please help generate a square correlation matrix

in each column, less than 10% values are 1, most of them are 0;

so I want to remove a  row with value of zero in both columns when calculate correlation between two columns.

If I have understood the request, I'm not sure that omitting all 0
pairs for each pair of columns makes much sense, but be that as it
may, here's another way to do it by using the 'FUN' argument of combn
to encapsulate any calculations that you do. I just use cor() as the
calculation -- you can use anything you like that takes two vectors of
0's and 1's and produces fixed length numeric results (or fromm which
you can extract such).

I encapsulated it all in a little function. Note that I first
converted the data frame to a matrix. Because of their generality,
data frames carry a lot of extra baggage that can slow purely numeric
manipulations down.

Anyway, here's the function, 'somecors' (I'm a bad name picker :(  ! )

   somecors <- function(dat, func = cor){
      dat <- as.matrix(dat)
      indx <- seq_len(ncol(dat))
         combn(indx, 2, FUN = \(z) {
            i <- z[1]; j <- z[2]
            k <- dat[, i ] | dat[, j ]
            c(z,func(dat[k,i ], dat[k,j ]))
         })
   }

Results come out as a matrix with combn(ncol(dat),2) columns, the
first 2 rows giving the pair of column numbers for each column,and
then 1 or more rows (possibly extracted) from whatever func you use.
Here's the results for your data formatted to 2 decimal places:

round(somecors(dat),2)

     [,1]  [,2]  [,3]  [,4] [,5]  [,6]
[1,]  1.0  1.00  1.00  2.00    2  3.00
[2,]  2.0  3.00  4.00  3.00    4  4.00
[3,] -0.5 -0.41 -0.35 -0.41   NA -0.47
Warning message:
In func(dat[k, i], dat[k, j]) : the standard deviation is zero

The NA and warning comes in the 2,4 pair of columns because after
removing all zero rows in the pair, dat[,4] is all 1's, giving a zero
in the denominator of the cor() calculation -- again, assuming I have
correctly understood your request. If so, this might be something you
need to worry about.

Again, feel free to ignore if  I have misinterpreterd or this does not suit.

Cheers,
Bert


On Thu, Jul 25, 2024 at 2:01?PM Rui Barradas <ruipbarradas at sapo.pt> wrote:

?s 20:47 de 25/07/2024, Yuan Chun Ding escreveu:

Hi Rui,

You are always very helpful!! Thank you,

I just modified your R codes to remove a row with zero values in both column pair as below for my real data.

Ding

dat<-gene22mut.coded
r <- P <- matrix(NA, nrow = 22L, ncol = 22L,
                  dimnames = list(names(dat), names(dat)))

for(i in 1:22) {
   #i=1
   x <- dat[[i]]
   for(j in (1:22)) {
     #j=2
     if(i == j) {
       # there's nothing to test, assign correlation 1
       r[i, j] <- 1
     } else {
       tmp <-cbind(x,dat[[j]])
       row0 <-rowSums(tmp)
       tem2 <-tmp[row0!=0,]
       tmp3 <- cor.test(tem2[,1],tem2[,2])
       r[i, j] <- tmp3$estimate
       P[i, j] <- tmp3$p.value
     }
   }
}
r<-as.data.frame(r)
P<-as.data.frame(P)

From: R-help <r-help-bounces at r-project.org> On Behalf Of Yuan Chun Ding via R-help
Sent: Thursday, July 25, 2024 11:26 AM
To: Rui Barradas <ruipbarradas at sapo.pt>; r-help at r-project.org
Subject: Re: [R] please help generate a square correlation matrix

HI Rui, Thank you for the help! You did not remove a row if zero values exist in both column pair, right? Ding From: Rui Barradas <ruipbarradas@?sapo.?pt> Sent: Thursday, July 25, 2024 11:?15 AM To: Yuan Chun Ding <ycding@?coh.?org>;


HI Rui,



Thank you for the  help!



You did not remove a row if zero values exist in both column pair, right?



Ding



From: Rui Barradas <ruipbarradas at sapo.pt<mailto:ruipbarradas at sapo.pt>>

Sent: Thursday, July 25, 2024 11:15 AM

To: Yuan Chun Ding <ycding at coh.org<mailto:ycding at coh.org>>; r-help at r-project.org<mailto:r-help at r-project.org>

Subject: Re: [R] please help generate a square correlation matrix



?s 17:?39 de 25/07/2024, Yuan Chun Ding via R-help escreveu: > Hi R users, > > I generated a square correlation matrix for the dat dataframe below; > dat<-data.?frame(g1=c(1,0,0,1,1,1,0,0,0), > g2=c(0,1,0,1,0,1,1,0,0), > g3=c(1,1,0,0,0,1,0,0,0),





?s 17:39 de 25/07/2024, Yuan Chun Ding via R-help escreveu:

Hi R users,

I generated a square correlation matrix for the dat dataframe below;

dat<-data.frame(g1=c(1,0,0,1,1,1,0,0,0),

                  g2=c(0,1,0,1,0,1,1,0,0),

                  g3=c(1,1,0,0,0,1,0,0,0),

                  g4=c(0,1,0,1,1,1,1,1,0))

library("Hmisc")

dat.rcorr = rcorr(as.matrix(dat))

dat.r <-round(dat.rcorr$r,2)

however, I want to modify this correlation calculation;

my dat has more than 1000 rows and 22 columns;

in each column, less than 10% values are 1, most of them are 0;

so I want to remove a  row with value of zero in both columns when calculate correlation between two columns.

I just want to check whether those values of 1 are correlated between two columns.

Please look at my code in the following;

cor.4gene <-matrix(0,nrow=4*4, ncol=4)

for (i in 1:4){

    #i=1

    for (j in 1:4) {

      #j=1

      d <-dat[,c(i,j)]%>%

        filter(eval(as.symbol(colnames(dat)[i]))!=0 |

                 eval(as.symbol(colnames(dat)[j]))!=0)

      c <-cor.test(d[,1],d[,2])

      cor.4gene[i*j,]<-c(colnames(dat)[i],colnames(dat)[j],

                          c$estimate,c$p.value)

    }

}

cor.4gene<-as.data.frame(cor.4gene)%>%filter(V1 !=0)

colnames(cor.4gene)<-c("gene1","gene2","cor","P")

Can you tell me what mistakes I made?

first, why cor is NA when calculation of correlation for g1 and g1, I though it should be 1.

cor.4gene$cor[is.na(cor.4gene$cor)]<-1

cor.4gene$cor[is.na(cor.4gene$P)]<-0

cor.4gene.sq <-pivot_wider(cor.4gene, names_from = gene1, values_from = cor)

Then this line of code above did not generate a square matrix as what the HMisc library did.

How to fix my code?

Thank you,

Ding

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Hello,







You are complicating the code, there's no need for as.symbol/eval, the



column numbers do exactly the same.







# create the two results matrices beforehand



r <- P <- matrix(NA, nrow = 4L, ncol = 4L, dimnames = list(names(dat),



names(dat)))







for(i in 1:4) {



    x <- dat[[i]]



    for(j in (1:4)) {



      if(i == j) {



        # there's nothing to test, assign correlation 1



        r[i, j] <- 1



      } else {



        tmp <- cor.test(x, dat[[j]])



        r[i, j] <- tmp$estimate



        P[i, j] <- tmp$p.value



      }



    }



}







# these two results are equal up to floating-point precision



dat.rcorr$r



#>           g1        g2        g3        g4



#> g1 1.0000000 0.1000000 0.3162278 0.1581139



#> g2 0.1000000 1.0000000 0.3162278 0.6324555



#> g3 0.3162278 0.3162278 1.0000000 0.0000000



#> g4 0.1581139 0.6324555 0.0000000 1.0000000



r



#>           g1        g2           g3           g4



#> g1 1.0000000 0.1000000 3.162278e-01 1.581139e-01



#> g2 0.1000000 1.0000000 3.162278e-01 6.324555e-01



#> g3 0.3162278 0.3162278 1.000000e+00 1.355253e-20



#> g4 0.1581139 0.6324555 1.355253e-20 1.000000e+00







# these two results are equal up to floating-point precision



dat.rcorr$P



#>           g1         g2        g3         g4



#> g1        NA 0.79797170 0.4070838 0.68452834



#> g2 0.7979717         NA 0.4070838 0.06758329



#> g3 0.4070838 0.40708382        NA 1.00000000



#> g4 0.6845283 0.06758329 1.0000000         NA



P



#>           g1         g2        g3         g4



#> g1        NA 0.79797170 0.4070838 0.68452834



#> g2 0.7979717         NA 0.4070838 0.06758329



#> g3 0.4070838 0.40708382        NA 1.00000000



#> g4 0.6845283 0.06758329 1.0000000         NA











You can put these two results in a list, like Hmisc::rcorr does.







lst_rcorr <- list(r = r, P = P)











Hope this helps,







Rui Barradas



















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Hello,

Here are two other ways.

The first is equivalent to your long format attempt.


library(tidyverse)

dat %>%
   names() %>%
   expand.grid(., .) %>%
   apply(1L, \(x) {
     tmp <- dat[rowSums(dat[x]) > 0, ]
     tmp2 <- cor.test(tmp[[ x[1L] ]], tmp[[ x[2L] ]])
     c(tmp2$estimate, P = tmp2$p.value)
   }) %>%
   t() %>%
   as.data.frame() %>%
   cbind(tmp_df, .) %>%
   na.omit()


The second is, in my opinion the one that makes more sense. If you see
the results, cor is symmetric (as it should) so the calculations are
repeated. If you only run the cor.tests on the combinations of
names(dat) by groups of 2, it will save a lot of work. But the output is
a much smaller a data.frame.


cbind(
   combn(names(dat), 2L) %>%
     t() %>%
     as.data.frame(),
   combn(dat, 2L, FUN = \(d) {
     d2 <- d[rowSums(d) > 0, ]
     tmp2 <- cor.test(d2[[1L]], d2[[2L]])
     c(tmp2$estimate, P = tmp2$p.value)
   }) %>% t()
) %>% na.omit()



Hope this helps,

Rui Barradas