Package 'sigminer'

Description

Add Horizontal Arrow with Text Label to a ggplot

Usage

add_h_arrow(
  p,
  x,
  y,
  label = "optimal number",
  space = 0.01,
  vjust = 0.3,
  seg_len = 0.1,
  arrow_len = unit(2, "mm"),
  arrow_type = c("closed", "open"),
  font_size = 5,
  font_family = c("serif", "sans", "mono"),
  font_face = c("plain", "bold", "italic")
)

Arguments

Value

a ggplot object.

Description

Add text labels to a ggplot object, such as the result from show_sig_profile.

Usage

add_labels(
  p,
  x,
  y,
  y_end = NULL,
  n_label = NULL,
  labels = NULL,
  revert_order = FALSE,
  font_size = 5,
  font_family = "serif",
  font_face = c("plain", "bold", "italic"),
  ...
)

Arguments

Value

a ggplot object.

Examples

# Load mutational signature
load(system.file("extdata", "toy_mutational_signature.RData",
  package = "sigminer", mustWork = TRUE
))
# Show signature profile
p <- show_sig_profile(sig2, mode = "SBS")

# Method 1
p1 <- add_labels(p,
  x = 0.75, y = 0.3, y_end = 0.9, n_label = 3,
  labels = paste0("text", 1:3)
)
p1

# Method 2
p2 <- add_labels(p,
  x = c(0.15, 0.6, 0.75), y = c(0.3, 0.6, 0.9),
  labels = paste0("text", 1:3)
)
p2

# Method 3
sim <- get_sig_similarity(sig2)
p3 <- add_labels(p,
  x = c(0.15, 0.6, 0.75), y = c(0.25, 0.55, 0.8),
  labels = sim, font_size = 2
)
p3

Description

These functions are combined to provide a best practice for optimally identifying mutational signatures and attributing their activities (exposures) in tumor samples. They are listed in order to use.

• bp_extract_signatures() for extracting signatures.

• bp_show_survey() for showing measures change under different signature numbers to help user select optimal signature number. At default, an aggregated score (named score) is generated to suggest the best solution.

• bp_show_survey2() for showing simplified signature number survey like show_sig_number_survey().

• bp_get_sig_obj() for get a (list of) Signature object which is common used in sigminer for analysis and visualization.

• bp_attribute_activity() for optimizing signature activities (exposures). NOTE: the activities from extraction step may be better! You can also use sig_extract to get optimal NMF result from multiple NMF runs. Besides, you can use sig_fit to quantify exposures based on signatures extracted from bp_extract_signatures().

• bp_extract_signatures_iter() for extracting signature in a iteration way.

• bp_cluster_iter_list() for clustering (hclust with average linkage) iterated signatures to help collapse multiple signatures into one. The result cluster can be visualized by plot() or factoextra::fviz_dend().

• bp_get_clustered_sigs() for getting clustered (grouped) mean signatures from signature clusters.

• Extra: bp_get_stats() for obtaining stats for signatures and samples of a solution. These stats are aggregated (averaged) as the stats for a solution (specific signature number).

• Extra: bp_get_rank_score() for obtaining rank score for all signature numbers.

Usage

bp_extract_signatures(
  nmf_matrix,
  range = 2:5,
  n_bootstrap = 20L,
  n_nmf_run = 50,
  RTOL = 0.001,
  min_contribution = 0,
  cores = min(4L, future::availableCores()),
  cores_solution = min(cores, length(range)),
  seed = 123456L,
  handle_hyper_mutation = TRUE,
  report_integer_exposure = FALSE,
  only_core_stats = nrow(nmf_matrix) > 100,
  cache_dir = file.path(tempdir(), "sigminer_bp"),
  keep_cache = FALSE,
  pynmf = FALSE,
  use_conda = TRUE,
  py_path = "/Users/wsx/anaconda3/bin/python"
)

bp_extract_signatures_iter(
  nmf_matrix,
  range = 2:5,
  sim_threshold = 0.95,
  max_iter = 10L,
  n_bootstrap = 20L,
  n_nmf_run = 50,
  RTOL = 0.001,
  min_contribution = 0,
  cores = min(4L, future::availableCores()),
  cores_solution = min(cores, length(range)),
  seed = 123456L,
  handle_hyper_mutation = TRUE,
  report_integer_exposure = FALSE,
  only_core_stats = nrow(nmf_matrix) > 100,
  cache_dir = file.path(tempdir(), "sigminer_bp"),
  keep_cache = FALSE,
  pynmf = FALSE,
  use_conda = FALSE,
  py_path = "/Users/wsx/anaconda3/bin/python"
)

bp_cluster_iter_list(x, k = NULL, include_final_iteration = TRUE)

bp_get_clustered_sigs(SigClusters, cluster_label)

bp_get_sig_obj(obj, signum = NULL)

bp_get_stats(obj)

bp_get_rank_score(obj)

bp_show_survey2(
  obj,
  x = "signature_number",
  left_y = "silhouette",
  right_y = "L2_error",
  left_name = left_y,
  right_name = right_y,
  left_color = "black",
  right_color = "red",
  left_shape = 16,
  right_shape = 18,
  shape_size = 4,
  highlight = NULL
)

bp_show_survey(
  obj,
  add_score = FALSE,
  scales = c("free_y", "free"),
  fixed_ratio = TRUE
)

bp_attribute_activity(
  input,
  sample_class = NULL,
  nmf_matrix = NULL,
  method = c("bt", "stepwise"),
  bt_use_prop = FALSE,
  return_class = c("matrix", "data.table"),
  use_parallel = FALSE,
  cache_dir = file.path(tempdir(), "sigminer_attribute_activity"),
  keep_cache = FALSE
)

Arguments

Details

The signature extraction approach is adopted from reference #1, #2, and the whole best practice is adopted from the pipeline used by reference #3. I implement the whole procedure with R code based on the method description of papers. The code is well organized, tested and documented so user will find it pretty simple and useful. Besides, the structure of the results is very clear to see and also visualize like other approaches provided by sigminer.

Value

It depends on the called function.

Measure Explanation in Survey Plot

The survey plot provides a pretty good way to facilitate the signature number selection. A score measure is calculated as the weighted mean of selected measures and visualized as the first sub-plot. The optimal number is highlighted with red color dot and the best values for each measures are also highlighted with orange color dots. The detail of 6 measures shown in plot are explained as below.

• score - an aggregated score based on rank scores from selected measures below. The higher, the better. When two signature numbers have the same score, the larger signature number is preferred (this is a rare situation, you have to double check other measures).

• silhouette - the average silhouette width for signatures, also named as ASW in reference #2. The signature number with silhouette decreases sharply is preferred.

• distance - the average sample reconstructed cosine distance, the lower value is better.

• error - the average sample reconstructed error calculated with L2 formula (i.e. L2 error). This lower value is better. This measure represents a similar concept like distance above, they are all used to quantify how well sample mutation profiles can be reconstructed from signatures, but distance cares the whole mutation profile similarity while error here cares value difference.

• ⁠pos cor⁠ - the average positive signature exposure correlation coefficient. The lower value is better. This measure is constructed based on my understanding about signatures: mutational signatures are typically treated as independent recurrent patterns, so their activities are less correlated.

• similarity - the average similarity within in a signature cluster. Like silhouette, the point decreases sharply is preferred. In the practice, results from multiple NMF runs are clustered with "clustering with match" algorithm proposed by reference #2. This value indicates if the signature profiles extracted from different NMF runs are similar.

Author(s)

Shixiang Wang [email protected]

References

Alexandrov, Ludmil B., et al. "Deciphering signatures of mutational processes operative in human cancer." Cell reports 3.1 (2013): 246-259.

Degasperi, Andrea, et al. "A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies." Nature cancer 1.2 (2020): 249-263.

Alexandrov, Ludmil B., et al. “The repertoire of mutational signatures in human cancer.” Nature 578.7793 (2020): 94-101.

See Also

See sig_estimate, sig_extract, sig_auto_extract, sigprofiler_extract for other approaches.

Examples

data("simulated_catalogs")

# Here I reduce the values for n_bootstrap and n_nmf_run
# for reducing the run time.
# In practice, you should keep default or increase the values
# for better estimation.
#
# The input data here is simulated from 10 mutational signatures

# e1 <- bp_extract_signatures(
#   t(simulated_catalogs$set1),
#   range = 8:12,
#   n_bootstrap = 5,
#   n_nmf_run = 10
# )
#
# To avoid computation in examples,
# Here just load the result
# (e1$signature and e1$exposure set to NA to reduce package size)
load(system.file("extdata", "e1.RData", package = "sigminer"))


# See the survey for different signature numbers
# The suggested solution is marked as red dot
# with highest integrated score.
p1 <- bp_show_survey(e1)
p1
# You can also exclude plotting and highlighting the score
p2 <- bp_show_survey(e1, add_score = FALSE)
p2

# You can also plot a simplified version
p3 <- bp_show_survey2(e1, highlight = 10)
p3

# Obtain the suggested solution from extraction result
obj_suggested <- bp_get_sig_obj(e1, e1$suggested)
obj_suggested
# If you think the suggested signature number is not right
# Just pick up the solution you want
obj_s8 <- bp_get_sig_obj(e1, 8)

# Track the reconstructed profile similarity
rec_sim <- get_sig_rec_similarity(obj_s8, t(simulated_catalogs$set1))
rec_sim

# After extraction, you can assign the signatures
# to reference COSMIC signatures
# More see ?get_sig_similarity
sim <- get_sig_similarity(obj_suggested)
# Visualize the match result
if (require(pheatmap)) {
  pheatmap::pheatmap(sim$similarity)
}

# You already got the activities of signatures
# in obj_suggested, however, you can still
# try to optimize the result.
# NOTE: the optimization step may not truly optimize the result!
expo <- bp_attribute_activity(e1, return_class = "data.table")
expo$abs_activity


## Not run: 
# Iterative extraction:
# This procedure will rerun extraction step
# for those samples with reconstructed catalog similarity
# lower than a threshold (default is 0.95)
e2 <- bp_extract_signatures_iter(
  t(simulated_catalogs$set1),
  range = 9:11,
  n_bootstrap = 5,
  n_nmf_run = 5,
  sim_threshold = 0.99
)
e2
# When the procedure run multiple rounds
# you can cluster the signatures from different rounds by
# the following command
# bp_cluster_iter_list(e2)

## Extra utilities
rank_score <- bp_get_rank_score(e1)
rank_score
stats <- bp_get_stats(e2$iter1)
# Get the mean reconstructed similarity
1 - stats$stats_sample$cosine_distance_mean

## End(Not run)

Description

Location of Centromeres at Genome Build hg19

Format

A data.frame

Source

Generate from UCSC gold path

Examples

data(centromeres.hg19)

Description

Location of Centromeres at Genome Build hg38

Format

A data.frame

Source

Generate from Genome Reference Consortium

Examples

data(centromeres.hg38)

Description

Location of Centromeres at Genome Build mm10

Format

A data.frame

Source

Generate from https://hgdownload.soe.ucsc.edu/goldenPath/mm10/database/gap.txt.gz

Examples

data(centromeres.mm10)

Description

Location of Centromeres at Genome Build mm9

Format

A data.frame

Source

Generate from https://hgdownload.soe.ucsc.edu/goldenPath/mm9/database/ with code:

for i in $(seq 1 19) X Y;
do
wget https://hgdownload.soe.ucsc.edu/goldenPath/mm9/database/chr${i}_gap.txt.gz
done

Examples

data(centromeres.mm9)

Description

Location of Centromeres at Genome Build T2T

Format

A data.frame

Source

from T2T study

Examples

data(centromeres.T2T)

Description

Chromosome Size of Genome Build hg19

Format

A data.frame

Source

Generate from UCSC gold path

Examples

data(chromsize.hg19)

Description

Chromosome Size of Genome Build hg38

Format

A data.frame

Source

Generate from UCSC gold path

Examples

data(chromsize.hg38)

Description

Chromosome Size of Genome Build mm10

Format

A data.frame

Source

Generate from UCSC gold path http://hgdownload.cse.ucsc.edu/goldenPath/mm10/bigZips/mm10.chrom.sizes

Examples

data(chromsize.mm10)

Description

Chromosome Size of Genome Build mm9

Format

A data.frame

Source

Generate from UCSC gold path http://hgdownload.cse.ucsc.edu/goldenPath/mm9/bigZips/mm9.chrom.sizes

Examples

data(chromsize.mm9)

Description

Chromosome Size of Genome Build T2T

Format

A data.frame

Source

from T2T study

Examples

data(chromsize.T2T)

Description

Classification Table of Copy Number Features Devised by Wang et al. for Method 'W'

Format

A data.table with "sigminer.features" class name

Source

Generate from code under data_raw/

Examples

data(CN.features)

Description

S4 class for storing summarized absolute copy number profile.

Slots

data

data.table of absolute copy number calling.

summary.per.sample

data.table of copy number variation summary per sample.

genome_build

genome build version, should be one of 'hg19' or 'hg38'.

genome_measure

Set 'called' will use autosomo called segments size to compute total size for CNA burden calculation, this option is useful for WES and target sequencing. Set 'wg' will autosome size from genome build, this option is useful for WGS, SNP etc..

annotation

data.table of annotation for copy number segments.

dropoff.segs

data.table of copy number segments dropped from raw input.

Description

Calculate Cosine Measures

Usage

cosine(x, y)

Arguments

Value

a numeric value or matrix.

Examples

x <- c(1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0)
y <- c(0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0)
z1 <- cosine(x, y)
z1
z2 <- cosine(matrix(x), matrix(y))
z2

Description

Location of Chromosome Cytobands at Genome Build hg19

Format

A data.frame

Source

from UCSC

Examples

data(cytobands.hg19)

Description

Location of Chromosome Cytobands at Genome Build hg38

Format

A data.frame

Source

from UCSC

Examples

data(cytobands.hg38)

Description

Location of Chromosome Cytobands at Genome Build mm10

Format

A data.frame

Source

from UCSC http://hgdownload.cse.ucsc.edu/goldenpath/mm10/database/cytoBand.txt.gz

Examples

data(cytobands.mm10)

Description

Location of Chromosome Cytobands at Genome Build mm9

Format

A data.frame

Source

from UCSC http://hgdownload.cse.ucsc.edu/goldenpath/mm9/database/cytoBand.txt.gz

Examples

data(cytobands.mm9)

Description

Location of Chromosome Cytobands at Genome Build T2T

Format

A data.frame

Source

from T2T study

Examples

data(cytobands.T2T)

Description

See sig_tally for examples.

Usage

enrich_component_strand_bias(mat)

Arguments

Value

a data.table sorted by p_value.

Description

Setting aes(label=..p.adj..) in ggpubr::compare_means() does not show adjust p values. The returned result of this function can be combined with ggpubr::stat_pvalue_manual() to fix this problem.

Usage

get_adj_p(
  data,
  .col,
  .grp = "Sample",
  comparisons = NULL,
  method = "wilcox.test",
  p.adjust.method = "fdr",
  p.digits = 3L,
  ...
)

Arguments

Details

More info see ggpubr::compare_means(), ggpubr::stat_compare_means() and stats::p.adjust().

Value

a data.frame containing comparison result

Source

https://github.com/kassambara/ggpubr/issues/143

Examples

library(ggpubr)
# T-test
stat.test <- compare_means(
  len ~ dose,
  data = ToothGrowth,
  method = "t.test",
  p.adjust.method = "fdr"
)
stat.test
# Create a simple box plot
p <- ggboxplot(ToothGrowth, x = "dose", y = "len")
p

# Add p values
my_comparisons <- list(c("0.5", "1"), c("1", "2"), c("0.5", "2"))
p + stat_compare_means(method = "t.test", comparisons = my_comparisons)

# Try adding adjust p values
# proposed by author of ggpubr
# however it does not work
p + stat_compare_means(aes(label = ..p.adj..), method = "t.test", comparisons = my_comparisons)

# Solution:
# calculate adjust p values and their location
# then use stat_pvalue_manual() function
p_adj <- get_adj_p(ToothGrowth, .col = "len", .grp = "dose")
p_adj
p + stat_pvalue_manual(p_adj, label = "p.adj")

# Show selected comparisons
# Of note, p value is ajusted
# for three comparisons, but only
# two are showed in figure
p_adj <- get_adj_p(ToothGrowth,
  .col = "len", .grp = "dose",
  comparisons = list(c("0.5", "1"), c("1", "2"))
)
p + stat_pvalue_manual(p_adj, label = "p.adj")

Description

This implements a Cohen-Sharir method (see reference) like "Aneuploidy Score" computation. You can read the source code to see how it works. Basically, it follows the logic of Cohen-Sharir method but with some difference in detail implementation. Their results should be counterpart, but with no data validation for now. Please raise an issue if you find problem/bugs in this function.

Usage

get_Aneuploidy_score(
  data,
  ploidy_df = NULL,
  genome_build = "hg19",
  rm_black_arms = FALSE
)

Arguments

Value

A data.frame

References

• Cohen-Sharir, Y., McFarland, J. M., Abdusamad, M., Marquis, C., Bernhard, S. V., Kazachkova, M., ... & Ben-David, U. (2021). Aneuploidy renders cancer cells vulnerable to mitotic checkpoint inhibition. Nature, 1-6.

• Logic reference: https://github.com/quevedor2/aneuploidy_score/.

• Taylor, Alison M., et al. "Genomic and functional approaches to understanding cancer aneuploidy." Cancer cell 33.4 (2018): 676-689.

Examples

# Load copy number object
load(system.file("extdata", "toy_copynumber.RData",
  package = "sigminer", mustWork = TRUE
))

df <- get_Aneuploidy_score(cn)
df

df2 <- get_Aneuploidy_score(cn@data)
df2

df3 <- get_Aneuploidy_score(cn@data,
  ploidy_df = get_cn_ploidy(cn@data)
)
df3

Description

Sometimes, we may want to use or inspect specified run result from sig_auto_extract. This function is designed for this purpose.

Usage

get_bayesian_result(run_info)

Arguments

Value

a list.

Author(s)

Shixiang Wang

Examples

load(system.file("extdata", "toy_copynumber_tally_W.RData",
  package = "sigminer", mustWork = TRUE
))

res <- sig_auto_extract(cn_tally_W$nmf_matrix, result_prefix = "Test_copynumber", nrun = 1)

# All run info are stored in res$Raw$summary_run
# Obtain result of run 1
res_run1 <- get_bayesian_result(res$Raw$summary_run[1, ])

Description

Get CNV Frequency Table

Usage

get_cn_freq_table(
  data,
  genome_build = "hg19",
  cutoff = 2L,
  resolution_factor = 1L
)

Arguments

Value

a data.table.

Description

Get Ploidy from Absolute Copy Number Profile

Usage

get_cn_ploidy(data)

Arguments

Value

a value or a data.table

Examples

# Load copy number object
load(system.file("extdata", "toy_copynumber.RData",
  package = "sigminer", mustWork = TRUE
))

df <- get_cn_ploidy(cn)
df

Description

Get Genome Annotation

Usage

get_genome_annotation(
  data_type = c("chr_size", "centro_loc", "cytobands", "transcript", "gene"),
  chrs = paste0("chr", c(1:22, "X", "Y")),
  genome_build = c("hg19", "hg38", "T2T", "mm10", "mm9", "ce11")
)

Arguments

Value

a data.frame containing annotation data

Examples

df1 <- get_genome_annotation()
df1

df2 <- get_genome_annotation(genome_build = "hg38")
df2

df3 <- get_genome_annotation(data_type = "centro_loc")
df3

df4 <- get_genome_annotation(data_type = "centro_loc", genome_build = "hg38")
df4

df5 <- get_genome_annotation(data_type = "cytobands")
df5

df6 <- get_genome_annotation(data_type = "cytobands", genome_build = "hg38")
df6

Description

Compare genotypes/phenotypes based on signature groups (samples are assigned to several groups). For categorical type, calculate fisher p value (using stats::fisher.test) and count table. In larger than 2 by 2 tables, compute p-values by Monte Carlo simulation. For continuous type, calculate anova p value (using stats::aov), summary table and Tukey Honest significant difference (using stats::TukeyHSD). The result of this function can be plotted by show_group_comparison().

Usage

get_group_comparison(
  data,
  col_group,
  cols_to_compare,
  type = "ca",
  NAs = NA,
  verbose = FALSE
)

Arguments

Value

a list contains data, summary, p value etc..

Author(s)

Shixiang Wang [email protected]

Examples

load(system.file("extdata", "toy_copynumber_signature_by_W.RData",
  package = "sigminer", mustWork = TRUE
))

# Assign samples to clusters
groups <- get_groups(sig, method = "k-means")

set.seed(1234)

groups$prob <- rnorm(10)
groups$new_group <- sample(c("1", "2", "3", "4", NA), size = nrow(groups), replace = TRUE)

# Compare groups (filter NAs for categorical coloumns)
groups.cmp <- get_group_comparison(groups[, -1],
  col_group = "group",
  cols_to_compare = c("prob", "new_group"),
  type = c("co", "ca"), verbose = TRUE
)

# Compare groups (Set NAs of categorical columns to 'Rest')
groups.cmp2 <- get_group_comparison(groups[, -1],
  col_group = "group",
  cols_to_compare = c("prob", "new_group"),
  type = c("co", "ca"), NAs = "Rest", verbose = TRUE
)

Description

One of key results from signature analysis is to cluster samples into different groups. This function takes Signature object as input and return the membership in each cluster.

Usage

get_groups(
  Signature,
  method = c("consensus", "k-means", "exposure", "samples"),
  n_cluster = NULL,
  match_consensus = TRUE
)

Arguments

Details

Users may find there are bigger differences between using method 'samples' and 'exposure' but they use a similar idear to find dominant signature, here goes the reason:

Method 'samples' using data directly from NMF decomposition, this means the two matrix W (basis matrix or signature matrix) and H (coefficient matrix or exposure matrix) are the results of NMF. For method 'exposure', it uses the signature exposure loading matrix. In this situation, each signture represents a number of mutations (alterations) about implementation please see source code of sig_extract() function.

Value

a data.table object

See Also

NMF::predict(), show_groups.

Examples

# Load copy number prepare object
load(system.file("extdata", "toy_copynumber_tally_W.RData",
  package = "sigminer", mustWork = TRUE
))
# Extract copy number signatures
library(NMF)
sig <- sig_extract(cn_tally_W$nmf_matrix, 2,
  nrun = 10
)

# Methods 'consensus' and 'samples' are from NMF::predict()
g1 <- get_groups(sig, method = "consensus", match_consensus = TRUE)
g1
g2 <- get_groups(sig, method = "samples")
g2

# Use k-means clustering
g3 <- get_groups(sig, method = "k-means")
g3

Description

Get Overlap Size between Interval x and y

Usage

get_intersect_size(x.start, x.end, y.start, y.end)

Arguments

Value

a numeric vector.

Examples

o1 <- get_intersect_size(1, 5, 3, 20)
o1
o2 <- get_intersect_size(3, 20, 1, 10)
o2
o3 <- get_intersect_size(c(1, 2, 1), c(10, 4, 6), c(4, 2, 5), c(10, 3, 22))
o3

Description

pLOH score represents the genome that displayed LOH.

Usage

get_pLOH_score(data, rm_chrs = c("chrX", "chrY"), genome_build = "hg19")

Arguments

Value

A data.frame

References

Steele, Christopher D., et al. "Signatures of copy number alterations in human cancer." bioRxiv (2021).

Examples

# Load toy dataset of absolute copynumber profile
load(system.file("extdata", "toy_segTab.RData",
  package = "sigminer", mustWork = TRUE
))

set.seed(1234)
segTabs$minor_cn <- sample(c(0, 1), size = nrow(segTabs), replace = TRUE)
cn <- read_copynumber(segTabs,
  seg_cols = c("chromosome", "start", "end", "segVal"),
  genome_measure = "wg", complement = TRUE, add_loh = TRUE
)

df <- get_pLOH_score(cn)
df

df2 <- get_pLOH_score(cn@data)
df2

Description

$H = - \sum_{i=1}^n{p_i ln(p_i)}$

where n is the number of signatures identified in the signature with exposure > cutoff, and pi is the normalized exposure of the ith signature with exposure > cutoff. Exposures of signatures were normalized to sum to 1.

Usage

get_shannon_diversity_index(rel_expo, cutoff = 0.001)

Arguments

Value

a data.frame

References

Steele, Christopher D., et al. "Undifferentiated sarcomas develop through distinct evolutionary pathways." Cancer Cell 35.3 (2019): 441-456.

Examples

# Load mutational signature
load(system.file("extdata", "toy_mutational_signature.RData",
  package = "sigminer", mustWork = TRUE
))
# Get signature exposure
rel_expo <- get_sig_exposure(sig2, type = "relative")
rel_expo
diversity_index <- get_shannon_diversity_index(rel_expo)
diversity_index

Description

Obtain Signature Index for Cancer Types

Usage

get_sig_cancer_type_index(
  sig_type = c("legacy", "SBS", "DBS", "ID"),
  seq_type = c("WGS", "WES"),
  source = c("PCAWG", "TCGA", "nonPCAWG"),
  keyword = NULL
)

Arguments

Value

a list.

Examples

l1 <- get_sig_cancer_type_index()
l2 <- get_sig_cancer_type_index(sig_type = "SBS")
l3 <- get_sig_cancer_type_index(sig_type = "DBS", source = "PCAWG", seq_type = "WGS")
l4 <- get_sig_cancer_type_index(sig_type = "ID")
l5 <- get_sig_cancer_type_index(keyword = "breast")
l1
l2
l3
l4
l5

Description

Reference mutational signatures and their aetiologies, mainly obtained from COSMIC database (SigProfiler results) and cleaned before saving into sigminer package. You can obtain:

• COSMIC legacy SBS signatures.

• COSMIC v3 SBS signatures.

• COSMIC v3 DBS signatures.

• COSMIC v3 ID (indel) signatures.

• SBS and RS (rearrangement) signatures from Nik lab 2020 Nature Cancer paper.

• RS signatures from BRCA560 and USARC cohorts.

• Copy number signatures from USARC cohort and TCGA.

• Copy number signatures from Liu lab 2023. It supports both PCAWG and TCGA cohort.

Usage

get_sig_db(sig_db = "legacy")

Arguments

Value

a list.

References

• Steele, Christopher D., et al. "Signatures of copy number alterations in human cancer." Nature 606.7916 (2022): 984-991.

• Alexandrov, Ludmil B., et al. "The repertoire of mutational signatures in human cancer." Nature 578.7793 (2020): 94-101.

• Steele, Christopher D., et al. "Undifferentiated sarcomas develop through distinct evolutionary pathways." Cancer Cell 35.3 (2019): 441-456.

• Ziyu Tao, et al. "The repertoire of copy number alteration signatures in human cancer." Briefings in Bioinformatics (2023): bbad053.

See Also

get_sig_similarity, sig_fit and show_cosmic_sig_profile.

Examples

s1 <- get_sig_db()
s2 <- get_sig_db("SBS")
s3 <- get_sig_db("DBS")
s4 <- get_sig_db("DBS_mm10")
s5 <- get_sig_db("SBS_Nik_lab")
s6 <- get_sig_db("ID")
s7 <- get_sig_db("RS_BRCA560")
s8 <- get_sig_db("RS_USARC")
s9 <- get_sig_db("RS_Nik_lab")
s10 <- get_sig_db("CNS_USARC")
s11 <- get_sig_db("CNS_TCGA")
s12 <- get_sig_db("CNS_TCGA176")
s13 <- get_sig_db("CNS_PCAWG176")
s1
s2
s3
s4
s5
s6
s7
s8
s9
s10
s11
s12
s13

Description

The expected number of mutations (or copy number segment records) with each signature was determined after a scaling transformation V ~ WH = W'H' where W' = WU' and H' = UH. The scaling matrix U is a KxK diagnal matrix (K is signature number, U' is the inverse of U) with the element corresponding to the L1-norm of column vectors of W (ie. the sum of the elements of the vector). As a result, the k-th row vector of the final matrix H' represents the absolute exposure (activity) of the k-th process across samples (e.g., for SBS, the estimated (or expected) number of mutations generated by the k-th process). Of note, for copy number signatures, only components of feature CN was used for calculating H'.

Usage

get_sig_exposure(
  Signature,
  type = c("absolute", "relative"),
  rel_threshold = 0.01
)

Arguments

Value

a data.table

Author(s)

Shixiang Wang [email protected]

References

Kim, Jaegil, et al. "Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors." Nature genetics 48.6 (2016): 600.

Examples

# Load mutational signature
load(system.file("extdata", "toy_mutational_signature.RData",
  package = "sigminer", mustWork = TRUE
))
# Get signature exposure
expo1 <- get_sig_exposure(sig2)
expo1
expo2 <- get_sig_exposure(sig2, type = "relative")
expo2

Description

Association of signature exposures with other features will be performed using one of two procedures: for a continuous association variable (including ordinal variable), correaltion is performed; for a binary association variable, samples will be divided into two groups and Mann-Whitney U-test is performed to test for differences in signature exposure medians between the two groups. See get_tidy_association for cleaning association result.

Usage

get_sig_feature_association(
  data,
  cols_to_sigs,
  cols_to_features,
  type = "ca",
  method_co = c("spearman", "pearson", "kendall"),
  method_ca = stats::wilcox.test,
  min_n = 0.01,
  verbose = FALSE,
  ...
)

Arguments

Value

a list. For 'co' features, 'measure' means correlation coefficient. For 'ca' features, 'measure' means difference in means of signature exposure.

See Also

get_tidy_association

Description

See bp_extract_signatures for examples.

Usage

get_sig_rec_similarity(Signature, nmf_matrix)

Arguments

Value

a data.table.

Description

The reference signatures can be either a Signature object specified by Ref argument or known COSMIC signatures specified by sig_db argument. Two COSMIC databases are used for comparisons - "legacy" which includes 30 signaures, and "SBS" - which includes updated/refined 65 signatures. This function is modified from compareSignatures() in maftools package. NOTE: all reference signatures are generated from gold standard tool: SigProfiler.

Usage

get_sig_similarity(
  Signature,
  Ref = NULL,
  sig_db = c("SBS", "legacy", "DBS", "ID", "TSB", "SBS_Nik_lab", "RS_Nik_lab",
    "RS_BRCA560", "RS_USARC", "CNS_USARC", "CNS_TCGA", "CNS_TCGA176", "CNS_PCAWG176",
    "SBS_hg19", "SBS_hg38", "SBS_mm9", "SBS_mm10", "DBS_hg19", "DBS_hg38", "DBS_mm9",
    "DBS_mm10", "SBS_Nik_lab_Organ", "RS_Nik_lab_Organ", "latest_SBS_GRCh37",
    "latest_DBS_GRCh37", "latest_ID_GRCh37", "latest_SBS_GRCh38", "latest_DBS_GRCh38",
    "latest_SBS_mm9", "latest_DBS_mm9", "latest_SBS_mm10", "latest_DBS_mm10",
    "latest_SBS_rn6", "latest_DBS_rn6", "latest_CN_GRCh37", 
    
    "latest_RNA-SBS_GRCh37", "latest_SV_GRCh38"),
  db_type = c("", "human-exome", "human-genome"),
  method = "cosine",
  normalize = c("row", "feature"),
  feature_setting = sigminer::CN.features,
  set_order = TRUE,
  pattern_to_rm = NULL,
  verbose = TRUE
)

Arguments

Value

a list containing smilarities, aetiologies if available, best match and RSS.

Author(s)

Shixiang Wang [email protected]

References

Alexandrov, Ludmil B., et al. "The repertoire of mutational signatures in human cancer." Nature 578.7793 (2020): 94-101.

Degasperi, Andrea, et al. "A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies." Nature cancer 1.2 (2020): 249-263.

Steele, Christopher D., et al. "Undifferentiated sarcomas develop through distinct evolutionary pathways." Cancer Cell 35.3 (2019): 441-456.

Nik-Zainal, Serena, et al. "Landscape of somatic mutations in 560 breast cancer whole-genome sequences." Nature 534.7605 (2016): 47-54.

Steele, Christopher D., et al. "Signatures of copy number alterations in human cancer." Nature 606.7916 (2022): 984-991.

Examples

# Load mutational signature
load(system.file("extdata", "toy_mutational_signature.RData",
  package = "sigminer", mustWork = TRUE
))

s1 <- get_sig_similarity(sig2, Ref = sig2)
s1

s2 <- get_sig_similarity(sig2)
s2
s3 <- get_sig_similarity(sig2, sig_db = "SBS")
s3

# Set order for result similarity matrix
s4 <- get_sig_similarity(sig2, sig_db = "SBS", set_order = TRUE)
s4

## Remove some components
## in similarity calculation
s5 <- get_sig_similarity(sig2,
  Ref = sig2,
  pattern_to_rm = c("T[T>G]C", "T[T>G]G", "T[T>G]T")
)
s5

## Same to DBS and ID signatures
x1 <- get_sig_db("DBS_hg19")
x2 <- get_sig_db("DBS_hg38")
s6 <- get_sig_similarity(x1$db, x2$db)
s6

Description

Get Tidy Signature Association Results

Usage

get_tidy_association(cor_res, p_adjust = FALSE, method = "fdr")

Arguments

Value

a data.frame

See Also

get_sig_feature_association

Description

This function takes a data.frame as input, compares proportion of positive cases or mean measure in one subgroup and the remaining samples.

Usage

group_enrichment(
  df,
  grp_vars = NULL,
  enrich_vars = NULL,
  cross = TRUE,
  co_method = c("t.test", "wilcox.test"),
  ref_group = NA
)

Arguments

Value

a data.table with following columns:

• grp_var: group variable name.

• enrich_var: enrich variable (variable to be compared) name.

• grp1: the first group name, should be a member in grp_var column.

• grp2: the remaining samples, marked as 'Rest'.

• grp1_size: sample size for grp1.

• grp1_pos_measure: for binary variable, it stores the proportion of positive cases in grp1; for continuous variable, it stores mean value.

• grp2_size: sample size for grp2.

• grp2_pos_measure: same as grp1_pos_measure but for grp2.

• measure_observed: for binary variable, it stores odds ratio; for continuous variable, it stores scaled mean ratio.

• measure_tested: only for binary variable, it stores estimated odds ratio and its 95% CI from fisher.test().

• p_value: for binary variable, it stores p value from fisher.test(); for continuous variable, it stores value from wilcox.test() or t.test().

• type: one of "binary" and "continuous".

• method: one of "fish.test", "wilcox.test" and "t.test".

See Also

show_group_enrichment

Examples

set.seed(1234)
df <- dplyr::tibble(
  g1 = factor(abs(round(rnorm(99, 0, 1)))),
  g2 = rep(LETTERS[1:4], c(50, 40, 8, 1)),
  e1 = sample(c("P", "N"), 99, replace = TRUE),
  e2 = rnorm(99)
)

print(str(df))
print(head(df))

# Compare g1:e1, g1:e2, g2:e1 and g2:e2
x1 <- group_enrichment(df, grp_vars = c("g1", "g2"), enrich_vars = c("e1", "e2"))
x1

# Only compare g1:e1, g2:e2
x2 <- group_enrichment(df,
  grp_vars = c("g1", "g2"),
  enrich_vars = c("e1", "e2"),
  co_method = "wilcox.test",
  cross = FALSE
)
x2


# Visualization
p1 <- show_group_enrichment(x1, fill_by_p_value = TRUE)
p1
p2 <- show_group_enrichment(x1, fill_by_p_value = FALSE)
p2
p3 <- show_group_enrichment(x1, return_list = TRUE)
p3

Description

More details see group_enrichment().

Usage

group_enrichment2(
  df,
  subset_var,
  grp_vars,
  enrich_vars,
  co_method = c("t.test", "wilcox.test"),
  ref_group = NA
)

Arguments

See Also

show_group_enrichment

Description

This can be used for SNV/INDEL count matrix. For copy number analysis, please skip it.

Usage

handle_hyper_mutation(nmf_matrix)

Arguments

Value

a matrix.

References

Kim, Jaegil, et al. "Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors." Nature genetics 48.6 (2016): 600.

Description

Say Hello to Users

Usage

hello()

Examples

hello()

Description

S4 class for storing summarized MAF. It is from maftools package.

Details

More about MAF object please see maftools.

Slots

data

data.table of MAF file containing all non-synonymous variants.

variants.per.sample

table containing variants per sample

variant.type.summary

table containing variant types per sample

variant.classification.summary

table containing variant classification per sample

gene.summary

table containing variant classification per gene

summary

table with basic MAF summary stats

maf.silent

subset of main MAF containing only silent variants

clinical.data

clinical data associated with each sample/Tumor_Sample_Barcode in MAF.

Description

Output Signature Bootstrap Fitting Results

Usage

output_bootstrap(x, result_dir, mut_type = "SBS", sig_db = mut_type)

Arguments

Value

Nothing.

Description

Output Signature Fitting Results

Usage

output_fit(x, result_dir, mut_type = "SBS", sig_db = mut_type)

Arguments

Value

Nothing.

Description

Output Signature Results

Usage

output_sig(sig, result_dir, mut_type = "SBS", sig_db = mut_type)

Arguments

Value

Nothing.

Description

Output Tally Result in Barplots

Usage

output_tally(x, result_dir, mut_type = "SBS")

Arguments

Value

Nothing.

Description

Read absolute copy number profile for preparing CNV signature analysis. See detail part of sig_tally() to see how to handle sex to get correct summary.

Usage

read_copynumber(
  input,
  pattern = NULL,
  ignore_case = FALSE,
  seg_cols = c("Chromosome", "Start.bp", "End.bp", "modal_cn"),
  samp_col = "sample",
  add_loh = FALSE,
  loh_min_len = 10000,
  loh_min_frac = 0.05,
  join_adj_seg = TRUE,
  skip_annotation = FALSE,
  use_all = add_loh,
  min_segnum = 0L,
  max_copynumber = 20L,
  genome_build = c("hg19", "hg38", "T2T", "mm10", "mm9", "ce11"),
  genome_measure = c("called", "wg"),
  complement = FALSE,
  ...
)

Arguments

Value

a CopyNumber object.

Author(s)

Shixiang Wang [email protected]

See Also

read_maf for reading mutation data to MAF object.

Examples

# Load toy dataset of absolute copynumber profile
load(system.file("extdata", "toy_segTab.RData",
  package = "sigminer", mustWork = TRUE
))


cn <- read_copynumber(segTabs,
  seg_cols = c("chromosome", "start", "end", "segVal"),
  genome_build = "hg19", complement = FALSE
)
cn
cn_subset <- subset(cn, sample == "TCGA-DF-A2KN-01A-11D-A17U-01")

# Add LOH
set.seed(1234)
segTabs$minor_cn <- sample(c(0, 1), size = nrow(segTabs), replace = TRUE)
cn <- read_copynumber(segTabs,
  seg_cols = c("chromosome", "start", "end", "segVal"),
  genome_measure = "wg", complement = TRUE, add_loh = TRUE
)
# Use tally method "S" (Steele et al.)
tally_s <- sig_tally(cn, method = "S")

tab_file <- system.file("extdata", "metastatic_tumor.segtab.txt",
  package = "sigminer", mustWork = TRUE
)
cn2 <- read_copynumber(tab_file)
cn2

Description

Note, the result is not a CopyNumber object, you need to generate it by yourself.

Usage

read_copynumber_ascat(x)

Arguments

Value

a tidy list.

Description

Read Absolute Copy Number Profile from Sequenza Result Directory

Usage

read_copynumber_seqz(target_dir, return_df = FALSE, ...)

Arguments

Value

a data.frame or a CopyNumber object.

Description

This function is a wrapper of maftools::read.maf. Useless options in maftools::read.maf are dropped here. You can also use maftools::read.maf to read the data. All reference alleles and mutation alleles should be recorded in positive strand format.

Usage

read_maf(maf, verbose = TRUE)

read_maf_minimal(dt)

Arguments

Functions

• read_maf_minimal(): Read Maf data.frame from a minimal maf-like data

See Also

read_copynumber for reading copy number data to CopyNumber object.

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools", mustWork = TRUE)
if (!require("R.utils")) {
  message("Please install 'R.utils' package firstly")
} else {
  laml <- read_maf(maf = laml.maf)
  laml

  laml_mini <- laml@data[, list(
    Tumor_Sample_Barcode, Chromosome,
    Start_Position, End_Position,
    Reference_Allele, Tumor_Seq_Allele2
  )]
  laml2 <- read_maf_minimal(laml_mini)
  laml2
}

Description

Read Structural Variation Data as RS object

Usage

read_sv_as_rs(input)

Arguments

Value

a list

Examples

sv <- readRDS(system.file("extdata", "toy_sv.rds", package = "sigminer", mustWork = TRUE))
rs <- read_sv_as_rs(sv)
# svclass is optional
rs2 <- read_sv_as_rs(sv[, setdiff(colnames(sv), "svclass")])
identical(rs, rs2)
## Not run: 
tally_rs <- sig_tally(rs)

## End(Not run)

Description

MAF file is more recommended. In this function, we will mimic the MAF object from the key c(1, 2, 4, 5, 7) columns of VCF file.

Usage

read_vcf(
  vcfs,
  samples = NULL,
  genome_build = c("hg19", "hg38", "T2T", "mm10", "mm9", "ce11"),
  keep_only_pass = FALSE,
  verbose = TRUE
)

Arguments

Value

a MAF.

See Also

read_maf, read_copynumber

Examples

vcfs <- list.files(system.file("extdata", package = "sigminer"), "*.vcf", full.names = TRUE)

maf <- read_vcf(vcfs)
maf <- read_vcf(vcfs, keep_only_pass = TRUE)

Description

Read UCSC Xena Variant Format Data as MAF Object

Usage

read_xena_variants(path)

Arguments

Value

a MAF object.

Examples

if (requireNamespace("UCSCXenaTools")) {
  library(UCSCXenaTools)
  options(use_hiplot = TRUE)
  example_file <- XenaGenerate(subset = XenaDatasets == "mc3/ACC_mc3.txt") %>%
    XenaQuery() %>%
    XenaDownload()
  x <- read_xena_variants(example_file$destfiles)
  x@data
  y <- sig_tally(x)
  y
}

Description

See examples in sig_fit_bootstrap.

Usage

report_bootstrap_p_value(x, thresholds = c(0.01, 0.05, 0.1))

Arguments

Value

a (list of) matrix

Description

This is a wrapper for several implementation that classify samples into same size clusters, the details please see this blog. The source code is modified based on code from the blog.

Usage

same_size_clustering(
  mat,
  diss = FALSE,
  clsize = NULL,
  algo = c("nnit", "hcbottom", "kmvar"),
  method = c("maxd", "random", "mind", "elki", "ward.D", "average", "complete", "single")
)

Arguments

Value

a vector.

Examples

set.seed(1234L)
x <- rbind(
  matrix(rnorm(100, sd = 0.3), ncol = 2),
  matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2)
)
colnames(x) <- c("x", "y")

y1 <- same_size_clustering(x, clsize = 10)
y11 <- same_size_clustering(as.matrix(dist(x)), clsize = 10, diss = TRUE)

y2 <- same_size_clustering(x, clsize = 10, algo = "hcbottom", method = "ward.D")

y3 <- same_size_clustering(x, clsize = 10, algo = "kmvar")
y33 <- same_size_clustering(as.matrix(dist(x)), clsize = 10, algo = "kmvar", diss = TRUE)

Description

Returns quantification of copy number profile and events including tandem duplication and Chromothripisis etc. Only copy number data from autosome is used here. Some of the quantification methods are rough, you use at your risk. You should do some extra work to check the result scores.

Usage

scoring(object, TD_size_cutoff = c(1000, 1e+05, 2e+06), TD_cn_cutoff = Inf)

Arguments

Value

a data.table with following scores:

• cnaBurden: CNA burden representing the altered genomic fraction as previously reported.

• cnaLoad: CNA load representing the quantity of copy number alteration.

• MACN: mean altered copy number (MACN) reflecting the property of altered copy number segments, calculated as

  $MACN = \frac{\sum_{i} CN_i}{N_{cnv}}$

  where $CN_i$ is the copy number of altered segment $i$, $N_{cnv}$ is the number of CNV.

• weightedMACN: same as MACN but weighted with segment length.

  $MACN_{weighted} = \frac{\sum_{i} (CN_i \times L_{i})}{ \sum_{i} L_{i} }$

  where $L_{i}$ is the length of altered copy number segment $i$.

• Ploidy: ploidy, the formula is same as weightedMACN but using all copy number segments instead of altered copy number segments.

• TDP_pnas: tandem duplication phenotype score from ⁠https://www.pnas.org/doi/10.1073/pnas.1520010113⁠, the threshold k in reference is omitted.

  $TDP = - \frac{\sum_{chr} |TD_{obs}-TD_{exp}|}{TD_{total}}$

  where $TD_{total}$ is the number of TD, $TD_{obs}$ and $TD_exp$ are observed number of TD and expected number of TD for each chromosome.

• TDP: tandem duplication score used defined by our group work, TD represents segment with copy number greater than 2.

  $TD = \frac{TD_{total}}{\sum_{chr} |TD_{obs}-TD_{exp}|+1}$

• sTDP: TDP score for short TD.

• lTDP: TDP score for long TD.

• TDP_size : TDP region size (Mb).

• sTDP_size: sTDP region size (Mb).

• lTDP_size: lTDP region size(Mb).

• Chromoth_state: chromothripsis state score, according to reference doi:10.1016/j.cell.2013.02.023, chromothripsis frequently leads to massive loss of segments on the affected chromosome with segmental losses being interspersed with regions displaying normal (disomic) copy-number (e.g., copy-number states oscillating between copy-number = 1 and copy-number = 2), form tens to hundreds of locally clustered DNA rearrangements. Most of methods use both SV and CNV to infer chromothripsis, here we roughly quantify it with

  $\sum_{chr}{N_{OsCN}^2}$

  where $N_{OsCN}$ is the number of oscillating copy number pattern "2-1-2" for each chromosome.

Examples

# Load copy number object
load(system.file("extdata", "toy_copynumber.RData",
  package = "sigminer", mustWork = TRUE
))

d <- scoring(cn)
d

d2 <- scoring(cn, TD_cn_cutoff = 4L)
d2

Description

Show Alteration Catalogue Profile

Usage

show_catalogue(
  catalogue,
  mode = c("SBS", "copynumber", "DBS", "ID", "RS"),
  method = "Wang",
  normalize = c("raw", "row", "feature"),
  style = c("default", "cosmic"),
  samples = NULL,
  samples_name = NULL,
  x_lab = "Components",
  y_lab = "Counts",
  ...
)

Arguments

Value

a ggplot object

Examples

data("simulated_catalogs")
p <- show_catalogue(simulated_catalogs$set1, style = "cosmic")
p

Description

Another visualization method for copy number profile like show_cn_profile.

Usage

show_cn_circos(
  data,
  samples = NULL,
  show_title = TRUE,
  chrs = paste0("chr", 1:22),
  genome_build = c("hg19", "hg38", "T2T", "mm10", "mm9", "ce11"),
  col = NULL,
  side = "inside",
  ...
)

Arguments

Value

a circos plot

Examples

load(system.file("extdata", "toy_copynumber.RData",
  package = "sigminer", mustWork = TRUE
))

show_cn_circos(cn, samples = 1)
show_cn_circos(cn, samples = "TCGA-99-7458-01A-11D-2035-01")

## Remove title
show_cn_circos(cn, samples = 1, show_title = FALSE)

## Subset chromosomes
show_cn_circos(cn, samples = 1, chrs = c("chr1", "chr2", "chr3"))

## Arrange plots
layout(matrix(1:4, 2, 2))
show_cn_circos(cn, samples = 4)

layout(1) # reset layout

Description

Show classified components ("Wang" ("W") method) for copy number data.

Usage

show_cn_components(
  parameters,
  method = "Wang",
  show_weights = TRUE,
  log_y = FALSE,
  return_plotlist = FALSE,
  base_size = 12,
  nrow = 2,
  align = "hv",
  ...
)

Arguments

Value

a ggplot object

Author(s)

Shixiang Wang [email protected]

Description

Visually summarize copy number distribution either by copy number segment length or chromosome. Input is a CopyNumber object, genome_build option will read from genome_build slot of object.

Usage

show_cn_distribution(
  data,
  rm_normal = TRUE,
  mode = c("ld", "cd"),
  fill = FALSE,
  scale_chr = TRUE,
  base_size = 14
)

Arguments

Value

a ggplot object

Author(s)

Shixiang Wang [email protected]

Examples

# Load copy number object
load(system.file("extdata", "toy_copynumber.RData",
  package = "sigminer", mustWork = TRUE
))
# Plot distribution
p1 <- show_cn_distribution(cn)
p1
p2 <- show_cn_distribution(cn, mode = "cd")
p2
p3 <- show_cn_distribution(cn, mode = "cd", fill = TRUE)
p3

Description

Show Copy Number Feature Distributions

Usage

show_cn_features(
  features,
  method = "Wang",
  rm_outlier = FALSE,
  ylab = NULL,
  log_y = FALSE,
  return_plotlist = FALSE,
  base_size = 12,
  nrow = 2,
  align = "hv",
  ...
)

Arguments

Value

a ggplot object

Description

Show Copy Number Variation Frequency Profile with Circos

Usage

show_cn_freq_circos(
  data,
  groups = NULL,
  cutoff = 2L,
  resolution_factor = 1L,
  title = c("AMP", "DEL"),
  chrs = paste0("chr", 1:22),
  genome_build = c("hg19", "hg38", "T2T", "mm10", "mm9", "ce11"),
  cols = NULL,
  plot_ideogram = TRUE,
  track_height = 0.5,
  ideogram_height = 1,
  ...
)

Arguments

Value

Nothing.

Examples

load(system.file("extdata", "toy_copynumber.RData",
  package = "sigminer", mustWork = TRUE
))

show_cn_freq_circos(cn)
ss <- unique(cn@data$sample)
show_cn_freq_circos(cn, groups = list(a = ss[1:5], b = ss[6:10]), cols = c("red", "green"))

Description

Show Summary Copy Number Profile for Sample Groups

Usage

show_cn_group_profile(
  data,
  groups = NULL,
  fill_area = TRUE,
  cols = NULL,
  chrs = paste0("chr", c(1:22, "X")),
  genome_build = c("hg19", "hg38", "T2T", "mm10", "mm9", "ce11"),
  cutoff = 2L,
  resolution_factor = 1L,
  force_y_limit = TRUE,
  highlight_genes = NULL,
  repel = FALSE,
  nrow = NULL,
  ncol = NULL,
  return_plotlist = FALSE
)

Arguments

Value

a (list of) ggplot object.

Examples

load(system.file("extdata", "toy_copynumber.RData",
  package = "sigminer", mustWork = TRUE
))

p1 <- show_cn_group_profile(cn)
p1

ss <- unique(cn@data$sample)
p2 <- show_cn_group_profile(cn, groups = list(a = ss[1:5], b = ss[6:10]))
p2
p3 <- show_cn_group_profile(cn,
  groups = list(g1 = ss[1:5], g2 = ss[6:10]),
  force_y_limit = c(-1, 1), nrow = 2
)
p3

## Set custom cutoff for custom data
data <- cn@data
data$segVal <- data$segVal - 2L
p4 <- show_cn_group_profile(data,
  groups = list(g1 = ss[1:5], g2 = ss[6:10]),
  force_y_limit = c(-1, 1), nrow = 2,
  cutoff = c(0, 0)
)
p4

## Add highlight gene
p5 <- show_cn_group_profile(cn, highlight_genes = c("TP53", "EGFR"))
p5

Description

Sometimes it is very useful to check details about copy number profile for one or multiple samples. This function is designed to do this job and can be further modified by ggplot2 related packages.

Usage

show_cn_profile(
  data,
  samples = NULL,
  show_n = NULL,
  show_title = FALSE,
  show_labels = NULL,
  chrs = paste0("chr", 1:22),
  position = NULL,
  genome_build = c("hg19", "hg38", "T2T", "mm10", "mm9", "ce11"),
  ylim = NULL,
  nrow = NULL,
  ncol = NULL,
  return_plotlist = FALSE
)

Arguments

Value

a ggplot object or a list

Examples

# Load copy number object
load(system.file("extdata", "toy_copynumber.RData",
  package = "sigminer", mustWork = TRUE
))

p <- show_cn_profile(cn, nrow = 2, ncol = 1)
p

p2 <- show_cn_profile(cn,
  nrow = 2, ncol = 1,
  position = "chr1:3218923-116319008"
)
p2

Description

All variables must be continuous. The matrix will be returned as an element of ggplot object. This is basically a wrapper of R package ggcorrplot.

Usage

show_cor(
  data,
  x_vars = colnames(data),
  y_vars = x_vars,
  cor_method = "spearman",
  vis_method = "square",
  lab = TRUE,
  test = TRUE,
  hc_order = FALSE,
  p_adj = NULL,
  ...
)

Arguments

Value

a ggplot object

See Also

show_sig_feature_corrplot for specific and more powerful association analysis and visualization.

Examples

data("mtcars")
p1 <- show_cor(mtcars)
p2 <- show_cor(mtcars,
  x_vars = colnames(mtcars)[1:4],
  y_vars = colnames(mtcars)[5:8]
)
p3 <- show_cor(mtcars, vis_method = "circle", p_adj = "fdr")
p1
p1$cor
p2
p3

## Auto detect problem variables
mtcars$xx <- 0L
p4 <- show_cor(mtcars)
p4

Description

Show Signature Information in Web Browser

Usage

show_cosmic(x = "home")

Arguments

Value

Nothing.

Examples

## Not run: 
show_cosmic()
show_cosmic("legacy")
show_cosmic("SBS")
show_cosmic("DBS")
show_cosmic("ID")
show_cosmic("SBS1")
show_cosmic("DBS2")
show_cosmic("ID3")

## End(Not run)

Description

Plot Reference (Mainly COSMIC) Signature Profile

Usage

show_cosmic_sig_profile(
  sig_index = NULL,
  show_index = TRUE,
  sig_db = "legacy",
  ...
)

Arguments

Value

a ggplot object

Author(s)

Shixiang Wang [email protected]

Examples

show_cosmic_sig_profile()
show_cosmic_sig_profile(sig_db = "SBS")
show_cosmic_sig_profile(sig_index = 1:5)
show_cosmic_sig_profile(sig_db = "SBS", sig_index = c("10a", "17a"))

gg <- show_cosmic_sig_profile(sig_index = 1:5)
gg$aetiology

Description

Using result data from get_group_comparison, this function plots genotypes/phenotypes comparison between signature groups using ggplot2 package and return a list of ggplot object contains individual and combined plots. The combined plot is easily saved to local using cowplot::save_plot(). Of note, default fisher test p values are shown for categorical data and fdr values are shown for continuous data.

Usage

show_group_comparison(
  group_comparison,
  xlab = "group",
  ylab_co = NA,
  legend_title_ca = NA,
  legend_position_ca = "bottom",
  set_ca_sig_yaxis = FALSE,
  set_ca_custom_xlab = FALSE,
  show_pvalue = TRUE,
  ca_p_threshold = 0.01,
  method = "wilcox.test",
  p.adjust.method = "fdr",
  base_size = 12,
  font_size_x = 12,
  text_angle_x = 30,
  text_hjust_x = 0.2,
  ...
)

Arguments

Value

a list of ggplot objects.

Author(s)

Shixiang Wang [email protected]

Examples

load(system.file("extdata", "toy_copynumber_signature_by_W.RData",
  package = "sigminer", mustWork = TRUE
))

# Assign samples to clusters
groups <- get_groups(sig, method = "k-means")

set.seed(1234)

groups$prob <- rnorm(10)
groups$new_group <- sample(c("1", "2", "3", "4", NA), size = nrow(groups), replace = TRUE)

# Compare groups (filter NAs for categorical coloumns)
groups.cmp <- get_group_comparison(groups[, -1],
  col_group = "group",
  cols_to_compare = c("prob", "new_group"),
  type = c("co", "ca"), verbose = TRUE
)

# Compare groups (Set NAs of categorical columns to 'Rest')
groups.cmp2 <- get_group_comparison(groups[, -1],
  col_group = "group",
  cols_to_compare = c("prob", "new_group"),
  type = c("co", "ca"), NAs = "Rest", verbose = TRUE
)

show_group_comparison(groups.cmp)

ggcomp <- show_group_comparison(groups.cmp2)
ggcomp$co_comb
ggcomp$ca_comb

Description

This is a general function, it can be used in any proper analysis.

Usage

show_group_distribution(
  data,
  gvar,
  dvar,
  fun = stats::median,
  order_by_fun = FALSE,
  alpha = 0.8,
  g_label = "label",
  g_angle = 0,
  g_position = "top",
  point_size = 1L,
  segment_size = 1L,
  segment_color = "red",
  xlab = NULL,
  ylab = NULL,
  nrow = 1L,
  background_color = c("#DCDCDC", "#F5F5F5")
)

Arguments

Value

a ggplot object.

Author(s)

Shixiang Wang [email protected]

Examples

set.seed(1234)
data <- data.frame(
  yval = rnorm(120),
  gr = c(rep("A", 50), rep("B", 40), rep("C", 30))
)
p <- show_group_distribution(data,
  gvar = 2, dvar = 1,
  g_label = "norm",
  background_color = "grey"
)
p
p2 <- show_group_distribution(data,
  gvar = "gr", dvar = "yval",
  g_position = "bottom",
  order_by_fun = TRUE,
  alpha = 0.3
)
p2

# Set custom group names
p3 <- show_group_distribution(data,
  gvar = 2, dvar = 1,
  g_label = c("A" = "X", "B" = "Y", "C" = "Z")
)
p3

Description

See group_enrichment for examples. NOTE the box fill and the box text have different meanings.

Usage

show_group_enrichment(
  df_enrich,
  return_list = FALSE,
  scales = "free",
  add_text_annotation = TRUE,
  fill_by_p_value = TRUE,
  use_fdr = TRUE,
  cut_p_value = FALSE,
  cut_breaks = c(-Inf, -5, log10(0.05), -log10(0.05), 5, Inf),
  cut_labels = c("↓ 1e-5", "↓ 0.05", "non-significant", "↑ 0.05", "↑ 1e-5"),
  fill_scale = scale_fill_gradient2(low = "#08A76B", mid = "white", high = "red",
    midpoint = ifelse(fill_by_p_value, 0, 1)),
  cluster_row = FALSE,
  cluster_col = FALSE,
  ...
)