thin_diff: Binomial thinning for differential expression analysis.

Description

Given a matrix of real RNA-seq counts, this function will add a known amount of signal to the count matrix. This signal is given in the form of a Poisson / negative binomial / mixture of negative binomials generalized linear model with a log (base 2) link. The user may specify any arbitrary design matrix and coefficient matrix. The user may also control for the amount of correlation between the observed covariates and any unobserved surrogate variables. The method is described in detail in Gerard (2020).

Usage

thin_diff(
  mat,
  design_fixed = NULL,
  coef_fixed = NULL,
  design_perm = NULL,
  coef_perm = NULL,
  target_cor = NULL,
  use_sva = FALSE,
  design_obs = NULL,
  relative = TRUE,
  change_colnames = TRUE,
  permute_method = c("hungarian", "marriage"),
  type = c("thin", "mult")
)

Value

A list-like S3 object of class ThinData. Components include some or all of the following:

mat: The modified matrix of counts.
designmat: The design matrix of variables used to simulate signal. This is made by column-binding design_fixed and the permuted version of design_perm.
coefmat: A matrix of coefficients corresponding to designmat.
design_obs: Additional variables that should be included in your design matrix in downstream fittings. This is made by column-binding the vector of 1's with design_obs.
sv: A matrix of estimated surrogate variables. In simulation studies you would probably leave this out and estimate your own surrogate variables.
cormat: A matrix of target correlations between the surrogate variables and the permuted variables in the design matrix. This might be different from the target_cor you input because we pass it through fix_cor to ensure positive semi-definiteness of the resulting covariance matrix.
matching_var: A matrix of simulated variables used to permute design_perm if the target_cor is not NULL.

Arguments

mat: A numeric matrix of RNA-seq counts. The rows index the genes and the columns index the samples.
design_fixed: A numeric design matrix whose rows are fixed and not to be permuted. The rows index the samples and the columns index the variables. The intercept should not be included (though see Section "Unestimable Components").
coef_fixed: A numeric matrix. The coefficients corresponding to design_fixed. The rows index the genes and the columns index the variables.
design_perm: A numeric design matrix whose rows are to be permuted (thus controlling the amount by which they are correlated with the surrogate variables). The rows index the samples and the columns index the variables. The intercept should not be included (though see Section "Unestimable Components").
coef_perm: A numeric matrix. The coefficients corresponding to design_perm. The rows index the genes and the columns index the variables.
target_cor: A numeric matrix of target correlations between the variables in design_perm and the surrogate variables. The rows index the observed covariates and the columns index the surrogate variables. That is, target_cor[i, j] specifies the target correlation between the ith column of design_perm and the jth surrogate variable. The surrogate variables are estimated either using factor analysis or surrogate variable analysis (see the parameter use_sva). The number of columns in target_cor specifies the number of surrogate variables. Set target_cor to NULL to indicate that design_perm and the surrogate variables are independent.
use_sva: A logical. Should we use surrogate variable analysis (Leek and Storey, 2008) using design_obs to estimate the hidden covariates (TRUE) or should we just do an SVD on log2(mat + 0.5) after regressing out design_obs (FALSE)? Setting this to TRUE allows the surrogate variables to be correlated with the observed covariates, while setting this to FALSE assumes that the surrogate variables are orthogonal to the observed covariates. This option only matters if design_obs is not NULL. Defaults to FALSE.
design_obs: A numeric matrix of observed covariates that are NOT to be a part of the signal generating process. Only used in estimating the surrogate variables (if target_cor is not NULL). The intercept should not be included (it will sometimes produce an error if it is included).
relative: A logical. Should we apply relative thinning (TRUE) or absolute thinning (FALSE). Only experts should change the default.
change_colnames: A logical. Should we change the column-names of the design matrices (TRUE) or not (FALSE)? Each new column name begins with either "O" (observed), "P" (permuted), or "F" (fixed), followed by a number. The letters correspond to whether the variables come from design_obs, design_perm, or design_fixed. Setting this to TRUE also changes the column-names of the corresponding coefficient matrices. Defaults to TRUE.
permute_method: Should we use the Gale-Shapley algorithm for stable marriages ("marriage") (Gale and Shapley, 1962) as implemented in the matchingR package, or the Hungarian algorithm (Papadimitriou and Steiglitz, 1982) ("hungarian") as implemented in the clue package (Hornik, 2005)? The Hungarian method almost always works better, so is the default.
type: Should we apply binomial thinning (type = "thin") or just naive multiplication of the counts (type = "mult"). You should always have this set to "thin".

Mathematical Formulation

Let

$N$: Be the number of samples.
$G$: Be the number of genes.
$Y$: Be an $G$ by $N$ matrix of real RNA-seq counts. This is mat.
$X_1$: Be an $N$ by $P_1$ user-provided design matrix. This is design_fixed.
$X_2$: Be an $N$ by $P_2$ user-provided design matrix. This is design_perm.
$X_3$: Be an $N$ by $P_3$ matrix of known covariates. This is design_obs.
$Z$: Be an $N$ by $K$ matrix of unobserved surrogate variables. This is estimated when target_cor is not NULL.
$M$: Be a $G$ by $N$ of additional (unknown) unwanted variation.

We assume that $Y$ is Poisson distributed given $X_3$ and $Z$ such that $$\log_2(EY) = \mu 1_N' + B_3X_3' + AZ' + M.$$ thin_diff() will take as input $X_1$, $X_2$, $B_1$, $B_2$, and will output a $\tilde{Y}$ and $W$ such that $\tilde{Y}$ is Poisson distributed given $X_1$, $X_2$, $X_3$, $W$, $Z$, and $M$ such that $$\log_2(E\tilde{Y}) \approx \tilde{\mu}1_N' + B_1X_1' + B_2X_2'W' + B_3X_3' + AZ' + M,$$ where $W$ is an $N$ by $N$ permutation matrix. $W$ is randomly drawn so that $WX_2$ and $Z$ are correlated approximately according to the target correlation matrix.

The Poisson assumption may be generalized to a mixture of negative binomials.

Unestimable Components

It is possible to include an intercept term or a column from design_obs into either design_fixed or design_perm. This will not produce an error and the specified thinning will be applied. However, If any column of design_fixed or design_perm is a vector of ones or contains a column from design_obs, then the corresponding columns in coef_fixed or coef_perm cannot be estimated by any method. This is represented in the output by having duplicate columns in designmat and design_obs.

Including duplicate columns in design_fixed and design_perm is also allowed but, again, will produce unestimable coefficients.

Including an intercept term in design_obs will produce an error if you are specifying correlated surrogate variables.

Author

David Gerard

References

Gale, David, and Lloyd S. Shapley. "College admissions and the stability of marriage." The American Mathematical Monthly 69, no. 1 (1962): 9-15. tools:::Rd_expr_doi("10.1080/00029890.1962.11989827").
Gerard, D (2020). "Data-based RNA-seq simulations by binomial thinning." BMC Bioinformatics. 21(1), 206. tools:::Rd_expr_doi("10.1186/s12859-020-3450-9").
Hornik K (2005). "A CLUE for CLUster Ensembles." Journal of Statistical Software, 14(12). tools:::Rd_expr_doi("10.18637/jss.v014.i12").
Leek, Jeffrey T., and John D. Storey. "A general framework for multiple testing dependence." Proceedings of the National Academy of Sciences 105, no. 48 (2008): 18718-18723. tools:::Rd_expr_doi("10.1073/pnas.0808709105").
C. Papadimitriou and K. Steiglitz (1982), Combinatorial Optimization: Algorithms and Complexity. Englewood Cliffs: Prentice Hall.

Examples

Run this code

## Generate simulated data with surrogate variables
## In practice, you would obtain mat from a real dataset, not simulate it.
set.seed(1)
n <- 10
p <- 1000
Z <- matrix(abs(rnorm(n, sd = 4)))
alpha <- matrix(abs(rnorm(p, sd = 1)))
mat <- round(2^(alpha %*% t(Z) + abs(matrix(rnorm(n * p, sd = 5),
                                            nrow = p,
                                            ncol = n))))

## Choose simulation parameters
design_perm <- cbind(rep(c(0, 1), length.out = n), runif(n))
coef_perm <- matrix(rnorm(p * ncol(design_perm), sd = 6), nrow = p)

## Specify one surrogate variable (number of columns in taget_cor),
## highly correlated with first observed covariate and uncorrelated
## with second observed covariate
target_cor <- matrix(c(0.9, 0))

## Thin
thout <- thin_diff(mat = mat,
                   design_perm = design_perm,
                   coef_perm = coef_perm,
                   target_cor = target_cor)

## target_cor approximates correlation between estimated surrogate variable
## and matching variable.
cor(thout$matching_var, thout$sv)

## Estimated surrogate variable is associated with true surrogate variable
## (because the signal is strong in this case)
plot(Z, thout$sv, xlab = "True SV", ylab = "Estimated SV")

## So target_cor approximates correlation between surrogate variable and
## matching variables
cor(thout$matching_var, Z)

## Correlation between permuted covariates and surrogate variables are less
## close to target_cor
cor(thout$designmat, Z)

## Estimated signal is correlated to true single. First variable is slightly
## biased because the surrogate variable is not included.
Ynew <- log2(t(thout$mat) + 0.5)
X <- thout$designmat
coef_est <- t(coef(lm(Ynew ~ X))[2:3, ])

plot(thout$coefmat[, 1], coef_est[, 1],
     main = "First Variable",
     xlab = "Coefficient",
     ylab = "Estimated Coefficient")
abline(0, 1, col = 2, lwd = 2)

plot(thout$coefmat[, 2], coef_est[, 2],
     main = "Second Variable",
     xlab = "Coefficient",
     ylab = "Estimated Coefficient")
abline(0, 1, col = 2, lwd = 2)

## But estimated coefficient of the first variable is slightly closer when
## the surrogate variable is included.
Ynew <- log2(t(thout$mat) + 0.5)
X <- cbind(thout$designmat, thout$sv)
coef_est <- t(coef(lm(Ynew ~ X))[2:3, ])

plot(thout$coefmat[, 1], coef_est[, 1],
     main = "First Variable",
     xlab = "Coefficient",
     ylab = "Estimated Coefficient")
abline(0, 1, col = 2, lwd = 2)

plot(thout$coefmat[, 2], coef_est[, 2],
     main = "Second Variable",
     xlab = "Coefficient",
     ylab = "Estimated Coefficient")
abline(0, 1, col = 2, lwd = 2)

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