Package 'ICIKendallTau' reference manual

Title:	Calculates information-content-informed Kendall-tau
Description:	Provides functions for calculating information-content-informed Kendall-tau. This version of Kendall-tau allows for the inclusion of missing values.
Authors:	Robert M Flight [aut, cre] , Hunter NB Moseley [aut]
Maintainer:	Robert M Flight <[email protected]>
License:	MIT + file LICENSE
Version:	1.2.1
Built:	2024-08-21 05:47:55 UTC
Source:	https://github.com/MoseleyBioinformaticsLab/ICIKendallTau

Fast correlation with test

Description

Allows to run cor.test on a matrix of inputs.

Usage

cor_fast(
  x,
  y = NULL,
  use = "everything",
  method = "pearson",
  alternative = "two.sided",
  continuity = FALSE,
  return_matrix = TRUE
)
cor_fast(
  x,
  y = NULL,
  use = "everything",
  method = "pearson",
  alternative = "two.sided",
  continuity = FALSE,
  return_matrix = TRUE
)

Arguments

`x`	a numeric vector, matrix, or data frame.
`y`	NULL (default) or a vector.
`use`	an optional character string giving a method for computing correlations in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", or "pairwise.complete.obs".
`method`	which correlation method to use, "pearson" or "spearman"
`alternative`	how to perform the statistical test
`continuity`	should a continuity correction be applied
`return_matrix`	should the matrices of values be returned, or a long data.frame

Details

Although the interface is mostly identical to the built-in stats::cor.test() method, there are some differences.

if only x is provided as a matrix, the columns must be named.
if providing both x and y, it is assumed they are both single vectors.
if NA values are present, this function does not error, but will either remove them or return NA, depending on the option.
"na.or.complete" is not a valid option for use.
A named list with matrices or data.frame is returned, with the rho and pvalue values.

Value

a list of matrices, rho, pvalue, or a data.frame.

convert matrix to data.frame

Description

Given a square correlation matrix, converts it to a long data.frame, with three columns.

Usage

cor_matrix_2_long_df(in_matrix)
cor_matrix_2_long_df(in_matrix)

Arguments

in_matrix

the correlation matrix

Details

The data.frame contains three columns:

s1: the first entry of comparison
s2: the second entry of comparison
cor: the correlation value

Value

data.frame

turn logging off

Description

There may be good reasons to turn the logging off after it's been turned on. This basically tells the package that the logger isn't available.

Usage

disable_logging()
disable_logging()

turn logging on

Description

Choose to enable logging, to a specific file if desired.

Usage

enable_logging(log_file = NULL, memory = FALSE)
enable_logging(log_file = NULL, memory = FALSE)

Arguments

`log_file`	the file to log to
`memory`	provide memory logging too? Only available on Linux and MacOS

Details

Uses the logger package under the hood, which is suggested in the dependencies. Having logging enabled is nice to see when things are starting and stopping, and what exactly has been done, without needing to write messages to the console. It is especially useful if you are getting errors, but can't really see them, then you can add "memory" logging to see if you are running out of memory.

Default log file has the pattern:

YYYY.MM.DD.HH.MM.SS_ICIKendallTau_run.log

Information-content-informed kendall tau

Description

Given a data-matrix, computes the information-theoretic Kendall-tau-b between all samples.

Usage

ici_kendalltau(
  data_matrix,
  global_na = c(NA, Inf, 0),
  perspective = "global",
  scale_max = TRUE,
  diag_good = TRUE,
  include_only = NULL,
  alternative = "two.sided",
  continuity = FALSE,
  check_timing = FALSE,
  return_matrix = TRUE
)
ici_kendalltau(
  data_matrix,
  global_na = c(NA, Inf, 0),
  perspective = "global",
  scale_max = TRUE,
  diag_good = TRUE,
  include_only = NULL,
  alternative = "two.sided",
  continuity = FALSE,
  check_timing = FALSE,
  return_matrix = TRUE
)

Arguments

`data_matrix`	matrix or data.frame of values, samples are columns, features are rows
`global_na`	numeric vector that defines globally, what should be treated as NA?
`perspective`	how to treat missing data in denominator and ties, character
`scale_max`	logical, should everything be scaled compared to the maximum correlation?
`diag_good`	logical, should the diagonal entries reflect how many entries in the sample were "good"?
`include_only`	only run the correlations that include the members (as a vector) or combinations (as a list or data.frame)
`alternative`	what is the alternative for the p-value test?
`continuity`	should a continuity correction be applied?
`check_timing`	logical to determine should we try to estimate run time for full dataset? (default is FALSE)
`return_matrix`	logical, should the data.frame or matrix result be returned?

Details

For more details, see the vignette ⁠vignette("ici-kendalltau", package = "ICIKendallTau"))⁠

The default for global_na includes what values in the data to replace with NA for the Kendall-tau calculation. By default these are global_na = c(NA, Inf, 0). If you want to replace something other than 0, for example, you might use global_na = c(NA, Inf, -2), and all values of -2 will be replaced instead of 0.

When check_timing = TRUE, 5 random pairwise comparisons will be run to generate timings on a single core, and then estimates of how long the full set will take are calculated. The data is returned as a data.frame, and will be on the low side, but it should provide you with a good idea of how long your data will take.

Returned is a list containing matrices with:

cor: scaled correlations
raw: raw kendall-tau correlations
pval: p-values
taumax: the theoretical maximum kendall-tau value possible

Eventually, we plan to provide two more parameters for replacing values, feature_na for feature specific NA values and sample_na for sample specific NA values.

If you want to know if the missing values in your data are possibly due to left-censorship, we recommend testing that hypothesis with test_left_censorship() first.

Value

list with cor, raw, pval, taumax

Examples

## Not run: 
# not run
set.seed(1234)
s1 = sort(rnorm(1000, mean = 100, sd = 10))
s2 = s1 + 10 

matrix_1 = cbind(s1, s2)

r_1 = ici_kendalltau(matrix_1)
r_1$cor

#    s1 s2
# s1  1  1
# s2  1  1
names(r_1)
# "cor", "raw", "pval", "taumax", "keep", "run_time"

s3 = s1
s3[sample(100, 50)] = NA

s4 = s2
s4[sample(100, 50)] = NA

matrix_2 = cbind(s3, s4)
r_2 = ici_kendalltau(matrix_2)
r_2$cor
#           s3        s4
# s3 1.0000000 0.9944616
# s4 0.9944616 1.0000000

# using include_only
set.seed(1234)
x = t(matrix(rnorm(5000), nrow = 100, ncol = 50))
colnames(x) = paste0("s", seq(1, nrow(x)))

# only calculate correlations of other columns with "s1"
include_s1 = "s1"
s1_only = ici_kendalltau(x, include_only = include_s1)

# include s1 and s3 things both
include_s1s3 = c("s1", "s3")
s1s3_only = ici_kendalltau(x, include_only = include_s1s3)

# only specify certain pairs either as a list
include_pairs = list(g1 = "s1", g2 = c("s2", "s3"))
s1_other = ici_kendalltau(x, include_only = include_pairs)

# or a data.frame
include_df = as.data.frame(list(g1 = "s1", g2 = c("s2", "s3")))
s1_df = ici_kendalltau(x, include_only = include_df)


## End(Not run)
## Not run: 
# not run
set.seed(1234)
s1 = sort(rnorm(1000, mean = 100, sd = 10))
s2 = s1 + 10 

matrix_1 = cbind(s1, s2)

r_1 = ici_kendalltau(matrix_1)
r_1$cor

#    s1 s2
# s1  1  1
# s2  1  1
names(r_1)
# "cor", "raw", "pval", "taumax", "keep", "run_time"

s3 = s1
s3[sample(100, 50)] = NA

s4 = s2
s4[sample(100, 50)] = NA

matrix_2 = cbind(s3, s4)
r_2 = ici_kendalltau(matrix_2)
r_2$cor
#           s3        s4
# s3 1.0000000 0.9944616
# s4 0.9944616 1.0000000

# using include_only
set.seed(1234)
x = t(matrix(rnorm(5000), nrow = 100, ncol = 50))
colnames(x) = paste0("s", seq(1, nrow(x)))

# only calculate correlations of other columns with "s1"
include_s1 = "s1"
s1_only = ici_kendalltau(x, include_only = include_s1)

# include s1 and s3 things both
include_s1s3 = c("s1", "s3")
s1s3_only = ici_kendalltau(x, include_only = include_s1s3)

# only specify certain pairs either as a list
include_pairs = list(g1 = "s1", g2 = c("s2", "s3"))
s1_other = ici_kendalltau(x, include_only = include_pairs)

# or a data.frame
include_df = as.data.frame(list(g1 = "s1", g2 = c("s2", "s3")))
s1_df = ici_kendalltau(x, include_only = include_df)


## End(Not run)

Calculates ici-kendall-tau

Description

Calculates ici-kendall-tau

Usage

ici_kt(
  x,
  y,
  perspective = "local",
  alternative = "two.sided",
  continuity = FALSE,
  output = "simple"
)
ici_kt(
  x,
  y,
  perspective = "local",
  alternative = "two.sided",
  continuity = FALSE,
  output = "simple"
)

Arguments

`x`	numeric vector
`y`	numeric vector
`perspective`	should we consider the "local" or "global" perspective?
`alternative`	what is the alternative for the p-value test?
`continuity`	logical: if true, a continuity correction is used
`output`	used to control reporting of values for debugging

Details

Calculates the information-content-informed Kendall-tau correlation measure. This correlation is based on concordant and discordant ranked pairs, like Kendall-tau, but also includes missing values (as NA). Missing values are assumed to be primarily due to lack of detection due to instrumental sensitivity, and therefore encode some information.

For more details see the ICI-Kendall-tau vignette:

browseVignettes("ICIKendallTau")

Value

kendall tau correlation, p-value, max-correlation

Examples

x = sort(rnorm(100))
y = x + 1
y2 = y
y2[1:10] = NA
ici_kt(x, y)
ici_kt(x, y2, "global")
ici_kt(x, y2)

x = sort(rnorm(100))
y = x + 1
y2 = y
y2[1:10] = NA
ici_kt(x, y)
ici_kt(x, y2, "global")
ici_kt(x, y2)

Fast kendall tau

Description

Uses the underlying c++ implementation of ici_kt to provide a fast version of Kendall-tau correlation.

Usage

kt_fast(
  x,
  y = NULL,
  use = "everything",
  alternative = "two.sided",
  continuity = FALSE,
  return_matrix = TRUE
)
kt_fast(
  x,
  y = NULL,
  use = "everything",
  alternative = "two.sided",
  continuity = FALSE,
  return_matrix = TRUE
)

Arguments

`x`	a numeric vector, matrix, or data frame.
`y`	NULL (default) or a vector.
`use`	an optional character string giving a method for computing correlations in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", or "pairwise.complete.obs".
`alternative`	the type of test
`continuity`	should a continuity correction be applied
`return_matrix`	Should the matrices of values be returned, or a long data.frame

Details

Although the interface is mostly identical to the built-in stats::cor() method, there are some differences.

if only x is provided as a matrix or data.frame, the columns must be named.
if providing both x and y, it is assumed they are both single vectors.
if NA values are present, this function does not error, but will either remove them or return NA, depending on the option.
"na.or.complete" is not a valid option for use.
A named list with matrices or data.frame is returned, with the tau and pvalue values.

Value

a list of matrices, tau, pvalue, or a data.frame.

log memory usage

Description

Logs the amount of memory being used to a log file if it is available, and generating warnings if the amount of RAM hits zero.

Usage

log_memory()
log_memory()

log messages

Description

If a log_appender is available, logs the given message at the info level.

Usage

log_message(message_string)
log_message(message_string)

Arguments

message_string

the string to put in the message

convert data.frame to matrix

Description

Given a long data.frame, converts it to a possibly square correlation matrix

Usage

long_df_2_cor_matrix(long_df, is_square = TRUE)
long_df_2_cor_matrix(long_df, is_square = TRUE)

Arguments

`long_df`	the long data.frame
`is_square`	should it be a square matrix?

Value

matrix

Example Dataset With Missingness

Description

An example dataset that has missingness from left-censorship

Usage

missing_dataset
missing_dataset

Format

`missing_dataset`

A matrix with 1000 rows and 20 columns, where rows are features and columns are samples.

Source

Robert M Flight

pairwise completeness

Description

Calculates the completeness between any two samples using "or", is an entry missing in either X "or" Y.

Usage

pairwise_completeness(
  data_matrix,
  global_na = c(NA, Inf, 0),
  include_only = NULL,
  return_matrix = TRUE
)
pairwise_completeness(
  data_matrix,
  global_na = c(NA, Inf, 0),
  include_only = NULL,
  return_matrix = TRUE
)

Arguments

`data_matrix`	samples are columns, features are rows
`global_na`	globally, what should be treated as NA?
`include_only`	is there certain comparisons to do?
`return_matrix`	should the matrix or data.frame be returned?

Value

matrix of degree of completeness

Rank order row data

Description

Given a data-matrix of numeric data, calculates the rank of each row in each column (feature in sample), gets the median rank across all columns, and returns the original data with missing values set to NA, the reordered data, and a data.frame of the ranks of each feature and the number of missing values.

Usage

rank_order_data(data_matrix, global_na = c(NA, Inf, 0), sample_classes = NULL)
rank_order_data(data_matrix, global_na = c(NA, Inf, 0), sample_classes = NULL)

Arguments

`data_matrix`	matrix or data.frame of values
`global_na`	the values to consider as missing
`sample_classes`	are the columns defined by some metadata?

Value

list with two matrices and a data.frame

turn progress on off

Description

Allow the user to turn progress messages to the console and off. Default is to provide messages to the console.

Usage

show_progress(progress = TRUE)
show_progress(progress = TRUE)

Arguments

progress

logical to have it on or off

Test for left censorship

Description

Does a binomial test to check if the most likely cause of missing values is due to values being below the limit of detection, or coming from a left-censored distribution.

Usage

test_left_censorship(
  data_matrix,
  global_na = c(NA, Inf, 0),
  sample_classes = NULL
)
test_left_censorship(
  data_matrix,
  global_na = c(NA, Inf, 0),
  sample_classes = NULL
)

Arguments

`data_matrix`	matrix or data.frame of numeric data
`global_na`	what represents zero or missing?
`sample_classes`	which samples are in which class

Details

For each feature that is missing in a group of samples, we save as a possibility to test. For each sample, we calculate the median value with any missing values removed. Each feature that had a missing value, we test whether the remaining non-missing values are below the sample median for those samples where the feature is non-missing. A binomial test considers the total number of features instances (minus missing values) as the number of trials, and the number of of features below the sample medians as the number of successes.

There is a bit more detail in the vignette: vignette("testing-for-left-censorship", package = "ICIKendallTau")

Value

data.frame of trials / successes, and binom.test result

Examples

# this example has 80% missing due to left-censorship
data(missing_dataset)
missingness = test_left_censorship(missing_dataset)
missingness$values
missingness$binomial_test

# this example has 80% missing due to left-censorship
data(missing_dataset)
missingness = test_left_censorship(missing_dataset)
missingness$values
missingness$binomial_test

Example RNA-Seq Dataset With Missingness

Description

An example dataset from RNA-seq experiment on yeast, created by Gierliński et al., "Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment", Bioinformatics, 31, 2015 https://doi.org/10.1093/bioinformatics/btv425.

Usage

yeast_missing
yeast_missing

Format

`yeast_missing`

A matrix with 6887 rows (genes) and 96 columns (samples).

Source

https://dx.doi.org/10.6084/M9.FIGSHARE.1425502.V1 https://dx.doi.org/10.6084/M9.FIGSHARE.1425503.V1

`value`	a single or vector of numeric values
`n_rep`	the number of replicates to make (numeric). Default is 1.
`sd`	the standard deviation of the data
`use_zero`	logical, should returned values be around zero or not?

`in_matrix`	numeric matrix of values
`use`	character of "col" or "row" defining columns or rows
`...`	extra parameters to the median function

Package 'ICIKendallTau'

Help Index

Add uniform noise

Description

Usage

Arguments

Value

Calculate matrix medians

Description

Usage

Arguments

Value

Fast correlation with test

Description

Usage

Arguments

Details

Value

convert matrix to data.frame

Description

Usage

Arguments

Details

Value

turn logging off

Description

Usage

turn logging on

Description

Usage

Arguments

Details

Information-content-informed kendall tau

Description

Usage

Arguments

Details

Value

See Also

Examples

Calculates ici-kendall-tau

Description

Usage

Arguments

Details

Value

Examples

Fast kendall tau

Description

Usage

Arguments

Details

Value

log memory usage

Description

Usage

log messages

Description

Usage

Arguments

convert data.frame to matrix

Description

Usage

Arguments

Value

Example Dataset With Missingness

Description

Usage

Format

missing_dataset

Source

pairwise completeness

Description

Usage

Arguments

Value

See Also

Rank order row data

Description

Usage

`missing_dataset`

`yeast_missing`