Filter significant results.
top_results returns the significant results obtained via svg_test and individual_svg.
It can also be used to merge gene- and cluster-level results into a single object.
Usage
top_results(
gene_results = NULL,
cluster_results,
cluster = NULL,
select = "both",
high_low = NULL
)Arguments
- gene_results
Results returned from
svg_test.- cluster_results
Results returned from
individual_svg.- cluster
A character indicating the cluster(s) whose results have to be returned. Results from all clusters are returned by default ("NULL").
- select
A character indicating what results should be returned ("FDR", "logFC", or "both"). Only used if "cluster_results" are provided. By default ("both"), both FDR and logFC are returned.
- high_low
A character indicating whether to filter results or not. Only used if "cluster_results" are provided, and one cluster is specified in "cluster" parameter. By default (NULL), all results are returned in a single data.frame. If "high" or "HIGH", we only return SVGs with average abundace in "cluster" higher than in the rest of the tissue (i.e., logFC > 0). If "low" or "LOW", we only return SVGs with average abundace in "cluster" lower than in the rest of the tissue (i.e., logFC < 0). If "both" or "BOTH", then both "high" and "low" results are returned, but in two separate data.frames.
Value
A data.frame object or a list of data.frame with results.
- When only “cluster_results” is provided, results are reported as a data.frame with columns for
gene names (gene_id), spatial clusters affected by SV (Cluster), cluster-specific likelihood ratio test statistics (LR),
cluster-specific average (across spots) log-2 counts per million (logCPM), cluster-specific log2-fold changes (logFC),
cluster-specific raw p-values (PValue), and Benjamini-Hochberg adjusted p-values (FDR) for each spatial cluster.
- When “gene_results” and “cluster_results” are given, results are reported as a data.frame that merges gene-
and cluster-level results.
- If “cluster” is specified, the function returns a subset data.frame for the given cluster, which contains cluster name,
gene name, LR, logCPM, logFC, PValue and FDR, ordered by FDR for the specified cluster.
- If “high_low” is set, the function returns a list of data.frame that contains subsets of results for genes with
higher and/or lower expression in the given cluster compared to the rest of the tissue.
Examples
# load pre-computed results (obtained via `svg_test`)
data("results_svg_test", package = "DESpace")
# svg_test returns of a list of 2 objects:
# "gene_results": a dataframe contains main edgeR test results;
# "estimated_y": a DGEList object contains the estimated common dispersion,
# which can later be used to speed-up calculation when testing individual clusters.
# We visualize differential results:
head(results_svg_test$gene_results, 3)
#> gene_id LR logCPM PValue FDR
#> ENSG00000115756 ENSG00000115756 1073.3689 15.89723 1.204871e-228 2.855545e-226
#> ENSG00000054690 ENSG00000054690 1056.7744 15.48088 4.686692e-225 5.553730e-223
#> ENSG00000111716 ENSG00000111716 752.4418 16.58993 2.894030e-159 2.286283e-157
# load pre-computed results (obtained via `individual_svg`)
data("results_individual_svg", package = "DESpace")
# Function `individual_svg()` can be used to identify SVGs for each individual cluster.
# `individual_svg()` returns a list containing the results of individual clusters.
# For each cluster, results are reported as a data.frame,
# where columns For each cluster, results are reported as a data.frame,
# where columns contain gene names (`genes`), likelihood ratio (`LR`),
# log2-fold changes (`logFC`) and adjusted p-value (`FDR`).
#
# Combine gene-and cluster-level results
merge_res = top_results(results_svg_test$gene_results,
results_individual_svg)
head(merge_res,3)
#> gene_id gene_LR gene_logCPM gene_Pvalue gene_FDR
#> 1 ENSG00000115756 1073.3689 15.89723 1.204871e-228 2.855545e-226
#> 2 ENSG00000054690 1056.7744 15.48088 4.686692e-225 5.553730e-223
#> 3 ENSG00000111716 752.4418 16.58993 2.894030e-159 2.286283e-157
#> Layer1_FDR Layer2_FDR Layer3_FDR Layer4_FDR Layer5_FDR
#> 1 8.177323e-07 9.097439e-117 1.298693e-42 2.954756e-16 1.287636e-48
#> 2 2.458083e-15 2.905948e-06 6.466892e-44 8.444596e-07 9.986696e-17
#> 3 4.260477e-47 2.227331e-01 4.739143e-26 8.444596e-07 5.775841e-15
#> Layer6_FDR WM_FDR Layer1_logFC Layer2_logFC Layer3_logFC
#> 1 1.163469e-07 1.087300e-37 0.6095577 -1.6910482 -0.7174741
#> 2 1.960661e-01 1.856053e-212 1.4127189 0.7519805 1.1098266
#> 3 1.750846e-02 3.355203e-89 1.2238140 -0.1102450 -0.3652317
#> Layer4_logFC Layer5_logFC Layer6_logFC WM_logFC
#> 1 1.0572344 1.0578709 0.3909357 -1.067597
#> 2 0.8599344 0.7981348 0.1633057 2.346996
#> 3 -0.3473850 -0.3200502 0.1242379 -1.079196
# 'select = "FDR"' can be used to visualize adjusted p-values for each spatial cluster.
merge_res = top_results(results_svg_test$gene_results,
results_individual_svg, select = "FDR")
head(merge_res,3)
#> gene_id gene_LR gene_logCPM gene_Pvalue gene_FDR
#> 1 ENSG00000115756 1073.3689 15.89723 1.204871e-228 2.855545e-226
#> 2 ENSG00000054690 1056.7744 15.48088 4.686692e-225 5.553730e-223
#> 3 ENSG00000111716 752.4418 16.58993 2.894030e-159 2.286283e-157
#> Layer1_FDR Layer2_FDR Layer3_FDR Layer4_FDR Layer5_FDR
#> 1 8.177323e-07 9.097439e-117 1.298693e-42 2.954756e-16 1.287636e-48
#> 2 2.458083e-15 2.905948e-06 6.466892e-44 8.444596e-07 9.986696e-17
#> 3 4.260477e-47 2.227331e-01 4.739143e-26 8.444596e-07 5.775841e-15
#> Layer6_FDR WM_FDR
#> 1 1.163469e-07 1.087300e-37
#> 2 1.960661e-01 1.856053e-212
#> 3 1.750846e-02 3.355203e-89
# Specify the cluster of interest and check top genes detected by svg_test.
results_WM_both = top_results(cluster_results = results_individual_svg,
cluster = "WM", high_low = "both")
head(results_WM_both$high_genes, 3)
#> Cluster gene_id Cluster_LR Cluster_logCPM Cluster_logFC
#> ENSG00000054690 WM ENSG00000054690 978.6555 15.48088 2.346996
#> ENSG00000166086 WM ENSG00000166086 572.4992 15.47657 1.845724
#> ENSG00000117266 WM ENSG00000117266 376.6276 15.46008 1.588379
#> Cluster_PValue Cluster_FDR
#> ENSG00000054690 7.831449e-215 1.856053e-212
#> ENSG00000166086 1.605722e-126 1.902780e-124
#> ENSG00000117266 6.748199e-84 3.998308e-82
head(results_WM_both$low_genes, 3)
#> Cluster gene_id Cluster_LR Cluster_logCPM Cluster_logFC
#> ENSG00000111716 WM ENSG00000111716 409.7061 16.58993 -1.079196
#> ENSG00000063180 WM ENSG00000063180 258.4652 16.10214 -1.064478
#> ENSG00000135940 WM ENSG00000135940 242.9055 16.88763 -0.693015
#> Cluster_PValue Cluster_FDR
#> ENSG00000111716 4.247092e-91 3.355203e-89
#> ENSG00000063180 3.707311e-58 1.757266e-56
#> ENSG00000135940 9.145322e-55 3.612402e-53