problems reading map positions in a 4way cross.

[joe....@gmail.com]

I'm having trouble with read.cross() for type "4way", reading from a 'csvr' file. I get the error

1: In summary.cross(cross) :
The genetic maps should all be matrices with two rows.

Currently, I have my data formatted so that;
Col1 = markername
Col2 = chromosome id
Col3 = male map position
Col4 = female map position

(sample below)

But clearly this is not what read.cross() expects. I am unable to find any examples of a 'correctly formatted' 4way cross anywhere, and write.cross() will not allow the export of 4way crosses to csv format, so I can't see the correct way to format this so that read.cross() can read it as a matrix with two rows.

Any help would be appreciated.

test <- read.cross('csvr','.', 'test.txt', genotypes=NULL, estimate.map=FALSE, crosstype="4way")

head test.txt
id,,,,a,b,c,d,e,f
sex,,,,0,1,1,1,1,1,0,1,0,0,1,1,0,0,0
phe,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
CHR2_288112,2,0.000,0.000,1,4,3,2,1,4
CHR2_334258,2,1.093,0.000,2,4,3,2,1,4
CHR2_334268,2,1.300,0.000,2,4,3,2,1,4
CHR2_455684,2,3.195,0.000,2,4,3,2,1,4
CHR2_517129,2,3.195,0.000,2,4,3,2,1,4
CHR2_740302,2,4.663,0.000,2,4,3,2,1,4

[Karl Broman]

It will only take a single set of sex-averaged positions. You’re unfortunately on your own to get sex-specific positions in there.

sorry,
karl

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[joe....@gmail.com]

Hi Karl, sorry but I don't understand your response. You say it will only take a single set of sex-averaged positions, but giving just one column still gives the error

Warning messages:

1: In summary.cross(cross) :
  The genetic maps should all be matrices with two rows.

and if you try to run calc.genoprob(), the error message

Error in map[1, ] : incorrect number of dimensions

so clearly it is expecting a matrix of two points. I can see that this is not achieved by having an extra column in the csvr file, but if not like that, how does read.cross() expect the data to be formatted so that it is a matrix with two rows?

[Karl Broman]

This is a bug. At the stage where the genetic map is supposed to be turned into a two-row matrix, it thinks the data are from cross type "f2" and so doesn't fix things properly.

A temporary fix is to read in the data with read.cross and then do:

for(i in seq_along(test$geno)) {
test$geno[[i]]$map <- rbind(test$geno[[i]]$map, test$geno[[i]]$map)
}

karl

[joe....@gmail.com]

Karl, you're a legend! Thanks so much for the fix.

[Noemie VT]

Hi Karl,

I am having a similar problem with reading in my 4way cross, however the temporary fix does not seem to work for me. 

Here is the first few lines and column of my input

wei_nov17 trevally000070_2842923 trevally000070_9235894 trevally000070_9932163 trevally000070_1677914 trevally000070_4785258

1 1 1 1 1

0 1.714 2.279 2.844 3.409

39 BD BD BD BD BD

115 BD BD BD BD BD

86 BC BC BC BC BC

138 AD AD AD AD AD

179 AC AC AC AC AC

144 AD AD AD AD AD

Any idea what is going wrong?

[Karl Broman]

Can you give more information about your problem? What code are you using to read the file, and what errors are you getting?

karl

To view this discussion on the web visit https://groups.google.com/d/msgid/rqtl-disc/55228c27-7bc3-4387-9209-7df31c4d2423n%40googlegroups.com.

[Noemie VT]

Of course!

This is how I read the data:

tre  <- read.cross(format = "csv", "path",

                  "hgt25_nov17.csv", na.strings=c("NA"),  genotypes=c("AC","AD","BC","BD"),

                  estimate.map = F, crosstype="4way")

And this is the message I get:

--Read the following data: 

 89 individuals 

 1830 markers 

 1 phenotypes 

 --Cross type: 

4way 

 Warning message: In summary.cross(cross) : The genetic maps should all be matrices with two rows.

What's funny is that this input file work a few months ago, I've just gone back to get some info I forgot to write down and it looks like something got corrupted in the mean time..

Let me know if you'd like me to send you the full file

Thanks for the help

Noemie

[Karl Broman]

Yes I would need to see the data file to figure out what the problem is.

What version of R and R/qtl are you using?

karl

[Karl Broman]

I’ll take a look, but I’d definitely recommending upgrading both R (now 4.0.4) and R/qtl (now 1.47-9). The problem earlier in this thread was something that got fixed in the more recent version of the package.

karl