| Type: | Package |
| Title: | A 'Sparklyr' Extension for 'VariantSpark' |
| Version: | 0.1.1 |
| Maintainer: | Samuel Macêdo <samuelmacedo@recife.ifpe.edu.br> |
| Description: | This is a 'sparklyr' extension integrating 'VariantSpark' and R. 'VariantSpark' is a framework based on 'scala' and 'spark' to analyze genome datasets, see https://bioinformatics.csiro.au/. It was tested on datasets with 3000 samples each one containing 80 million features in either unsupervised clustering approaches and supervised applications, like classification and regression. The genome datasets are usually writing in VCF, a specific text file format used in bioinformatics for storing gene sequence variations. So, 'VariantSpark' is a great tool for genome research, because it is able to read VCF files, run analyses and return the output in a 'spark' data frame. |
| License: | Apache License 2.0 | file LICENSE |
| LazyData: | true |
| Imports: | sparklyr (≥ 1.0.1) |
| RoxygenNote: | 6.1.1 |
| Suggests: | testthat |
| Encoding: | UTF-8 |
| NeedsCompilation: | no |
| Packaged: | 2019-06-11 23:30:12 UTC; dmmad |
| Author: | Samuel Macêdo [aut, cre], Javier Luraschi [aut] |
| Repository: | CRAN |
| Date/Publication: | 2019-06-13 16:20:03 UTC |
Extract the importance data frame
Description
This function extracts the importance data frame from the Importance Analysis jobj.
Usage
importance_tbl(importance, name = "importance_tbl")
Arguments
importance |
A jobj from the class |
name |
The name to assign to the copied table in Spark. |
Examples
## Not run:
library(sparklyr)
sc <- spark_connect(master = "local")
vsc <- vs_connect(sc)
hipster_vcf <- vs_read_vcf(vsc,
system.file("extdata/hipster.vcf.bz2",
package = "variantspark"))
labels <- vs_read_labels(vsc,
system.file("extdata/hipster_labels.txt",
package = "variantspark"))
importance <- vs_importance_analysis(vsc, hipster_vcf, labels, 10)
importance_tbl(importance)
## End(Not run)
Display sample names
Description
This function display the first N variant names.
Usage
sample_names(vcf_source, n_samples = NULL)
Arguments
vcf_source |
An object with |
n_samples |
The number os samples to display. |
Value
spark_jobj, shell_jobj
Examples
## Not run:
library(sparklyr)
sc <- spark_connect(master = "local")
vsc <- vs_connect(sc)
hipster_vcf <- vs_read_vcf(vsc,
system.file("extdata/hipster.vcf.bz2",
package = "variantspark"))
sample_names(hipster_vcf, 3)
## End(Not run)
Creating a variantspark connection
Description
You need to create a variantspark connection to use this extension. To do this,
you pass as argument a spark connection that you can create
using sparklyr::spark_connect().
Usage
vs_connect(sc)
Arguments
sc |
A spark connection. |
Value
A variantspark connection
Examples
library(sparklyr)
sc <- spark_connect(master = "spark://HOST:PORT")
connection_is_open(sc)
vsc <- vs_connect(sc)
spark_disconnect(sc)
Importance Analysis
Description
This function performs an Importance Analysis using random forest algorithm. For more details, please look at here.
Usage
vs_importance_analysis(vsc, vcf_source, labels, n_trees)
Arguments
vsc |
A variantspark connection. |
vcf_source |
An object with |
labels |
An object with |
n_trees |
The number of trees using in the random forest. |
Value
spark_jobj, shell_jobj
Examples
## Not run:
library(sparklyr)
sc <- spark_connect(master = "local")
vsc <- vs_connect(sc)
hipster_vcf <- vs_read_vcf(vsc,
system.file("extdata/hipster.vcf.bz2",
package = "variantspark"))
labels <- vs_read_labels(vsc,
system.file("extdata/hipster_labels.txt",
package = "variantspark"))
vs_importance_analysis(vsc, hipster_vcf, labels, 10)
## End(Not run)
Reading a CSV file
Description
The vs_read_csv() reads a CSV file format and returns a jobj
object from CsvFeatureSource scala class.
Usage
vs_read_csv(vsc, path)
Arguments
vsc |
A variantspark connection. |
path |
The file's path. |
Value
spark_jobj, shell_jobj
Examples
## Not run:
library(sparklyr)
sc <- spark_connect(master = "local")
vsc <- vs_context(sc)
hipster_labels <- vs_read_csv(vsc,
system.file("extdata/hipster_labels.txt",
package = "variantspark"))
hipster_labels
## End(Not run)
Reading labels
Description
This function reads only the label column of a CSV file and returns a jobj
object from CsvLabelSource scala class.
Usage
vs_read_labels(vsc, path, label = "label")
Arguments
vsc |
A variantspark connection. |
path |
The file's path. |
label |
A string with the label column name. |
Value
spark_jobj, shell_jobj
Examples
## Not run:
library(sparklyr)
sc <- spark_connect(master = "local")
vsc <- vs_context(sc)
labels <- vs_read_labels(vsc,
system.file("extdata/hipster_labels.txt",
package = "variantspark"))
labels
## End(Not run)
Reading a VCF file
Description
The Variant Call Format (VCF) specifies the format of a text file used in
bioinformatics for storing gene sequence variations. The format has been developed
with the advent of large-scale genotyping and DNA sequencing projects, such as
the 1000 Genomes Project. The vs_read_vcf() reads this format and returns
a jobj object from VCFFeatureSource scala class.
Usage
vs_read_vcf(vsc, path)
Arguments
vsc |
A variantspark connection. |
path |
The file's path. |
Value
spark_jobj, shell_jobj
Examples
## Not run:
library(sparklyr)
sc <- spark_connect(master = "local")
vsc <- vs_context(sc)
hipster_vcf <- vs_read_vcf(vsc,
system.file("extdata/hipster.vcf.bz2",
package = "variantspark"))
hipster_vcf
## End(Not run)