hapResult summary and visualization
Once we have the hapResult object, can we summarize and
visualize our hapResult by interact with annotations and
phenotypes.
Summary hapResult
Now, we have the hapResult object with INFOs we want
display in next step. The hap_summary() function convert
the object of hapResult class, which is a long table
format, into a short table belong to hapSummary class. In
hapResult each row represent a accession, while each row
represents a hap in hapSummary.
hapSummary <- hap_summary(hapResult)
hapSummary##
## Accssions: 33
## Sites: 9
## Indels: 5
## SNPs: 4
##
## Haplotypes: 9
## H001 10 C8, C9, C11, C14, C18, C25, ...
## H002 8 C5, C15, C17, C19, C22, C32, ...
## H003 4 C6, C20, C23, C37
## H004 4 C7, C13, C24, C30
## H005 2 C4, C21
## H006 2 C10, C27
## H007 1 C1
## H008 1 C2
## H009 1 C3
##
## Options:
## hapPrefix: H
## CHROM: scaffold_1
## POS: 4300-6856
## hyb_remove: YES
## NA_remove: YES
##
## # A tibble: 13 × 12
## Hap `4300` `4345` `4879` `4910` `4950` `5037` `5209` `5213` `6856`
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 CHR scaffold_1 scaff… scaff… scaff… scaff… scaff… scaff… scaff… scaff…
## 2 POS 4300 4345 4879 4910 4950 5037 5209 5213 6856
## 3 INFO PrChange=C->D… PrCha… PrCha… PrCha… PrCha… PrCha… PrCha… PrCha… PrCha…
## 4 ALLELE G/C T/A,GG T/A,G GCCTA… T/AA,… A/AA,… A/AC C/G A/G
## 5 H001 G T T GCCTA T A A C A
## 6 H002 C T T A T A A G A
## 7 H003 G A A A AA AA AC G A
## 8 H004 G T T A T A A G A
## 9 H005 G T G GCCTA T GGG A C A
## 10 H006 G T T A T A A G G
## 11 H007 C GG G A GG CC A G A
## 12 H008 G GG T A T CC A G G
## 13 H009 G T T A GG GGG A G G
## # … with 2 more variables: Accession <chr>, freq <int>Visualize haplotye as table
Let’s see how to visualization of our haplotype results.
At first let’s display the hapSummary as a table. In
this table like figure we can see all the variants and their positions,
haplotypes and their frequencies.
plotHapTable(hapSummary)
Also we can add an annotation, “CDSChange”, to the table by assign
the INFO_tag. It’s your responsibility to verify whether
the INFO_tag was existed in the INFO field.
# add one annotation
plotHapTable(hapSummary,
hapPrefix = "H",
INFO_tag = "CDSChange",
tag_name = "CDS",
displayIndelSize = 1,
angle = 45,
replaceMultiAllele = TRUE,
ALLELE.color = "grey90")
Now let’s add another INFO_tag named as “PrChange”.
# add multi annotation
plotHapTable(hapSummary,
hapPrefix = "H",
INFO_tag = c("CDSChange", "PrChange"),
displayIndelSize = 1,
angle = 45,
replaceMultiAllele = TRUE,
ALLELE.color = "grey90")
Parameter tag_name was used to replace the character if
INFO_tag was too long.
# add multi annotation
plotHapTable(hapSummary,
hapPrefix = "H",
INFO_tag = c("CDSChange", "PrChange"),
tag_name = c("CDS", "Pr"),
displayIndelSize = 1,
angle = 45,
replaceMultiAllele = TRUE,
ALLELE.color = "grey90")
Display variations on gene model.
I think it’s a good idea to figure out where are the variants by marking them on gene model.
displayVarOnGeneModel(gff,
hapSummary,
Chr = "scaffold_1",
startPOS = 4300, endPOS = 7910,
type = "pin", cex = 0.7,
CDS_h = 0.05, fiveUTR_h = 0.02, threeUTR_h = 0.01)
hapNet calculation and visualization
The hapNet could be generated from object of
hapSummary class. The accession group information could be
attached in this step.
hapNet <- get_hapNet(hapSummary,
AccINFO = AccINFO,
groupName = "Type")Once we have the hapNet object, we can plot it with
‘R’.
# plot haploNet
plotHapNet(hapNet,
size = "freq", # circle size
scale = "log2", # scale circle with 'log10(size + 1)'
cex = 0.8, # size of hap symbol
col.link = 2, # link colors
link.width = 2, # link widths
show.mutation = 2, # mutation types one of c(0,1,2,3)
legend = c(-13,-2)) # legend position
Phenotype association analysis
Finally, let’s see which haplotype has superiority at particular area by interact with phynotype.
Here are two options, merged or separated, to organized the heatmap
of p-values and violin plot. The figure as an object of
ggplot2, which means user could add/modified figure
elements with ggplot2.
Here is an example for merged arrangement:
results <-hapVsPheno(hapResult,
hapPrefix = "H",
title = "This is title",
mergeFigs = TRUE,
pheno = pheno,
phenoName = "GrainWeight.2021",
minAcc = 3)
plot(results$figs)
An example for separated plot:
results <- hapVsPheno(hap = hapResult,
hapPrefix = "H",
title = "This is title",
pheno = pheno,
phenoName = "GrainWeight.2021",
minAcc = 3,
mergeFigs = FALSE)
plot(results$fig_pvalue)
plot(results$fig_Violin)
Association analysis of multi-phenotypes once a time
I believe the function of hapVsPhenos() will be useful
when you have a lot of phenotype need to be associated with haplotype
results.
Note that: the pheno name will be added between the file name and surfix.
hapVsPhenos(hapResult,
pheno,
outPutSingleFile = TRUE,
hapPrefix = "H",
title = "Seita.0G000000",
file = "mypheno.tiff",
width = 12,
height = 8,
res = 300)