geneHapR workfolw
R Markdown
Workflow
Haplotype analysis with geneHapR begins from
variant call format file (VCF) or DNA
sequences file (FASTA).
Annotation file, phenotype data (data.frame) and accession group information (data.frame) are conditionally needed but strongly also recommend.
The below workflow outlines the major steps involved in
geneHapR usage.
geneHapR workfolw
The blue fonts indicate functions the user need to become familiar with. There are effectively three phases in the workflow: firstly, import data; secondly, processing the objects in memory; and finally, visualization and export the results. Import large data set frequently presents a bottleneck due to restriction of hardware performance and insufficient computer memory. Fortunately, it is only a matter of time to import a large data set due to the former limitation. It’s suggest that perform haplotype analysis with a comparatively small range every time and then merge the results to get over the latter limitation.
Once the data sets are import into memory, we can continue the
process with geneHapR. There are three small steps in data processing.
First, we calculate haplotype result (an object of
hapResult class) from VCF (object of vcfR
class) or DNA Sequences (object of DNAStringSet class).
Next, we summarize the hap result (generate an object of
hapSummary class) into a shorter table. Finally, we compute
the haplo network (generate an object of haploNet class)
from summarized haplotpe result (suggest with accession groups
information).
Finally, we can visualize and export the results. Display variants on
gene model with R base graphics package takes an object of
hapSummary class and an object of Granges
class contains genome annotations. . Visualization of hapTable with
ggplot2 package takes an object of hapSummary
class. HapNet Visualization with R base graphics package takes an object
of haploNet class. Visualization of association between hap
and phenotype with ggplot2 package takes an object of
hapResult class and an object of data.frame
contains phenotype data. Certainly, the user is able to make some
modifications to the plots with command of ggplot2 or R
base graphics packages.
Note that the hap results (object of
hapResult and hapSummary class) are able to
export to drive or import from drive by write.hap() or
import_hap().