Package 'cageminer'

Title: Candidate Gene Miner
Description: This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments.
Authors: Fabrício Almeida-Silva [aut, cre] , Thiago Venancio [aut]
Maintainer: Fabrício Almeida-Silva <[email protected]>
License: GPL-3
Version: 1.13.0
Built: 2024-11-19 03:30:06 UTC
Source: https://github.com/bioc/cageminer

Help Index

Pepper chromosome lengths

Description

Lengths of pepper chromosomes 1-12 in a GRanges object. The genome for which lengths were calculated (v1.55) was downloaded from http://peppergenome.snu.ac.kr/download.php

Usage

data(chr_length)

Format

A GRanges object

Examples

data(chr_length)

Simulation of the output list from BioNERO::exp2gcn() with pepper data

Description

This object is a list as returned by BioNERO::exp2gcn(), but only the element genes_and_modules is included. For running time issues, only genes in the cyan module were kept in the element genes_and_modules. All other list elements have been assigned NULL. The network was inferred using the code from the vignette.

Usage

data(gcn)

Format

A list with the elements returned by BioNERO::exp2gcn().

Examples

data(gcn)

Genomic coordinates of pepper genes

Description

GRanges object with genomic coordinates of pepper genes downloaded from http://peppergenome.snu.ac.kr/download.php.

Usage

data(gene_ranges)

Format

A GRanges object

Examples

data(gene_ranges)

Guide genes associated with defense and resistance to oomycetes

Description

The GO annotation was retrieved from PLAZA 4.0 Dicots.

Usage

data(guides)

Format

A data frame with genes in the first column and GO description in the second column.

References

Van Bel, M., Diels, T., Vancaester, E., Kreft, L., Botzki, A., Van de Peer, Y., ... & Vandepoele, K. (2018). PLAZA 4.0: an integrative resource for functional, evolutionary and comparative plant genomics. Nucleic acids research, 46(D1), D1190-D1196.

Examples

data(guides)

Example hub genes for the network stored in the gcn object

Description

The data frame was created using the code from the vignette.

Usage

data(hubs)

Format

Data frame with gene IDs, module and intramodular degree.

Examples

data(hubs)

Mine high-confidence candidate genes in a single step

Description

Mine high-confidence candidate genes in a single step

Usage

mine_candidates(
  gene_ranges = NULL,
  marker_ranges = NULL,
  window = 2,
  expand_intervals = TRUE,
  gene_col = "ID",
  exp = NULL,
  gcn = NULL,
  guides = NULL,
  metadata,
  metadata_cols = 1,
  sample_group,
  min_cor = 0.2,
  alpha = 0.05,
  ...
)

Arguments

gene_ranges

A GRanges object with genomic coordinates of all genes in the genome.
marker_ranges

Genomic positions of SNPs. For a single trait, a GRanges object. For multiple traits, a GRangesList or CompressedGRangesList object, with each element of the list representing SNP positions for a particular trait.
window

Sliding window (in Mb) upstream and downstream relative to each SNP. Default: 2.
expand_intervals

Logical indicating whether or not to expand markers that are represented by intervals. This is particularly useful if users want to use a custom interval defined by linkage disequilibrium, for example. Default: TRUE.
gene_col

Column of the GRanges object containing gene ID. Default: "ID", the default for gff/gff3 files imported with rtracklayer::import.
exp

Expression data frame with genes in row names and samples in column names or a SummarizedExperiment object.
gcn

Gene coexpression network returned by BioNERO::exp2gcn().
guides

Guide genes as a character vector or as a data frame with genes in the first column and gene annotation class in the second column.
metadata

Sample metadata with samples in row names and sample information in the first column. Ignored if exp is a SummarizedExperiment object, as the colData will be extracted from the object.
metadata_cols

A vector (either numeric or character) indicating which columns should be extracted from column metadata if exp is a SummarizedExperiment object. The vector can contain column indices (numeric) or column names (character). By default, all columns are used.
sample_group

Level of sample metadata to be used for filtering in gene-trait correlation.
min_cor

Minimum correlation value for BioNERO::gene_significance(). Default: 0.2
alpha

Numeric indicating significance level. Default: 0.05
...

Additional arguments to BioNERO::gene_significance.

Value

A data frame with mined candidate genes and their correlation to the condition of interest.

Examples

data(pepper_se)
data(snp_pos)
data(gene_ranges)
data(guides)
data(gcn)
set.seed(1)
candidates <- mine_candidates(gene_ranges, snp_pos, exp = pepper_se,
                              gcn = gcn, guides = guides$Gene,
                              sample_group = "PRR_stress")

Step 1: Get all putative candidate genes for a given sliding window

Description

For a user-defined sliding window relative to each SNP, this function will subset all genes whose genomic positions overlap with the sliding window.

Usage

mine_step1(gene_ranges, marker_ranges, window = 2, expand_intervals = TRUE)

Arguments

gene_ranges

A GRanges object with genomic coordinates of all genes in the genome.
marker_ranges

Genomic positions of SNPs. For a single trait, a GRanges object. For multiple traits, a GRangesList or CompressedGRangesList object, with each element of the list representing SNP positions for a particular trait.
window

Sliding window (in Mb) upstream and downstream relative to each SNP. Default: 2.
expand_intervals

Logical indicating whether or not to expand markers that are represented by intervals. This is particularly useful if users want to use a custom interval defined by linkage disequilibrium, for example. Default: TRUE.

Value

A GRanges or GRangesList object with the genomic positions of all putative candidate genes.

See Also

findOverlaps-methods

Examples

data(snp_pos)
data(gene_ranges)
genes <- mine_step1(gene_ranges, snp_pos, window = 2)

Step 2: Get candidates in modules enriched in guide genes

Description

Step 2: Get candidates in modules enriched in guide genes

Usage

mine_step2(exp, gcn, guides, candidates, ...)

Arguments

exp

Expression data frame with genes in row names and samples in column names or a SummarizedExperiment object.
gcn

Gene coexpression network returned by BioNERO::exp2gcn().
guides

Guide genes as a character vector or as a data frame with genes in the first column and gene annotation class in the second column.
candidates

Character vector of all candidates genes to be inspected.
...

Additional arguments to BioNERO::module_enrichment

Value

A list of 2 elements:

candidates

Character vector of candidates after step 2

enrichment

Data frame of results for enrichment analysis

Examples

data(pepper_se)
data(guides)
data(gcn)
set.seed(1)
mine2 <- mine_step2(
    exp = pepper_se,
    gcn = gcn,
    guides = guides$Gene,
    candidates = rownames(pepper_se)
)

Step 3: Select candidates based on gene significance

Description

Step 3: Select candidates based on gene significance

Usage

mine_step3(
  exp,
  metadata,
  metadata_cols = 1,
  candidates,
  sample_group,
  min_cor = 0.2,
  alpha = 0.05,
  ...
)

Arguments

exp

Expression data frame with genes in row names and samples in column names or a SummarizedExperiment object.
metadata

Sample metadata with samples in row names and sample information in the first column. Ignored if exp is a SummarizedExperiment object, as the colData will be extracted from the object.
metadata_cols

A vector (either numeric or character) indicating which columns should be extracted from column metadata if exp is a SummarizedExperiment object. The vector can contain column indices (numeric) or column names (character). By default, all columns are used.
candidates

Character vector of candidate genes to be inspected.
sample_group

Level of sample metadata to be used for filtering in gene-trait correlation.
min_cor

Minimum correlation value for BioNERO::gene_significance(). Default: 0.2
alpha

Numeric indicating significance level. Default: 0.05
...

Additional arguments to BioNERO::gene_significance.

Value

A data frame with mined candidate genes and their correlation to the condition of interest.

Examples

data(pepper_se)
data(snp_pos)
data(gene_ranges)
data(guides)
data(gcn)
data(mine2)
set.seed(1)
mine3 <- mine_step3(
    exp = pepper_se,
    candidates = mine2$candidates,
    sample_group = "PRR_stress"
)

Example output from mine_step2()

Description

The list was created using the example code from mine_step().

Usage

data(mine2)

Format

List with elements 'candidates' (character vector) and 'enrichment' (data frame).

Examples

data(mine2)

Example output from mined_candidates()

Description

The data frame was created using the code from the vignette.

Usage

data(mined_candidates)

Format

Data frame with an example of the output from mined_candidates

Examples

data(mined_candidates)

Gene expression data from Kim et al., 2018.

Description

The data were filtered to keep only the top 4000 genes with highest RPKM values in PRR stress-related samples.

Usage

data(pepper_se)

Format

A SummarizedExperiment object.

References

Kim, MS., Kim, S., Jeon, J. et al. Global gene expression profiling for fruit organs and pathogen infections in the pepper, Capsicum annuum L.. Sci Data 5, 180103 (2018). https://doi.org/10.1038/sdata.2018.103

Examples

data(pepper_se)

Circos plot of SNP distribution across chromosomes

Description

Circos plot of SNP distribution across chromosomes

Usage

plot_snp_circos(genome_ranges, gene_ranges, marker_ranges)

Arguments

genome_ranges

A GRanges object with chromosome lengths.
gene_ranges

A GRanges object with genomic coordinates of all genes in the genome.
marker_ranges

Genomic positions of SNPs. For a single trait, a GRanges object. For multiple traits, a GRangesList or CompressedGRangesList object, with each element of the list representing SNP positions for a particular trait.

Value

A ggplot object with a circos plot of molecular marker distribution across chromosomes.

Examples

data(snp_pos)
data(gene_ranges)
data(chr_length)
p <- plot_snp_circos(chr_length, gene_ranges, snp_pos)

Plot a barplot of SNP distribution across chromosomes

Description

Plot a barplot of SNP distribution across chromosomes

Usage

plot_snp_distribution(marker_ranges)

Arguments

marker_ranges

Genomic positions of SNPs. For a single trait, a GRanges object. For multiple traits, a GRangesList or CompressedGRangesList object, with each element of the list representing SNP positions for a particular trait. List elements must have names for proper labelling.

Value

A ggplot object.

Examples

data(snp_pos)
p <- plot_snp_distribution(snp_pos)

Score candidate genes and select the top n genes

Description

Score candidate genes and select the top n genes

Usage

score_genes(
  mined_candidates,
  hubs = NULL,
  tfs = NULL,
  pick_top = 10,
  weight_tf = 2,
  weight_hub = 2,
  weight_both = 3
)

Arguments

mined_candidates

Data frame resulting from mine_candidates() or mine_step().
hubs

Character vector of hub genes.
tfs

Character vector of transcription factors.
pick_top

Number of top genes to select. Default: 10.
weight_tf

Numeric scalar with the weight to which correlation coefficients will be multiplied if the gene is a TF. Default: 2.
weight_hub

Numeric scalar with the weight to which correlation coefficients will be multiplied if the gene is a hub. Default: 2.
weight_both

Numeric scalar with the weight to which correlation coefficients will be multiplied if the gene is both a TF and a hub. Default: 3.

Value

Data frame with top n candidates and their scores.

Examples

data(tfs)
data(hubs)
data(mined_candidates)
set.seed(1)
scored <- score_genes(mined_candidates, hubs$Gene, tfs$Gene_ID)

Simulate number of genes for each sliding window

Description

This function counts genes that are contained in sliding windows related to each SNP.

Usage

simulate_windows(
  gene_ranges,
  marker_ranges,
  windows = seq(0.1, 2, by = 0.1),
  expand_intervals = TRUE
)

Arguments

gene_ranges

A GRanges object with genomic coordinates of all genes in the genome.
marker_ranges

Genomic positions of SNPs. For a single trait, a GRanges object. For multiple traits, a GRangesList or CompressedGRangesList object, with each element of the list representing SNP positions for a particular trait.
windows

Sliding windows (in Mb) upstream and downstream relative to each SNP. Default: seq(0.1, 2, by = 0.1).
expand_intervals

Logical indicating whether or not to expand markers that are represented by intervals. This is particularly useful if users want to use a custom interval defined by linkage disequilibrium, for example. Default: TRUE.

Details

By default, the function creates 20 sliding windows by expanding upstream and downstream boundaries for each SNP from 0.1 Mb (100 kb) to 2 Mb.

Value

A ggplot object summarizing the results of the simulations.

See Also

findOverlaps-methods

Examples

data(snp_pos)
data(gene_ranges)
simulate_windows(gene_ranges, snp_pos)

Capsicum annuum SNPs associated with resistance to Phytophthora root rot.

Description

The SNPs in this data set were retrieved from Siddique et al., 2019, and they are associated to resistance to Phytophthora root rot.

Usage

data(snp_pos)

Format

A GRanges object.

References

Siddique, M.I., Lee, HY., Ro, NY. et al. Identifying candidate genes for Phytophthora capsici resistance in pepper (Capsicum annuum) via genotyping-by-sequencing-based QTL mapping and genome-wide association study. Sci Rep 9, 9962 (2019). https://doi.org/10.1038/s41598-019-46342-1

Examples

data(snp_pos)

Pepper transcription factors

Description

Pepper transcription factors and their families retrieved from PlantTFDB 4.0.

Usage

data(tfs)

Format

A data frame with gene IDs in the first column and TF families in the second column.

References

Jin, J., Tian, F., Yang, D. C., Meng, Y. Q., Kong, L., Luo, J., & Gao, G. (2016). PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants. Nucleic acids research, gkw982.

Examples

data(tfs)