R/data.R
In Alice-MacQueen/switchgrassGWAS: Genome Wide Association on Panicum virgatum
#' Phenotypes available for the Panicum virgatum diversity panel.
#'
#' A dataset containing phenotypes for the Panicum virgatum diversity panel.
#' The variables are as follows:
#'
#' \itemize{
#' \item PLANT_ID. ID of the Panicum virgatum genotype sequenced in the
#' diversity panel.
#' \item GWAS_CT. The number of times that genotype was clonally propagated
#' and planted at these common gardens.
#' \item MAT. Mean annual temperature at the genotype's location of origin.
#' \item bio17. Precipitation in the driest quarter at the genotype's location
#' of origin.
#' \item bio4. Temperature seasonality at the genotype's location of origin.
#' \item bio16. Precipitation in the wettest quarter at the genotype's
#' location of origin.
#' \item AHM. Annual heat moisture index, calculated as (MAT+10)/(MAP/1000),
#' at the genotype's location of origin.
#' \item bio2. Mean diurnal temperature range at the genotype's location of
#' origin.
#' \item bio5. Max temperature of the warmest month at the genotype's location
#' of origin.
#' \item FRAC_SRV_THREE. Fraction of individuals surviving winter 2018/2019
#' at three northern gardens (BRKG, LINC, CLMB).
#' \item CLMB_BIOMASS. Biomass at the end of the 2019 season in Columbia,
#' Missouri.
#' \item KBSM_BIOMASS. Biomass at the end of the 2019 season in Hickory
#' Corners, Michigan (Kellog Biological Station).
#' \item PKLE_BIOMASS. Biomass at the end of the 2019 season in Austin, Texas
#' (Pickle Research Campus).
#' }
#'
#' @name pvdiv_phenotypes
#' @docType data
#' @author Alice MacQueen \email{alice.macqueen@@utexas.edu}
#' @references \url{https://www.nature.com/articles/s41586-020-03127-1}
#' @keywords data
#' @usage data(pvdiv_phenotypes)
#' @format A data frame with 1114 rows and 13 variables
NULL
#' GFF3 Gene information
#'
#' A dataset containing the gene information in GFF3 format.
#' The variables are as follows:
#'
#' \itemize{
#' \item seqid. name of the chromosome or scaffold; here, of Panicum virgatum.
#' \item source. name of the program that generated this feature.
#' \item type. type of feature. Term or accession from the SOFA sequence ontology.
#' \item start. Start position of the feature, with sequence numbering starting at 1.
#' \item end. End position of the feature, with sequence numbering starting at 1.
#' \item score. A floating point value.
#' \item strand. defined as + (forward) or - (reverse).
#' \item phase. One of '0', '1' or '2'. '0' indicates that the first base of the feature is the first base of a codon, '1' that the second base is the first base of a codon, and so on.
#' \item attributes. A semicolon-separated list of tag-value pairs, providing additional information about each feature. Some of these tags are predefined, e.g. ID, Name, Alias, Parent.
#' }
#'
#' @name gff_gene
#' @docType data
#' @author Alice MacQueen \email{alice.macqueen@@utexas.edu}
#' @keywords data
#' @usage data(gff_gene)
#' @format A data frame with 1236881 rows and 9 variables
NULL
#' Metadata lines for the gene GFF3
#'
#' A dataset containing the three metadata lines for the gff3 file gff_gene.
#'
#' @name gff_metadata
#' @docType data
#' @author Alice MacQueen \email{alice.macqueen@@utexas.edu}
#' @keywords data
#' @usage data(gff_metadata)
#' @format A data frame with 3 rows and 1 variable.
NULL
#' Annotation information from Pvirgatum_516_v5.1.annotation_info
#'
#' A dataset containing the v5.1 annotation information for Panicum virgatum.
#' The variables are as follows:
#'
#' \itemize{
#' \item pacID ID of the Panicum virgatum gene from the annotation file.
#' \item locusName The gene name from Panicum virgatum. Only distinct locus
#' names are included in this dataset.
#' \item transcriptName The transcript name from P. virgatum. Only the first
#' transcript variant is included in this dataset.
#' \item peptideName The protein name for the variant. Only the first protein
#' variant is included in this dataset.
#' \item Pfam
#' \item Panther Panther categories associated with this locus.
#' \item KOG assignments from the euKaryotic Orthologous Groups (KOG) database
#' \item ec EC numbers in Enzyme Nomenclature.
#' \item KO KEGG Orthology (KO) numbers associated with this locusName.
#' \item GO Gene ontology entries associated with this locusName.
#' \item Best-hit-arabi-name Best Arabidopsis thaliana homolog locus name.
#' \item arabi-symbol Best A. thaliana homolog gene name.
#' \item arabi-defline Best A. thaliana homolog gene annotation.
#' \item Best-hit-rice-name Best rice homolog locus name.
#' \item rice-symbol Best rice homolog gene name.
#' \item rice-defline Best rice homolog gene annotation.
#' }
#'
#' @name anno_info
#' @docType data
#' @author Alice MacQueen \email{alice.macqueen@@utexas.edu}
#' @references \url{https://www.nature.com/articles/s41586-020-03127-1}
#' @keywords data
#' @usage data(anno_info)
#' @format A data frame with 80278 rows and 16 variables.
NULL
#' Oeco ggplot theme
#'
#' A list containing the oeco ggplot2 theme for use in cdbn_standard_gwas and
#' mash_plot_ ... functions.
#'
#' @name theme_oeco
#' @docType data
#' @author Alice MacQueen \email{alice.macqueen@@utexas.edu}
#' @references \url{https://www.nature.com/articles/s41586-020-03127-1}
#' @keywords data
#' @usage data(theme_oeco)
#' @format A list containing ggplot2 specifications
NULL
#' Metadata table for the Panicum virgatum diversity panel
#'
#' A dataset containing the collected metadata information for individuals in
#' the Panicum virgatum diversity panel.
#' The variables are as follows:
#'
#' \itemize{
#' \item PLANT_ID Individual plant name. Unique to each plant. The column we
#' should be using for analyses.
#' \item SUBPOP Using clustering from an additive kinship matrix, which
#' genetic subpopulation does the PLANT_ID belong to? 4X and 8X
#' individuals have been assigned a ploidy by flow cytometry, but not a
#' subpopulation by sequencing, as of the end of 2020.
#' \item LIBRARY Four letter library code used for sequencing. IF NA, the
#' plant has not been sequenced as of the end of 2020.
#' \item GENETIC_SUBPOP_KINSHIP Using clustering from an additive kinship
#' matrix, which genetic subpopulation does the PLANT_ID belong to? NA
#' individuals were not included in the kinship matrix.
#' \item KINSHIP_ORDER Order of individuals in an additive kinship matrix.
#' Roughly speaking, closer numbers represent more similar individuals.
#' \item GENETIC_SUBPOP_50PER From hierarchical k=3 analysis done early 2020.
#' Which genetic subpopulation does the PLANT_ID belong to, at 50%
#' assignment or more. Admixed individuals are unassigned PLANT_IDs
#' included in the analysis; NA individuals were not included.
#' \item GENETIC_SUBPOP_95PER From hierarchical k=3 analysis done early 2020.
#' Which genetic subpopulation does the PLANT_ID belong to, at 95%
#' assignment or more. Admixed individuals are unassigned PLANT_IDs
#' included in the analysis; NA individuals were not included.
#' \item GULF_Q From hierarchical k=3 analysis done early 2020. Structure Q
#' value for assignment to the Gulf genetic subpopulation. NA individuals
#' were not included in the Structure analysis.
#' \item ATLANTIC_Q From hierarchical k=3 analysis done winter 2020. Structure
#' Q value for assignment to the Atlantic subpopulation. NA individuals
#' were not included in the Structure analysis.
#' \item MIDWEST_DA From hierarchical k=3 analysis done winter 2020. DAPC
#' value for assignment to the Midwest subpopulation. NA individuals were
#' not included in the DAPC analysis.
#' \item ECOTYPE From John's phenotypic analysis of Ecotypes. Which phenotypic
#' group does the PLANT_ID belong to, at 50% assignment or more. NA
#' individuals did not have phenotypic data for this classification.
#' \item ECOTYPE_NNET From John's neural network extending ecotype
#' classification to individuals without phenotypic data.
#' \item ECOTYPE_PC1 From John's phenotypic analysis of ecotypes done summer
#' 2019. The first principle component separating individuals into
#' ecotypes.
#' \item UPLAND_ASSIGNMENT From John's phenotypic analysis of Ecotypes done
#' summer 2019. Assignment to the Upland ecotype.
#' \item LOWLAND_ASSIGNMENT From John's phenotypic analysis of Ecotypes done
#' summer 2019. Assignment to the Texas ecotype.
#' \item COASTAL_ASSIGNMENT: From John's phenotypic analysis of Ecotypes done
#' summer 2019. Assignment to the Coastal ecotype.
#' \item LIB_GROWN For tetraploid plants with sequencing libraries, was the
#' PLANT_ID grown at one or more of the 10 common garden sites in 2018?
#' "Y" = yes, "N" = no. Does not include plants where LIB_PHENO is "N".
#' \item LIB_BIOCLIM Do we have bioclim variables for this PLANT_ID?
#' "Y" = yes, "N" = no. Does not include plants where LIB_CLIMATE is "N".
#' \item LIB_CLIMATE John & Jason's decision on whether this PLANT_ID should
#' be used for climate analyses done summer 2019. "Y" = yes, "N" = no.
#' \item LIB_PHENO John & Jason's decision on whether this PLANT_ID should be
#' used for phenotypic analyses done summer 2019. "Y" = yes, "N" = no.
#' \item LIB_ACC John & Jason's decision on whether this PLANT_ID should be
#' used for analyses of a single plant per location done summer 2019 .
#' "Y" = yes, "N" = no.
#' \item LIB_NOTES Notes on John & Jason's decisions for the above 3 columns.
#' This analysis was done summer 2019.
#' \item PLOIDY Ploidy as determined by both sequencing & flow cytometry
#' analyses done in 2019. Typically 4X (tetraploid) or 8X (octoploid).
#' \item COLLECTION_TYPE Original plant collection metadata. What kind of
#' collection was this? Breeding selection, Natural collection, Cultivar.
#' \item COLLECTION_METHOD How was the plant collected?
#' \item COLLECTOR Who collected this plant?
#' \item STATE State of the United States that the plant was collected from.
#' \item COUNTY County that the plant was collected from.
#' \item LATITUDE Latitude of origin of the collected plant.
#' \item LONGITUDE Longitude of origin of the collected plant.
#' \item ELEVATION Elevation of origin of the collected plant.
#' \item NOTE_LATLONG Notes on exactness of latitude & longitude of collection
#' \item LOCALITY Additional details on plant collection location.
#' \item TAXON Genus & species of the collected plant.
#' \item HABITAT Details on habitat of plant collection location, if known.
#' \item COLL_DATE Information on plant collection date, if known.
#' \item PLANTED_2018 All plants clonally replicated and transplanted into
#' one or more common garden in the summer of 2018.
#' }
#'
#' @name pvdiv_metadata
#' @docType data
#' @author Alice MacQueen \email{alice.macqueen@@utexas.edu}
#' @references \url{https://www.nature.com/articles/s41586-020-03127-1}
#' @keywords data
#' @usage data(pvdiv_metadata)
#' @format A data frame with 1347 rows and 37 variables.
NULL
Alice-MacQueen/switchgrassGWAS documentation built on Jan. 23, 2022, 7:55 p.m.
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#' Phenotypes available for the Panicum virgatum diversity panel.
#'
#' A dataset containing phenotypes for the Panicum virgatum diversity panel.
#' The variables are as follows:
#'
#' \itemize{
#' \item PLANT_ID. ID of the Panicum virgatum genotype sequenced in the
#' diversity panel.
#' \item GWAS_CT. The number of times that genotype was clonally propagated
#' and planted at these common gardens.
#' \item MAT. Mean annual temperature at the genotype's location of origin.
#' \item bio17. Precipitation in the driest quarter at the genotype's location
#' of origin.
#' \item bio4. Temperature seasonality at the genotype's location of origin.
#' \item bio16. Precipitation in the wettest quarter at the genotype's
#' location of origin.
#' \item AHM. Annual heat moisture index, calculated as (MAT+10)/(MAP/1000),
#' at the genotype's location of origin.
#' \item bio2. Mean diurnal temperature range at the genotype's location of
#' origin.
#' \item bio5. Max temperature of the warmest month at the genotype's location
#' of origin.
#' \item FRAC_SRV_THREE. Fraction of individuals surviving winter 2018/2019
#' at three northern gardens (BRKG, LINC, CLMB).
#' \item CLMB_BIOMASS. Biomass at the end of the 2019 season in Columbia,
#' Missouri.
#' \item KBSM_BIOMASS. Biomass at the end of the 2019 season in Hickory
#' Corners, Michigan (Kellog Biological Station).
#' \item PKLE_BIOMASS. Biomass at the end of the 2019 season in Austin, Texas
#' (Pickle Research Campus).
#' }
#'
#' @name pvdiv_phenotypes
#' @docType data
#' @author Alice MacQueen \email{alice.macqueen@@utexas.edu}
#' @references \url{https://www.nature.com/articles/s41586-020-03127-1}
#' @keywords data
#' @usage data(pvdiv_phenotypes)
#' @format A data frame with 1114 rows and 13 variables
NULL
#' GFF3 Gene information
#'
#' A dataset containing the gene information in GFF3 format.
#' The variables are as follows:
#'
#' \itemize{
#' \item seqid. name of the chromosome or scaffold; here, of Panicum virgatum.
#' \item source. name of the program that generated this feature.
#' \item type. type of feature. Term or accession from the SOFA sequence ontology.
#' \item start. Start position of the feature, with sequence numbering starting at 1.
#' \item end. End position of the feature, with sequence numbering starting at 1.
#' \item score. A floating point value.
#' \item strand. defined as + (forward) or - (reverse).
#' \item phase. One of '0', '1' or '2'. '0' indicates that the first base of the feature is the first base of a codon, '1' that the second base is the first base of a codon, and so on.
#' \item attributes. A semicolon-separated list of tag-value pairs, providing additional information about each feature. Some of these tags are predefined, e.g. ID, Name, Alias, Parent.
#' }
#'
#' @name gff_gene
#' @docType data
#' @author Alice MacQueen \email{alice.macqueen@@utexas.edu}
#' @keywords data
#' @usage data(gff_gene)
#' @format A data frame with 1236881 rows and 9 variables
NULL
#' Metadata lines for the gene GFF3
#'
#' A dataset containing the three metadata lines for the gff3 file gff_gene.
#'
#' @name gff_metadata
#' @docType data
#' @author Alice MacQueen \email{alice.macqueen@@utexas.edu}
#' @keywords data
#' @usage data(gff_metadata)
#' @format A data frame with 3 rows and 1 variable.
NULL
#' Annotation information from Pvirgatum_516_v5.1.annotation_info
#'
#' A dataset containing the v5.1 annotation information for Panicum virgatum.
#' The variables are as follows:
#'
#' \itemize{
#' \item pacID ID of the Panicum virgatum gene from the annotation file.
#' \item locusName The gene name from Panicum virgatum. Only distinct locus
#' names are included in this dataset.
#' \item transcriptName The transcript name from P. virgatum. Only the first
#' transcript variant is included in this dataset.
#' \item peptideName The protein name for the variant. Only the first protein
#' variant is included in this dataset.
#' \item Pfam
#' \item Panther Panther categories associated with this locus.
#' \item KOG assignments from the euKaryotic Orthologous Groups (KOG) database
#' \item ec EC numbers in Enzyme Nomenclature.
#' \item KO KEGG Orthology (KO) numbers associated with this locusName.
#' \item GO Gene ontology entries associated with this locusName.
#' \item Best-hit-arabi-name Best Arabidopsis thaliana homolog locus name.
#' \item arabi-symbol Best A. thaliana homolog gene name.
#' \item arabi-defline Best A. thaliana homolog gene annotation.
#' \item Best-hit-rice-name Best rice homolog locus name.
#' \item rice-symbol Best rice homolog gene name.
#' \item rice-defline Best rice homolog gene annotation.
#' }
#'
#' @name anno_info
#' @docType data
#' @author Alice MacQueen \email{alice.macqueen@@utexas.edu}
#' @references \url{https://www.nature.com/articles/s41586-020-03127-1}
#' @keywords data
#' @usage data(anno_info)
#' @format A data frame with 80278 rows and 16 variables.
NULL
#' Oeco ggplot theme
#'
#' A list containing the oeco ggplot2 theme for use in cdbn_standard_gwas and
#' mash_plot_ ... functions.
#'
#' @name theme_oeco
#' @docType data
#' @author Alice MacQueen \email{alice.macqueen@@utexas.edu}
#' @references \url{https://www.nature.com/articles/s41586-020-03127-1}
#' @keywords data
#' @usage data(theme_oeco)
#' @format A list containing ggplot2 specifications
NULL
#' Metadata table for the Panicum virgatum diversity panel
#'
#' A dataset containing the collected metadata information for individuals in
#' the Panicum virgatum diversity panel.
#' The variables are as follows:
#'
#' \itemize{
#' \item PLANT_ID Individual plant name. Unique to each plant. The column we
#' should be using for analyses.
#' \item SUBPOP Using clustering from an additive kinship matrix, which
#' genetic subpopulation does the PLANT_ID belong to? 4X and 8X
#' individuals have been assigned a ploidy by flow cytometry, but not a
#' subpopulation by sequencing, as of the end of 2020.
#' \item LIBRARY Four letter library code used for sequencing. IF NA, the
#' plant has not been sequenced as of the end of 2020.
#' \item GENETIC_SUBPOP_KINSHIP Using clustering from an additive kinship
#' matrix, which genetic subpopulation does the PLANT_ID belong to? NA
#' individuals were not included in the kinship matrix.
#' \item KINSHIP_ORDER Order of individuals in an additive kinship matrix.
#' Roughly speaking, closer numbers represent more similar individuals.
#' \item GENETIC_SUBPOP_50PER From hierarchical k=3 analysis done early 2020.
#' Which genetic subpopulation does the PLANT_ID belong to, at 50%
#' assignment or more. Admixed individuals are unassigned PLANT_IDs
#' included in the analysis; NA individuals were not included.
#' \item GENETIC_SUBPOP_95PER From hierarchical k=3 analysis done early 2020.
#' Which genetic subpopulation does the PLANT_ID belong to, at 95%
#' assignment or more. Admixed individuals are unassigned PLANT_IDs
#' included in the analysis; NA individuals were not included.
#' \item GULF_Q From hierarchical k=3 analysis done early 2020. Structure Q
#' value for assignment to the Gulf genetic subpopulation. NA individuals
#' were not included in the Structure analysis.
#' \item ATLANTIC_Q From hierarchical k=3 analysis done winter 2020. Structure
#' Q value for assignment to the Atlantic subpopulation. NA individuals
#' were not included in the Structure analysis.
#' \item MIDWEST_DA From hierarchical k=3 analysis done winter 2020. DAPC
#' value for assignment to the Midwest subpopulation. NA individuals were
#' not included in the DAPC analysis.
#' \item ECOTYPE From John's phenotypic analysis of Ecotypes. Which phenotypic
#' group does the PLANT_ID belong to, at 50% assignment or more. NA
#' individuals did not have phenotypic data for this classification.
#' \item ECOTYPE_NNET From John's neural network extending ecotype
#' classification to individuals without phenotypic data.
#' \item ECOTYPE_PC1 From John's phenotypic analysis of ecotypes done summer
#' 2019. The first principle component separating individuals into
#' ecotypes.
#' \item UPLAND_ASSIGNMENT From John's phenotypic analysis of Ecotypes done
#' summer 2019. Assignment to the Upland ecotype.
#' \item LOWLAND_ASSIGNMENT From John's phenotypic analysis of Ecotypes done
#' summer 2019. Assignment to the Texas ecotype.
#' \item COASTAL_ASSIGNMENT: From John's phenotypic analysis of Ecotypes done
#' summer 2019. Assignment to the Coastal ecotype.
#' \item LIB_GROWN For tetraploid plants with sequencing libraries, was the
#' PLANT_ID grown at one or more of the 10 common garden sites in 2018?
#' "Y" = yes, "N" = no. Does not include plants where LIB_PHENO is "N".
#' \item LIB_BIOCLIM Do we have bioclim variables for this PLANT_ID?
#' "Y" = yes, "N" = no. Does not include plants where LIB_CLIMATE is "N".
#' \item LIB_CLIMATE John & Jason's decision on whether this PLANT_ID should
#' be used for climate analyses done summer 2019. "Y" = yes, "N" = no.
#' \item LIB_PHENO John & Jason's decision on whether this PLANT_ID should be
#' used for phenotypic analyses done summer 2019. "Y" = yes, "N" = no.
#' \item LIB_ACC John & Jason's decision on whether this PLANT_ID should be
#' used for analyses of a single plant per location done summer 2019 .
#' "Y" = yes, "N" = no.
#' \item LIB_NOTES Notes on John & Jason's decisions for the above 3 columns.
#' This analysis was done summer 2019.
#' \item PLOIDY Ploidy as determined by both sequencing & flow cytometry
#' analyses done in 2019. Typically 4X (tetraploid) or 8X (octoploid).
#' \item COLLECTION_TYPE Original plant collection metadata. What kind of
#' collection was this? Breeding selection, Natural collection, Cultivar.
#' \item COLLECTION_METHOD How was the plant collected?
#' \item COLLECTOR Who collected this plant?
#' \item STATE State of the United States that the plant was collected from.
#' \item COUNTY County that the plant was collected from.
#' \item LATITUDE Latitude of origin of the collected plant.
#' \item LONGITUDE Longitude of origin of the collected plant.
#' \item ELEVATION Elevation of origin of the collected plant.
#' \item NOTE_LATLONG Notes on exactness of latitude & longitude of collection
#' \item LOCALITY Additional details on plant collection location.
#' \item TAXON Genus & species of the collected plant.
#' \item HABITAT Details on habitat of plant collection location, if known.
#' \item COLL_DATE Information on plant collection date, if known.
#' \item PLANTED_2018 All plants clonally replicated and transplanted into
#' one or more common garden in the summer of 2018.
#' }
#'
#' @name pvdiv_metadata
#' @docType data
#' @author Alice MacQueen \email{alice.macqueen@@utexas.edu}
#' @references \url{https://www.nature.com/articles/s41586-020-03127-1}
#' @keywords data
#' @usage data(pvdiv_metadata)
#' @format A data frame with 1347 rows and 37 variables.
NULL
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