01A.11R.A41B.07 = c(237.3844,
0, 0, 70.8646, 4.3502), TCGA.3C.AALJ.01A.31R.A41B.07 = c(423.2366,
0.9066, 0, 161.2602, 0), TCGA.3C.AALK.01A.11R.A41B.07 = c(191.0178,
0, 0, 62.5072, 1.6549), TCGA.4H.AAAK.01A.12R.A41B.07 = c(268.8809,
0.4255, 3.8298, 154.3702, 3.4043)), row.names = c("A1BG", "A1CF",
"A2BP1", "A2LD1", "A2ML1"), class = "data.frame")
> dput(head(TSPAN1_table))
structure(list(Lum_A_Q1_means = c(NA, NA, NA, NA, NA, NA), Lum_A_Q2_means = c(NA,
NA, NA, NA, NA, NA), Lum_A_Q3_means = c(NA, NA, NA, NA, NA, NA
), Lum_A_Q4_means = c(NA, NA, NA, NA, NA, NA), Lum_B_Q1_means = c(NA,
NA, NA, NA, NA, NA), Lum_B_Q2_means = c(NA, NA, NA, NA, NA, NA
), Lum_B_Q3_means = c(NA, NA, NA, NA, NA, NA), Lum_B_Q4_means = c(NA,
NA, NA, NA, NA, NA), Her_2_Q1_means = c(NA, NA, NA, NA, NA, NA
), Her_2_Q2_means = c(NA, NA, NA, NA, NA, NA), Her_2_Q3_means = c(NA,
NA, NA, NA, NA, NA), Her_2_Q4_means = c(NA, NA, NA, NA, NA, NA
), Basal_Q1_means = c(NA, NA, NA, NA, NA, NA), Basal_Q2_means = c(NA,
NA, NA, NA, NA, NA), Basal_Q3_means = c(NA, NA, NA, NA, NA, NA
), Basal_Q4_means = c(NA, NA, NA, NA, NA, NA), Normal_Q1_means = c(NA,
NA, NA, NA, NA, NA), Normal_Q2_means = c(NA, NA, NA, NA, NA,
NA), Normal_Q3_means = c(NA, NA, NA, NA, NA, NA), Normal_Q4_means = c(NA,
NA, NA, NA, NA, NA)), row.names = c("aDC", "Adipocytes", "Astrocytes",
"B-cells", "Basophils", "CD4+ memory T-cells"), class = "data.frame")

Answer 1

We need to get the value with get an assign using assign
temp_cell <- xCell_cells[i]
print(temp_TSPAN)
print(temp_cell)
temp_means <- c(mean(xCell_Lum_A_Q1[,temp_cell]),mean(xCell_Lum_A_Q2[,temp_cell]),mean(xCell_Lum_A_Q3[,temp_cell]),mean(xCell_Lum_A_Q4[,temp_cell]),
mean(xCell_Lum_B_Q1[,temp_cell]),mean(xCell_Lum_B_Q2[,temp_cell]),mean(xCell_Lum_B_Q3[,temp_cell]),mean(xCell_Lum_B_Q4[,temp_cell]),
mean(xCell_Her_2_Q1[,temp_cell]),mean(xCell_Her_2_Q2[,temp_cell]),mean(xCell_Her_2_Q3[,temp_cell]),mean(xCell_Her_2_Q4[,temp_cell]),
mean(xCell_Basal_Q1[,temp_cell]),mean(xCell_Basal_Q2[,temp_cell]),mean(xCell_Basal_Q3[,temp_cell]),mean(xCell_Basal_Q4[,temp_cell]),
mean(xCell_Normal_Q1[,temp_cell]),mean(xCell_Normal_Q2[,temp_cell]),mean(xCell_Normal_Q3[,temp_cell]),mean(xCell_Normal_Q4[,temp_cell]))
print(temp_means)
nm1 <- temp_TSPAN, "_table")
assign(nm1, `[<-`(get(nm1), get(nm1)[temp_cell,], temp_means))
temp_means <- c()
}
May be the OP is looking for simplified version with
lapply(split(RNA_seq, setNames(PAM50$subtype, PAM50$Sample_ID)[colnames(RNA_seq)]),
function(dat) apply(dat, 1, function(x) {
qnt <- quantile(x)
data.frame(val = names(x), grp = names(qnt)[findInterval(x, qnt)])
apply(xCell[, names(x)], 2, function(y) tapply(y, names(x), FUN = mean))
}))