Commit 3f386707 authored by Marie  BOURDON's avatar Marie BOURDON
Browse files

modif annot vignette

parent 3a8ea8f0
......@@ -114,3 +114,20 @@ head(rqtl_file)
rqtl_file[10,10]
rqtl_file[1:10,1:10]
save.image()
library(stuart)
library(dplyr)
urlfile <- "https://github.com/kbroman/MUGAarrays/blob/master/UWisc/mini_uwisc_v2.csv"
annot_mini <- read.csv(urlfile)
annot_mini <- read.csv(url(urlfile))
annot_mini <- read_csv(url(urlfile))
annot_mini <- read.csv(url("https://github.com/kbroman/MUGAarrays/blob/master/UWisc/mini_uwisc_v2.csv"))
View(annot_mini)
annot_mini <- read.csv(url(urlfile))
View(annot_mini)
annot_mini <- read.csv(url("https://github.com/kbroman/MUGAarrays/blob/master/UWisc/mini_uwisc_v2.csv"))
View(annot_mini)
annot_mini <- read.csv(url("https://raw.githubusercontent.com/kbroman/MUGAarrays/master/UWisc/mini_uwisc_v2.csv"))
View(annot_mini)
rm(urlfile)
View(annot_mini)
View(annot_mini)
......@@ -34,16 +34,19 @@ library(dplyr)
## Annotation files
The developer of Rqtl and Rqtl2 packages, Karl Broman, realised that the annotation of the MUGA arrays was not correct for some markers. Thus, he produced new annotation files for MUGA, miniMUGA, megaMUGA and gigaMUGA arrays. These files contain some informations about the markers: the chromosome and position where the probe of the marker matchs on the genome, wether the marker maps uniquely or not, etc. These files also contains the genetic position of the markers calculated with two methods : "cM_cox" and "cM_g2f1" (see https://kbroman.org/MUGAarrays/mini_revisited.html).
The developer of Rqtl and Rqtl2 packages, Karl Broman, realised that the annotation of the MUGA arrays was not correct for some markers. Thus, he produced new annotation files for MUGA, miniMUGA, megaMUGA and gigaMUGA arrays. These files contain some informations about the markers including the chromosome and position where the probe of the marker matchs on the genome, wether the marker maps uniquely or not, etc. These files also contains the genetic position of the markers calculated with two methods : "cM_cox" and "cM_g2f1" (see https://kbroman.org/MUGAarrays/mini_revisited.html for more informations).
We recommand to use these annotation files to reconstruct the file use for Rqtl analysis. You can load the datasets with these annotations directly with stuaRt package. These versions of the annotation by Karl Broman have less column as only crucial information were kept.
We recommand to use these annotation files to reconstruct the file use for Rqtl analysis. You can load the datasets with these annotations from GitHub (https://github.com/kbroman/MUGAarrays/tree/master/UWisc). Choose the file corresponding to the MUGA array that you used and use the URL to load the dataset in R.
Here, we will present an example of the use of stuaRt with results of a F2 cross genotyped with miniMUGA. We load the annotation file for miniMUGA: `annot_mini`, the result of Neogen genotyping: `genos` and thephenotype dataset produced by the lab: `phenos`. All these datasets are available for example in stuaRt package.
```{r annot}
annot_mini <- read.csv(url("https://raw.githubusercontent.com/kbroman/MUGAarrays/master/UWisc/mini_uwisc_v2.csv"))
```
```{r load}
data(annot_mini)
summary(annot_mini)
data(genos)
summary(genos)
data(phenos)
......
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