,
Charlotte Soneson [aut] | Title: | Differentially regulated genes from scRNA-seq data |
|---|---|
| Description: | DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. From a mathematical point of view, DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. Furthermore, our method also deals with two major sources of mapping uncertainty: i) 'ambiguous' reads, compatible with both spliced and unspliced versions of a gene, and ii) reads mapping to multiple genes. In particular, ambiguous reads are treated separately from spliced and unsplced reads, while reads that are compatible with multiple genes are allocated to the gene of origin. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs). |
| Authors: | Simone Tiberi [aut, cre] ,
Charlotte Soneson [aut] |
| Maintainer: | Simone Tiberi <[email protected]> |
| License: | GPL-3 |
| Version: | 2.3.0 |
| Built: | 2024-07-12 04:50:41 UTC |
| Source: | https://github.com/bioc/DifferentialRegulation |
DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. From a mathematical point of view, DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. Furthermore, our method also deals with two major sources of mapping uncertainty: i) 'ambiguous' reads, compatible with both spliced and unspliced versions of a gene, and ii) reads mapping to multiple genes. In particular, ambiguous reads are treated separately from spliced and unsplced reads, while reads that are compatible with multiple genes are allocated to the gene of origin. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs).
The DESCRIPTION file:
| Package: | DifferentialRegulation |
| Type: | Package |
| Title: | Differentially regulated genes from scRNA-seq data |
| Version: | 2.3.0 |
| Description: | DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. From a mathematical point of view, DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. Furthermore, our method also deals with two major sources of mapping uncertainty: i) 'ambiguous' reads, compatible with both spliced and unspliced versions of a gene, and ii) reads mapping to multiple genes. In particular, ambiguous reads are treated separately from spliced and unsplced reads, while reads that are compatible with multiple genes are allocated to the gene of origin. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs). |
| Authors@R: | c(person(given = "Simone", family = "Tiberi", role = c("aut", "cre"), email = "[email protected]", comment = c(ORCID = "0000-0002-3054-9964")), person(given = "Charlotte", family = "Soneson", role = c("aut"), email = "[email protected]", comment = c(ORCID = "0000-0003-3833-2169"))) |
| biocViews: | DifferentialSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization, SingleCell, GeneTarget |
| License: | GPL-3 |
| Depends: | R (>= 4.3.0) |
| Imports: | methods, Rcpp, doRNG, MASS, data.table, doParallel, parallel, foreach, stats, BANDITS, Matrix, SingleCellExperiment, SummarizedExperiment, ggplot2, tximport, gridExtra |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, rmarkdown, testthat, BiocStyle |
| SystemRequirements: | C++17 |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.2.3 |
| ByteCompile: | true |
| Encoding: | UTF-8 |
| URL: | https://github.com/SimoneTiberi/DifferentialRegulation |
| BugReports: | https://github.com/SimoneTiberi/DifferentialRegulation/issues |
| Repository: | https://bioc.r-universe.dev |
| RemoteUrl: | https://github.com/bioc/DifferentialRegulation |
| RemoteRef: | HEAD |
| RemoteSha: | fb49478c3c45136152b2010363a85b38e371d0e0 |
| Author: | Simone Tiberi [aut, cre] (<https://orcid.org/0000-0002-3054-9964>), Charlotte Soneson [aut] (<https://orcid.org/0000-0003-3833-2169>) |
| Maintainer: | Simone Tiberi <[email protected]> |
Questions relative to DifferentialRegulation should be reported as a new issue at https://github.com/SimoneTiberi/DifferentialRegulatio/issues.
To access the vignettes, type: browseVignettes("DifferentialRegulation").
Index of help topics:
DifferentialRegulation
Discover differentially regulated genes from
single-cell RNA-seq data
DifferentialRegulation-package
Differentially regulated genes from scRNA-seq
data
DifferentialRegulation_bulk
Discover differentially regulated genes from
bulk RNA-seq data
compute_PB_counts Discover differentially regulated genes
load_EC Create a list containing the equivalence
classes objects for the single-cell RNA-seq
data
load_USA Creates a SingleCellExperiment containing the
estimated USA counts for the single-cell
RNA-seq data
load_bulk_EC Create a list containing the equivalence
classes objects for the bulk RNA-seq data
load_bulk_US Creates a SummarizedExperiment containing the
estimated US counts for the bulk RNA-seq data
plot_bulk_pi Plot the estimated proportions of US counts in
each group - bulk RNA-seq data
plot_bulk_traceplot Traceplot of the posterior chain of the
proportion (i.e., pi) of unspliced (U) counts
in each group - bulk RNA-seq data
plot_pi Plot the estimated proportions of US or USA
counts in each group - single-cell RNA-seq data
plot_traceplot Traceplot of the posterior chain of the
proportion (i.e., pi) of unspliced (U) counts
in each group - single-cell RNA-seq data
Simone Tiberi [email protected]
compute_PB_counts computese the pseudo-bulk (PB) counts,
needed to perform differential testing by DifferentialRegulation.
compute_PB_counts( sce, EC_list, design, sample_col_name = "sample", group_col_name = "group", sce_cluster_name = "cell_type", min_cells_per_cluster = 100, min_counts_per_gene_per_group = 20, min_counts_ECs = 0 )compute_PB_counts( sce, EC_list, design, sample_col_name = "sample", group_col_name = "group", sce_cluster_name = "cell_type", min_cells_per_cluster = 100, min_counts_per_gene_per_group = 20, min_counts_ECs = 0 )
sce |
a |
EC_list |
a |
design |
a |
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. |
sce_cluster_name |
a character ("cell_type" by default), indicating the name of the 'colData(sce)' element, which stores the cluster id of each cell (i.e., colData(sce)$name_cluster). |
min_cells_per_cluster |
cell cluster (e.g., cell-type) filter. 'min_cells_per_cluster' is the minimum number of cells, across all samples and groups, for a cell cluster to be considered. Cell clusters with less than 'min_cells_per_cluster' cells will not be analyzed. |
min_counts_per_gene_per_group |
minimum number of counts per gene, in each cell, across all samples of every group. In each cell cluster, only genes with at least 'min_counts_per_gene_per_group' counts in both groups of samples will be analyzed. |
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. |
A list of objects required perform differential testing by DifferentialRegulation.
Simone Tiberi [email protected]
load_EC, load_USA, DifferentialRegulation, plot_pi
# load internal data to the package: data_dir = system.file("extdata", package = "DifferentialRegulation") # specify samples ids: sample_ids = paste0("organoid", c(1:3, 16:18)) # set directories of each sample input data (obtained via alevin-fry): base_dir = file.path(data_dir, "alevin-fry", sample_ids) file.exists(base_dir) # set paths to USA counts, cell id and gene id: # Note that alevin-fry needs to be run with '--use-mtx' option # to store counts in a 'quants_mat.mtx' file. path_to_counts = file.path(base_dir,"/alevin/quants_mat.mtx") path_to_cell_id = file.path(base_dir,"/alevin/quants_mat_rows.txt") path_to_gene_id = file.path(base_dir,"/alevin/quants_mat_cols.txt") # load USA counts: sce = load_USA(path_to_counts, path_to_cell_id, path_to_gene_id, sample_ids) # define the design of the study: design = data.frame(sample = sample_ids, group = c( rep("3 mon", 3), rep("6 mon", 3) )) design # cell types should be assigned to each cell; # here we load pre-computed cell types: path_to_DF = file.path(data_dir,"DF_cell_types.txt") DF_cell_types = read.csv(path_to_DF, sep = "\t", header = TRUE) matches = match(colnames(sce), DF_cell_types$cell_id) sce$cell_type = DF_cell_types$cell_type[matches] # set paths to EC counts and ECs: path_to_EC_counts = file.path(base_dir,"/alevin/geqc_counts.mtx") path_to_EC = file.path(base_dir,"/alevin/gene_eqclass.txt.gz") # load EC counts: EC_list = load_EC(path_to_EC_counts, path_to_EC, path_to_cell_id, path_to_gene_id, sample_ids) PB_counts = compute_PB_counts(sce = sce, EC_list = EC_list, design = design, sample_col_name = "sample", group_col_name = "group", sce_cluster_name = "cell_type", min_cells_per_cluster = 100, min_counts_per_gene_per_group = 20)# load internal data to the package: data_dir = system.file("extdata", package = "DifferentialRegulation") # specify samples ids: sample_ids = paste0("organoid", c(1:3, 16:18)) # set directories of each sample input data (obtained via alevin-fry): base_dir = file.path(data_dir, "alevin-fry", sample_ids) file.exists(base_dir) # set paths to USA counts, cell id and gene id: # Note that alevin-fry needs to be run with '--use-mtx' option # to store counts in a 'quants_mat.mtx' file. path_to_counts = file.path(base_dir,"/alevin/quants_mat.mtx") path_to_cell_id = file.path(base_dir,"/alevin/quants_mat_rows.txt") path_to_gene_id = file.path(base_dir,"/alevin/quants_mat_cols.txt") # load USA counts: sce = load_USA(path_to_counts, path_to_cell_id, path_to_gene_id, sample_ids) # define the design of the study: design = data.frame(sample = sample_ids, group = c( rep("3 mon", 3), rep("6 mon", 3) )) design # cell types should be assigned to each cell; # here we load pre-computed cell types: path_to_DF = file.path(data_dir,"DF_cell_types.txt") DF_cell_types = read.csv(path_to_DF, sep = "\t", header = TRUE) matches = match(colnames(sce), DF_cell_types$cell_id) sce$cell_type = DF_cell_types$cell_type[matches] # set paths to EC counts and ECs: path_to_EC_counts = file.path(base_dir,"/alevin/geqc_counts.mtx") path_to_EC = file.path(base_dir,"/alevin/gene_eqclass.txt.gz") # load EC counts: EC_list = load_EC(path_to_EC_counts, path_to_EC, path_to_cell_id, path_to_gene_id, sample_ids) PB_counts = compute_PB_counts(sce = sce, EC_list = EC_list, design = design, sample_col_name = "sample", group_col_name = "group", sce_cluster_name = "cell_type", min_cells_per_cluster = 100, min_counts_per_gene_per_group = 20)
DifferentialRegulation 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 USA (Unspliced, Spliced and Ambiguous) counts relative abundance.
DifferentialRegulation( PB_counts, n_cores = NULL, N_MCMC = 2000, burn_in = 500, undersampling_int = 10, traceplot = FALSE )DifferentialRegulation( PB_counts, n_cores = NULL, N_MCMC = 2000, burn_in = 500, undersampling_int = 10, traceplot = FALSE )
PB_counts |
a |
n_cores |
the number of cores to parallelize the tasks on. Since parallelization is at the cluster level (each cluster is parallelized on a thread), we suggest setting n_cores to the number of clusters (e.g., cell-types), as set by default if 'n_cores' is not specified. |
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). |
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_traceplot' function. |
A list of 4 data.frame objects.
'Differential_results' contains results from differential testing only;
'US_results' has results for the proportion of Spliced and Unspliced counts
(Ambiguous counts are allocated 50:50 to Spliced and Unspliced);
'USA_results' includes results for the proportion of Spliced, Unspliced and Ambiguous counts
(Ambiguous counts are reported separately from Spliced and Unspliced counts);
'Convergence_results' contains information about convergence of posterior chains.
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.
Simone Tiberi [email protected]
load_EC, load_USA, codecompute_PB_counts, plot_pi, plot_traceplot
# load internal data to the package: data_dir = system.file("extdata", package = "DifferentialRegulation") # specify samples ids: sample_ids = paste0("organoid", c(1:3, 16:18)) # set directories of each sample input data (obtained via alevin-fry): base_dir = file.path(data_dir, "alevin-fry", sample_ids) file.exists(base_dir) # set paths to USA counts, cell id and gene id: # Note that alevin-fry needs to be run with '--use-mtx' option # to store counts in a 'quants_mat.mtx' file. path_to_counts = file.path(base_dir,"/alevin/quants_mat.mtx") path_to_cell_id = file.path(base_dir,"/alevin/quants_mat_rows.txt") path_to_gene_id = file.path(base_dir,"/alevin/quants_mat_cols.txt") # load USA counts: sce = load_USA(path_to_counts, path_to_cell_id, path_to_gene_id, sample_ids) # define the design of the study: design = data.frame(sample = sample_ids, group = c( rep("3 mon", 3), rep("6 mon", 3) )) design # cell types should be assigned to each cell; # here we load pre-computed cell types: path_to_DF = file.path(data_dir,"DF_cell_types.txt") DF_cell_types = read.csv(path_to_DF, sep = "\t", header = TRUE) matches = match(colnames(sce), DF_cell_types$cell_id) sce$cell_type = DF_cell_types$cell_type[matches] # set paths to EC counts and ECs: path_to_EC_counts = file.path(base_dir,"/alevin/geqc_counts.mtx") path_to_EC = file.path(base_dir,"/alevin/gene_eqclass.txt.gz") # load EC counts: EC_list = load_EC(path_to_EC_counts, path_to_EC, path_to_cell_id, path_to_gene_id, sample_ids) PB_counts = compute_PB_counts(sce = sce, EC_list = EC_list, design = design, sample_col_name = "sample", group_col_name = "group", sce_cluster_name = "cell_type", min_cells_per_cluster = 100, min_counts_per_gene_per_group = 20) # to reduce memory usage, we can remove the EC_list object: rm(EC_list) set.seed(1609612) results = DifferentialRegulation(PB_counts, n_cores = 2, traceplot = TRUE) names(results) # We visualize differential results: head(results$Differential_results) # plot top (i.e., most significant) result: # plot USA proportions: plot_pi(results, type = "USA", gene_id = results$Differential_results$Gene_id[1], cluster_id = results$Differential_results$Cluster_id[1]) # plot US proportions: plot_pi(results, type = "US", gene_id = results$Differential_results$Gene_id[1], cluster_id = results$Differential_results$Cluster_id[1]) # plot the corresponding traceplot: plot_traceplot(results, gene_id = results$Differential_results$Gene_id[1], cluster_id = results$Differential_results$Cluster_id[1])# load internal data to the package: data_dir = system.file("extdata", package = "DifferentialRegulation") # specify samples ids: sample_ids = paste0("organoid", c(1:3, 16:18)) # set directories of each sample input data (obtained via alevin-fry): base_dir = file.path(data_dir, "alevin-fry", sample_ids) file.exists(base_dir) # set paths to USA counts, cell id and gene id: # Note that alevin-fry needs to be run with '--use-mtx' option # to store counts in a 'quants_mat.mtx' file. path_to_counts = file.path(base_dir,"/alevin/quants_mat.mtx") path_to_cell_id = file.path(base_dir,"/alevin/quants_mat_rows.txt") path_to_gene_id = file.path(base_dir,"/alevin/quants_mat_cols.txt") # load USA counts: sce = load_USA(path_to_counts, path_to_cell_id, path_to_gene_id, sample_ids) # define the design of the study: design = data.frame(sample = sample_ids, group = c( rep("3 mon", 3), rep("6 mon", 3) )) design # cell types should be assigned to each cell; # here we load pre-computed cell types: path_to_DF = file.path(data_dir,"DF_cell_types.txt") DF_cell_types = read.csv(path_to_DF, sep = "\t", header = TRUE) matches = match(colnames(sce), DF_cell_types$cell_id) sce$cell_type = DF_cell_types$cell_type[matches] # set paths to EC counts and ECs: path_to_EC_counts = file.path(base_dir,"/alevin/geqc_counts.mtx") path_to_EC = file.path(base_dir,"/alevin/gene_eqclass.txt.gz") # load EC counts: EC_list = load_EC(path_to_EC_counts, path_to_EC, path_to_cell_id, path_to_gene_id, sample_ids) PB_counts = compute_PB_counts(sce = sce, EC_list = EC_list, design = design, sample_col_name = "sample", group_col_name = "group", sce_cluster_name = "cell_type", min_cells_per_cluster = 100, min_counts_per_gene_per_group = 20) # to reduce memory usage, we can remove the EC_list object: rm(EC_list) set.seed(1609612) results = DifferentialRegulation(PB_counts, n_cores = 2, traceplot = TRUE) names(results) # We visualize differential results: head(results$Differential_results) # plot top (i.e., most significant) result: # plot USA proportions: plot_pi(results, type = "USA", gene_id = results$Differential_results$Gene_id[1], cluster_id = results$Differential_results$Cluster_id[1]) # plot US proportions: plot_pi(results, type = "US", gene_id = results$Differential_results$Gene_id[1], cluster_id = results$Differential_results$Cluster_id[1]) # plot the corresponding traceplot: plot_traceplot(results, gene_id = results$Differential_results$Gene_id[1], cluster_id = results$Differential_results$Cluster_id[1])
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.
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 )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 )
SE |
a |
EC_list |
a |
design |
a |
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. |
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.
Simone Tiberi [email protected]
load_bulk_EC, load_bulk_US, plot_pi, plot_bulk_traceplot
# 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])# 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])
load_bulk_EC imports the bulk equivalence classes (computed by salmon), and stores them into a list.
load_bulk_EC(path_to_eq_classes = NULL, n_cores = NULL)load_bulk_EC(path_to_eq_classes = NULL, n_cores = NULL)
path_to_eq_classes |
a vector of length equals to the number of samples: each element indicates the path to the equivalence classes counts of the respective sample (i.e., aux_info/eq_classes.txt.gz file). |
n_cores |
the number of cores to parallelize the tasks on. Since parallelization is at the sample level (each sample is parallelized on a thread), we suggest setting n_cores to the number of sample, as set by default if 'n_cores' is not specified. |
A list object.
Simone Tiberi [email protected]
load_bulk_US, DifferentialRegulation_bulk
# load internal data to the package: data_dir = system.file("extdata", package = "DifferentialRegulation") # specify samples ids: sample_ids = paste0("sample", seq_len(6)) # 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 internal data to the package: data_dir = system.file("extdata", package = "DifferentialRegulation") # specify samples ids: sample_ids = paste0("sample", seq_len(6)) # 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_bulk_US imports the bulk estimated US (Unspliced, and Spliced)
counts (computed by salmon), and stores them into a SummarizedExperiment object.
load_bulk_US(path_to_quant_files, sample_ids)load_bulk_US(path_to_quant_files, sample_ids)
path_to_quant_files |
a vector of length equals to the number of samples: each element indicates the path to the US estimated count matrix of the respective sample (i.e., quant.sf file). |
sample_ids |
a vector of length equals to the number of samples, indicating the names of the respective samples in 'path_to_quant_files'. |
A SummarizedExperiment object.
Simone Tiberi [email protected]
load_bulk_EC, DifferentialRegulation_bulk
# 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) # load US estimated counts: SE = load_bulk_US(quant_files, sample_ids)# 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) # load US estimated counts: SE = load_bulk_US(quant_files, sample_ids)
load_EC imports the single-cell equivalence classes (computed by alevin-fry), and stores them into a list.
load_EC( path_to_EC_counts, path_to_EC, path_to_cell_id, path_to_gene_id, sample_ids )load_EC( path_to_EC_counts, path_to_EC, path_to_cell_id, path_to_gene_id, sample_ids )
path_to_EC_counts |
a vector of length equals to the number of samples: each element indicates the path to the equivalence classes counts of the respective sample (i.e., geqc_counts.mtx file). |
path_to_EC |
a vector of length equals to the number of samples: each element indicates the path to the equivalence classes of the respective sample (i.e., gene_eqclass.txt.gz file). |
path_to_cell_id |
a vector of length equals to the number of samples: each element indicates the path to the cell ids of the respective sample (i.e., quants_mat_rows.txt file). |
path_to_gene_id |
a vector of length equals to the number of samples: each element indicates the path to the gene ids of the respective sample (i.e., quants_mat_cols.txt file). |
sample_ids |
a vector of length equals to the number of samples: each element indicates the name of the sample. |
A list object.
Simone Tiberi [email protected]
load_USA, DifferentialRegulation
# load internal data to the package: data_dir = system.file("extdata", package = "DifferentialRegulation") # specify samples ids: sample_ids = paste0("organoid", c(1:3, 16:18)) # set directories of each sample input data (obtained via alevin-fry): base_dir = file.path(data_dir, "alevin-fry", sample_ids) file.exists(base_dir) # set paths to USA counts, cell id, gene id, EC counts and ECs: # Note that alevin-fry needs to be run with `--use-mtx` option # to store counts in a `quants_mat.mtx` file. path_to_cell_id = file.path(base_dir,"/alevin/quants_mat_rows.txt") path_to_gene_id = file.path(base_dir,"/alevin/quants_mat_cols.txt") path_to_EC_counts = file.path(base_dir,"/alevin/geqc_counts.mtx") path_to_EC = file.path(base_dir,"/alevin/gene_eqclass.txt.gz") # load EC counts: EC_list = load_EC(path_to_EC_counts, path_to_EC, path_to_cell_id, path_to_gene_id, sample_ids)# load internal data to the package: data_dir = system.file("extdata", package = "DifferentialRegulation") # specify samples ids: sample_ids = paste0("organoid", c(1:3, 16:18)) # set directories of each sample input data (obtained via alevin-fry): base_dir = file.path(data_dir, "alevin-fry", sample_ids) file.exists(base_dir) # set paths to USA counts, cell id, gene id, EC counts and ECs: # Note that alevin-fry needs to be run with `--use-mtx` option # to store counts in a `quants_mat.mtx` file. path_to_cell_id = file.path(base_dir,"/alevin/quants_mat_rows.txt") path_to_gene_id = file.path(base_dir,"/alevin/quants_mat_cols.txt") path_to_EC_counts = file.path(base_dir,"/alevin/geqc_counts.mtx") path_to_EC = file.path(base_dir,"/alevin/gene_eqclass.txt.gz") # load EC counts: EC_list = load_EC(path_to_EC_counts, path_to_EC, path_to_cell_id, path_to_gene_id, sample_ids)
load_USA imports the single-cell estimated USA (Unspliced, Spliced and Ambiguous)
counts (computed by alevin-fry), and stores them into a SingleCellExperiment object.
load_USA(path_to_counts, path_to_cell_id, path_to_gene_id, sample_ids)load_USA(path_to_counts, path_to_cell_id, path_to_gene_id, sample_ids)
path_to_counts |
a vector of length equals to the number of samples: each element indicates the path to the USA estimated count matrix of the respective sample (i.e., quants_mat.mtx file). |
path_to_cell_id |
a vector of length equals to the number of samples: each element indicates the path to the cell ids of the respective sample (i.e., quants_mat_rows.txt file). |
path_to_gene_id |
a vector of length equals to the number of samples: each element indicates the path to the gene ids of the respective sample (i.e., quants_mat_cols.txt file). |
sample_ids |
a vector of length equals to the number of samples: each element indicates the name of the sample. |
A SingleCellExperiment object.
Simone Tiberi [email protected]
load_EC, DifferentialRegulation
# load internal data to the package: data_dir = system.file("extdata", package = "DifferentialRegulation") # specify samples ids: sample_ids = paste0("organoid", c(1:3, 16:18)) # set directories of each sample input data (obtained via alevin-fry): base_dir = file.path(data_dir, "alevin-fry", sample_ids) file.exists(base_dir) # set paths to USA counts, cell id and gene id: # Note that alevin-fry needs to be run with `--use-mtx` option # to store counts in a `quants_mat.mtx` file. path_to_counts = file.path(base_dir,"/alevin/quants_mat.mtx") path_to_cell_id = file.path(base_dir,"/alevin/quants_mat_rows.txt") path_to_gene_id = file.path(base_dir,"/alevin/quants_mat_cols.txt") # load USA counts: sce = load_USA(path_to_counts, path_to_cell_id, path_to_gene_id, sample_ids)# load internal data to the package: data_dir = system.file("extdata", package = "DifferentialRegulation") # specify samples ids: sample_ids = paste0("organoid", c(1:3, 16:18)) # set directories of each sample input data (obtained via alevin-fry): base_dir = file.path(data_dir, "alevin-fry", sample_ids) file.exists(base_dir) # set paths to USA counts, cell id and gene id: # Note that alevin-fry needs to be run with `--use-mtx` option # to store counts in a `quants_mat.mtx` file. path_to_counts = file.path(base_dir,"/alevin/quants_mat.mtx") path_to_cell_id = file.path(base_dir,"/alevin/quants_mat_rows.txt") path_to_gene_id = file.path(base_dir,"/alevin/quants_mat_cols.txt") # load USA counts: sce = load_USA(path_to_counts, path_to_cell_id, path_to_gene_id, sample_ids)
plot_bulk_pi plots the posterior means of the proportions of US counts, in each group.
If 'CI' is TRUE, a profile Wald type confidence interval will also be added;
the level of the confidence interval is specified by 'CI_level'.
plot_bulk_pi(results, transcript_id, CI = TRUE, CI_level = 0.95)plot_bulk_pi(results, transcript_id, CI = TRUE, CI_level = 0.95)
results |
a |
transcript_id |
a character, indicating the transcript to plot. |
CI |
a logical ('TRUE' by default), indicating whether to plot a profile Wald type confidence interval around the estimated proportions. |
CI_level |
a numeric between 0 and 1, indicating the level of the confidence interval. |
A ggplot object.
Simone Tiberi [email protected]
# see the example of DifferentialRegulation_bulk function: help(DifferentialRegulation_bulk)# see the example of DifferentialRegulation_bulk function: help(DifferentialRegulation_bulk)
plot_bulk_traceplot plots the traceplot of the posterior chain
of the proportion (i.e., pi) of unspliced (U) counts in each group.
The vertical grey dashed line indicates the burn-in
(the iterations on the left side of the burn-in are discarded in posterior analyses).
plot_bulk_traceplot(results, transcript_id)plot_bulk_traceplot(results, transcript_id)
results |
a |
transcript_id |
a character, indicating the transcript to plot. |
A gtable object.
Simone Tiberi [email protected]
# see the example of DifferentialRegulation_bulk function: help(DifferentialRegulation_bulk)# see the example of DifferentialRegulation_bulk function: help(DifferentialRegulation_bulk)
plot_pi plots the posterior means of the proportions
of US (if 'type' = 'US') or USA (if 'type' = 'USA') counts, in each group.
If 'CI' is TRUE, a profile Wald type confidence interval will also be added;
the level of the confidence interval is specified by 'CI_level'.
plot_pi(results, gene_id, cluster_id, type = "USA", CI = TRUE, CI_level = 0.95)plot_pi(results, gene_id, cluster_id, type = "USA", CI = TRUE, CI_level = 0.95)
results |
a |
gene_id |
a character, indicating the gene to plot. |
cluster_id |
a character, indicating the cell cluster to plot. |
type |
a character (either 'SU' or 'SUA'). If "SU", it plots the proportion of Spliced and Unspliced counts (Ambiguous counts are assigned 50:50 to Spliced and Unspliced counts). If "SUA" (default), it plots the proportion of Spliced, Unspliced and Ambiguous counts (Ambiguous counts are kept separately). Note that, although US reads are easier to interpret, USA reads more closely reflect what is being compared between conditions. |
CI |
a logical ('TRUE' by default), indicating whether to plot a profile Wald type confidence interval around the estimated proportions. |
CI_level |
a numeric between 0 and 1, indicating the level of the confidence interval. |
A ggplot object.
Simone Tiberi [email protected]
# see the example of DifferentialRegulation function: help(DifferentialRegulation)# see the example of DifferentialRegulation function: help(DifferentialRegulation)
plot_traceplot plots the traceplot of the posterior chain
of the proportion (i.e., pi) of unspliced (U) counts in each group.
The vertical grey dashed line indicates the burn-in
(the iterations on the left side of the burn-in are discarded in posterior analyses).
plot_traceplot(results, gene_id, cluster_id)plot_traceplot(results, gene_id, cluster_id)
results |
a |
gene_id |
a character, indicating the gene to plot. |
cluster_id |
a character, indicating the cell cluster to plot. |
A gtable object.
Simone Tiberi [email protected]
# see the example of DifferentialRegulation function: help(DifferentialRegulation)# see the example of DifferentialRegulation function: help(DifferentialRegulation)