DifferentialRegulation_bulk: Discover differentially regulated genes from bulk RNA-seq...

In SimoneTiberi/DifferentialRegulation: Differentially regulated genes from scRNA-seq data

Discover differentially regulated genes from bulk RNA-seq data

Description

DifferentialRegulation_bulk identified differentially regulated genes between two conditions (e.g., healthy vs. disease or treated vs. untreated) in each cluster of cells. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a differential testing is performed via a multivariate Wald test on the posterior densities of the group-level US (Unspliced, and Spliced) counts relative abundance.

Usage

DifferentialRegulation_bulk(
  SE,
  EC_list,
  design,
  sample_col_name = "sample",
  group_col_name = "group",
  min_counts_per_transcript_per_group = 10,
  N_MCMC = 2000,
  burn_in = 500,
  min_counts_ECs = 0,
  n_cores = 1,
  undersampling_int = 10,
  traceplot = FALSE
)

Arguments

SE	a SummarizedExperiment object, computed via load_bulk_US.
EC_list	a list, computed via load_bulk_EC.
design	a data.frame indicating the design of the experiment with one row for each sample; 'design' must contain a column with the sample id and one with the group id.
sample_col_name	a character ("sample" by default), indicating the column name of the 'design' element which stores the sample id.
group_col_name	a character ("group" by default), indicating the column name of the 'design' element which stores the group id.
min_counts_per_transcript_per_group	minimum number of counts per transcript, across all samples in a group Only genes with at least 'min_counts_per_transcript_per_group' counts in both groups of samples will be analyzed.
N_MCMC	the number of iterations for the MCMC algorithm (including burn-in). Min 2*10^3. If our algorithm does not converge (according to Heidelberger and Welch's convergence diagnostic), we automatically double N_MCMC and burn_in, and run it a second time (a message will be printed on screen to inform users).
burn_in	the length of the burn-in; i.e., the initial part of the MCMC chain to be discarded (before convergence is reached). Min 500. If no convergence is reached, the 'burn_in' is automatically increased (up to N_MCMC/2) according to the convergence detected by Heidelberger and Welch's convergence diagnostic. If our algorithm does not converge even after increasing the burn-in, we automatically double N_MCMC and burn_in, and run it a second time (a message will be printed on screen to inform users).
min_counts_ECs	equivalence classes (ECs) filter. 'min_counts_ECs' indicates the minimum number of counts (across all cells in a cell cluster) for each equivalence class; by default all ECs are considered (min_counts_ECs = 0). ECs with less or equal than 'min_counts_ECs' will be discarded. Increasing 'min_counts_ECs' will marginally decrease computational cost computational at the cost of a marginal loss in performance.
n_cores	the number of cores to parallelize the tasks on. Note that only a minor part of the function runs in parallel (i.e., the prior for the dispersion), while most of the function does not (e.g., the MCMC).
undersampling_int	the undersampling of the latent variables. While model parameters are sampled at each iteration, RNA-seq counts are allocated to their transcript (and spliced/unspliced) version of origin, every 'undersampling_int' iterations. Increasing 'undersampling_int' will decrease the runtime, but may marginally affect performance. In our benchmarks, no differences in performance were observed for values up to 10.
traceplot	a logical value indicating whether to return the posterior chain of "pi_U", for both groups (i.e., the group-level relative abundance of unspliced reads). If TRUE, the posterior chains are stored in 'MCMC_U' object, and can be plotted via 'plot_bulk_traceplot' function.

Value

A list of data.frame objects. 'Differential_results' contains results from differential testing only; 'Convergence_results' contains information about convergence of posterior chains; 'MCMC_U' (only if traceplot is TRUE) contains the posterior chains for 'pi_U' in both groups. Columns 'Gene_id' and 'Cluster_id' contain the gene and cell-cluster name, while 'p_val', 'p_adj.loc' and 'p_adj.glb' report the raw p-values, locally and globally adjusted p-values, via Benjamini and Hochberg (BH) correction. In locally adjusted p-values ('p_adj.loc') BH correction is applied to each cluster separately, while in globally adjusted p-values ('p_adj.glb') BH correction is performed to the results from all clusters. Columns 'pi' and 'sd' indicate the proportion and standard deviation, respectively, 'S', 'U' and 'A' refer to Spliced, Unspliced and Ambiguous counts, respectively, while 'gr_A' and 'gr_B' refer to group A and B, respectively. For instance, columns 'pi_S-gr_A' and 'sd_S-gr_A' indicate the estimates and standard deviation (sd) for the proportion of Spliced (pi_S) and Unspliced (pi_U) counts in group A, respectively.

Author(s)

Simone Tiberi [e-mail]

See Also

load_bulk_EC, load_bulk_US, plot_pi, plot_bulk_traceplot

Examples

# load internal data to the package:
data_dir = system.file("extdata", package = "DifferentialRegulation")

# specify samples ids:
sample_ids = paste0("sample", seq_len(6))

# US estimates:
quant_files = file.path(data_dir, "salmon", sample_ids, "quant.sf")
file.exists(quant_files)

# Equivalence classes:
equiv_classes_files = file.path(data_dir, "salmon", sample_ids, "aux_info/eq_classes.txt.gz")
file.exists(equiv_classes_files)

# load EC:
EC_list = load_bulk_EC(path_to_eq_classes = equiv_classes_files,
                       n_cores = 2)

# load US estimated counts:
SE = load_bulk_US(quant_files,
                  sample_ids)

# define the design of the study:
group_names = rep(c("A", "B"), each = 3)
design = data.frame(sample = sample_ids,
                    group = group_names)
design

set.seed(1609612) 
results = DifferentialRegulation_bulk(SE = SE, 
                                      EC_list = EC_list,
                                      design = design, 
                                      n_cores = 2,
                                      traceplot = TRUE)

names(results)
  
# We visualize differential results:
head(results$Differential_results)

# plot top (i.e., most significant) result:
# plot USA proportions:
plot_bulk_pi(results,
             transcript_id = results$Differential_results$Transcript_id[1])

# plot the corresponding traceplot:
plot_bulk_traceplot(results,
                    transcript_id = results$Differential_results$Transcript_id[1])

SimoneTiberi/DifferentialRegulation documentation built on Aug. 22, 2024, 7:43 a.m.