Improve speed of 3 for loops in R - r

Question

I am working with a matrix set_onco of 206 rows x 196 cols and I have a vector, genes_100 (it's a matrix but I take only the first col), with 101 names.
here's a snippet of how they look
> set_onco[1:10,1:10]
V2 V3 V4 V5 V6 V7 V8 V9 V10 V11
GLI1_UP.V1_DN COPZ1 C10orf46 C20orf118 TMEM181 CCNL2 YIPF1 GTDC1 OPN3 RSAD2 SLC22A1
GLI1_UP.V1_UP IGFBP6 HLA-DQB1 CCND2 PTH1R TXNDC12 M6PR PPT2 STAU1 IGJ TMOD3
E2F1_UP.V1_DN TGFB1I1 CXCL5 POU5F1 SAMD10 KLF2 STAT6 ENTPD6 VCAN HMGCS1 ANXA8
E2F1_UP.V1_UP RRP1B HES1 ADCY6 CHAF1B VPS37B GRSF1 TLX2 SSX2IP DNA2 CMA1
EGFR_UP.V1_DN NPY1R PDZK1 GFRA1 GREB1 MSMB DLC1 MYB SLC6A14 IFI44 IFI44L
EGFR_UP.V1_UP FGG GBP1 TNFRSF11B FGB GJA1 DUSP6 S100A9 ADM ITGB6 DUSP4
ERB2_UP.V1_DN NPY1R PDZK1 ANXA3 GREB1 HSPB8 DLC1 NRIP1 FHL2 EGR3 IFI44
FAM18B1
ERB2_UP.V1_UP CYP1A1 CEACAM5 FAM129A TNFRSF11B DUSP4 CYP1B1 UPK2 DAB2 CEACAM6 KIAA1199
GCNP_SHH_UP_EARLY.V1_DN SRRM2 KIAA1217 DEFA1 DLK1 PITX2 CCL2 UPK3B SEZ6 TAF15 EMP1
genes_100[1:10,1]
[1] AL591845.1 B3GALT6 RAP1GAP HSPG2 BX293535.1 RP1-159A19.1 IFI6 FAM76A FAM176B CSF3R
101 Levels: 5_8S_rRNA AC018470.1 AC091179.2 AC103702.3 AC138972.1 ACVR1B AL049829.5 AL137797.2 AL139260.2 AL450326.2 AL591845.1 AL607122.2 B3GALT6 BX293535.1 ... ZNF678
what I want to do is to parse through the matrix and count the frequency at which each row contains the names in genes_100
to do that I created 3 for loops: the first one moves down one row at the time, the second one moves into the row and the third one loops over the list genes_100 checking for matches.
at the end I save in a matrix how many times genes_100 matched with the terms in each row, saving also the row names from the matrix (so that I know which one is which)
the code works and gives me the correct output...but it's just really slow!!
a snippet of the output is:
head(result_matrix_100)
freq_100
[1,] "GLI1_UP.V1_DN" "0"
[2,] "GLI1_UP.V1_UP" "0"
[3,] "E2F1_UP.V1_DN" "0"
[4,] "E2F1_UP.V1_UP" "0"
[5,] "EGFR_UP.V1_DN" "0"
[6,] "EGFR_UP.V1_UP" "0"
I used system.time() and I get:
user system elapsed
525.38 0.06 530.34
which is way too slow since I have even bigger matrices to parse, and in some cases I have to repeat this 10k times!!!
the code is:
result_matrix_100 <- matrix(nrow=0, ncol=2)
for (q in seq(1,nrow(set_onco),1)) {
for (j in seq(1, length(set_onco[q,]),1)) {
for (x in seq(1,101,1)) {
if (as.character(genes_100[x,1]) == as.character(set_onco[q,j])) {
freq_100 <- freq_100+1
}
}
}
result_matrix_100 <- rbind(result_matrix_100, cbind(row.names(set_onco)[q], freq_100))
}
what would you suggest?
thanks in advance :)

Answer 1

#joran's will possibly be faster although it may not be "factor-safe". Your set_onco values are probably encoded as factor variables (because your genes_100 object clearly is.) This will be safer:
set_onco[] <- lapply(set_onco, as.character)
# that converts a data.frame with factor columns to character valued
# at that point #joran's solution could be used safely
freq100 <- apply(set_onco, 1, function(x) sum(x %in% genes_100) )
# that does a row-by-row count of the number of matches to genes_100
freq100
GLI1_UP.V1_DN GLI1_UP.V1_UP E2F1_UP.V1_DN
0 0 0
E2F1_UP.V1_UP EGFR_UP.V1_DN EGFR_UP.V1_UP
0 0 0
ERB2_UP.V1_DN ERB2_UP.V1_UP GCNP_SHH_UP_EARLY.V1_DN
0 0 0
The size of your dataset (206 rows x 196 cols) is quite small so this will be virtually immediate. These dput statements and output can be used to construct what I think your objects look like internally:
dput(set_onco)
structure(list(V2 = structure(c(1L, 4L, 8L, 6L, 5L, 3L, 5L, 2L,
7L), .Label = c("COPZ1", "CYP1A1", "FGG", "IGFBP6", "NPY1R",
"RRP1B", "SRRM2", "TGFB1I1"), class = "factor"), V3 = structure(c(1L,
6L, 3L, 5L, 8L, 4L, 8L, 2L, 7L), .Label = c("C10orf46", "CEACAM5",
"CXCL5", "GBP1", "HES1", "HLA-DQB1", "KIAA1217", "PDZK1"), class = "factor"),
V4 = structure(c(3L, 4L, 8L, 1L, 7L, 9L, 2L, 6L, 5L), .Label = c("ADCY6",
"ANXA3", "C20orf118", "CCND2", "DEFA1", "FAM129A", "GFRA1",
"POU5F1", "TNFRSF11B"), class = "factor"), V5 = structure(c(7L,
5L, 6L, 1L, 4L, 3L, 4L, 8L, 2L), .Label = c("CHAF1B", "DLK1",
"FGB", "GREB1", "PTH1R", "SAMD10", "TMEM181", "TNFRSF11B"
), class = "factor"), V6 = structure(c(1L, 8L, 5L, 9L, 6L,
3L, 4L, 2L, 7L), .Label = c("CCNL2", "DUSP4", "GJA1", "HSPB8",
"KLF2", "MSMB", "PITX2", "TXNDC12", "VPS37B"), class = "factor"),
V7 = structure(c(8L, 6L, 7L, 5L, 3L, 4L, 3L, 2L, 1L), .Label = c("CCL2",
"CYP1B1", "DLC1", "DUSP6", "GRSF1", "M6PR", "STAT6", "YIPF1"
), class = "factor"), V8 = structure(c(2L, 5L, 1L, 7L, 3L,
6L, 4L, 8L, 9L), .Label = c("ENTPD6", "GTDC1", "MYB", "NRIP1",
"PPT2", "S100A9", "TLX2", "UPK2", "UPK3B"), class = "factor"),
V9 = structure(c(4L, 8L, 9L, 7L, 6L, 1L, 3L, 2L, 5L), .Label = c("ADM",
"DAB2", "FHL2", "OPN3", "SEZ6", "SLC6A14", "SSX2IP", "STAU1",
"VCAN"), class = "factor"), V10 = structure(c(8L, 6L, 4L,
2L, 5L, 7L, 3L, 1L, 9L), .Label = c("CEACAM6", "DNA2", "EGR3",
"HMGCS1", "IFI44", "IGJ", "ITGB6", "RSAD2", "TAF15"), class = "factor"),
V11 = structure(c(8L, 9L, 1L, 2L, 6L, 3L, 5L, 7L, 4L), .Label = c("ANXA8",
"CMA1", "DUSP4", "EMP1", "IFI44", "IFI44L", "KIAA1199", "SLC22A1",
"TMOD3"), class = "factor")), .Names = c("V2", "V3", "V4",
"V5", "V6", "V7", "V8", "V9", "V10", "V11"), class = "data.frame", row.names = c("GLI1_UP.V1_DN",
"GLI1_UP.V1_UP", "E2F1_UP.V1_DN", "E2F1_UP.V1_UP", "EGFR_UP.V1_DN",
"EGFR_UP.V1_UP", "ERB2_UP.V1_DN", "ERB2_UP.V1_UP", "GCNP_SHH_UP_EARLY.V1_DN"
))
dput(factor(genes_100) )
structure(c(1L, 2L, 9L, 7L, 3L, 10L, 8L, 6L, 5L, 4L), .Label = c("AL591845.1",
"B3GALT6", "BX293535.1", "CSF3R", "FAM176B", "FAM76A", "HSPG2",
"IFI6", "RAP1GAP", "RP1-159A19.1"), class = "factor")

Answer 2

Something like this will probably be quite fast:
#Sample data
m <- matrix(sample(letters,206*196,replace = TRUE),206,196)
genes_100 <- letters[1:5]
m1 <- matrix(m %in% genes_100,206,196)
rowSums(m1)