Hello,

 

I will be very obliged if someone can help me with this statistical R
problem:

I am trying to do a Pearson correlation on my datasets X, Y with
randomization test. My X and Y datasets are pairs.

1.     I want to randomize (rearrange) only my X dataset per row ,while
keeping the my Y dataset as it is. 

2.     Then Calculate the correlation  for this pair, and compare it to
your true value of correlation. 

3.     Repeat 2 and 3 maybe a 100 times

4.     If your true p-value  is greater than 95% of the random values,
then you can reject the null hypothesis at   p<0.05.

 

I am stuck at the randomization step. I need some help in implementing
it the appropriate randomization step in my correlation.

Below is my incomplete code. I will be very obliged if someone could
help:

 

X <- read.table("X.txt",as.is=T,header=T,row.names=1)

Y <- read.table("Y.txt",as.is=T,header=T,row.names=1)

 

X.mat<- as.matrix(X)

Y.mat<- as.matrix(Y)

 

Corrs<- cor.test(X.mat[1,],Y.mat[1,],alternative =c("greater"),method=
c("pearson"))

 

Corrs.rand <- list()

 

for (i in 1:length(X.mat)){

for (j in 1:100){

 

# This doesnot seem to wrok correctly. How do I run sample function 100
times for the same row?

 

SNP.rand<- sample(SNP.mat[i,],56, replace = FALSE, prob = NULL)

Corrs.rand[[j]]<- cor.test(SNP.rand,CNV.mat[j,],alternative
=c("greater"),method= c("pearson"))

 

# need to calculate how many times my pvalue from true p-value> random
pvalue

}

}

 

X dataset:

 

#Probes

X10851

X12144

X12155

X11882

X10860

X12762

X12239

X12154

1

1

1

0

0

1

0

2

0

2

0

0

0

0

0

0

0

0

3

2

2

2

2

1

2

1

2

4

0

0

0

0

0

0

0

0

5

2

2

2

2

2

2

2

2

6

0

1

0

0

1

1

1

1

7

2

2

NaN

2

2

2

2

2

8

2

2

2

2

2

2

2

2

9

0

1

0

1

1

NaN

1

2

10

2

2

2

2

2

2

2

2

11

2

0

0

0

0

0

0

0

12

0

1

0

1

1

0

1

1

 

Y dataset:

Probes

X10851

X12144

X12155

X11882

X10860

X12762

X12239

X12154

1

793.0831

788.1814

867.8504

729.8321

816.852

805.2114

774.599

854.6384

2

12.8695

4.312894

10.69769

5.872213

13.793

9.394133

6.297553

9.307943

3

699.7792

826.9974

795.641

770.9376

806.1241

782.397

817.1075

859.7155

4

892.8217

869.0481

806.3387

812.0431

873.5565

794.4752

813.9587

814.8681

5

892.8217

869.0481

806.3387

812.0431

873.5565

794.4752

813.9587

814.8681

6

839.735

943.4456

950.7575

859.0208

894.246

853.5241

941.4842

913.0246

7

653.1272

751.5218

750.1758

737.3821

757.8486

758.2407

724.2186

770.8669

8

12.8695

4.312894

10.69769

5.872213

13.793

9.394133

6.297553

9.307943

9

839.735

943.4456

950.7575

859.0208

894.246

853.5241

941.4842

913.0246

10

653.1272

751.5218

750.1758

737.3821

757.8486

758.2407

724.2186

770.8669

11

653.1272

751.5218

750.1758

737.3821

757.8486

758.2407

724.2186

770.8669

12

839.735

943.4456

950.7575

859.0208

894.246

853.5241

941.4842

913.0246

 

 

 

Thanks in advance

 

Manisha

 

Mount Sinai School of Medicine

Icahn Medical Institute, 

1425 Madison Avenue, Box 1498

NY-10029, NEW-YORK, USA

 


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