In tnaake/MatrixQCvis: Shiny-based interactive data-quality exploration for omics data
BiocStyle::markdown()
library("knitr")
opts_chunk$set(stop_on_error = 1L)
suppressPackageStartupMessages(library("MatrixQCvis"))
se_r <- params$se_r
se_n <- params$se_n
se_b <- params$se_b
se_t <- params$se_t
se_i <- params$se_i
missingValue <- params$missingValue
This is an automatically-produced report from shinyQC. The analyzed data
contains r nrow(se_r) features and r ncol(se_r) samples.
Tab: Samples
Histogram
Use the settings
- *Categorical variable = *
r params$sample_hist.
tbl <- hist_sample_num(se = se_r, category = params$sample_hist)
hist_sample(tbl, category = params$category_hist)
Mosaic
if (!is.null(params$sample_mosaic_f1)) {
called <- TRUE
sample_mosaic_f1 <- params$sample_mosaic_f1
} else {
called <- FALSE
sample_mosaic_f1 <- colnames(SummarizedExperiment::colData(se_r))[1]
}
if (!is.null(params$sample_mosaic_f2)) {
sample_mosaic_f2 <- params$sample_mosaic_f2
} else {
sample_mosaic_f2 <- colnames(SummarizedExperiment::colData(se_r))[1]
}
r if (!called) {"The mosaic plot was not called within the shiny application. Use default settings."}
Use the settings
- Categorical variable 1 =
r sample_mosaic_f1 and
- Categorical variable 2 =
r sample_mosaic_f2.
mosaic(se = params$se_r, f1 = sample_mosaic_f1, f2 = sample_mosaic_f2)
if (shiny::isRunning())
shiny::setProgress(0.1)
r if (missingValue) {"# Tab: Measured Values\n"}
r if (missingValue) {"## Barplot for samples\n"}
tbl <- samples_memi(se_r)
barplot_samples_memi(tbl, measured = TRUE)
r if (missingValue) {"## Histogram Features\n"}
if (!is.null(params$mev_binwidth)) {
called <- TRUE
binwidth <- params$mev_binwidth
} else {
called <- FALSE
binwidth <- 1
}
r if (missingValue & !called) {"The 'Histogram Features' plot was not called within the shiny application. Use default settings."}
r if (missingValue) {"Use the settings"}
r if (missingValue) {"- *Binwidth =*"} r if (missingValue) paste(binwidth, ".", sep = "")
hist_feature(SummarizedExperiment::assay(se_r), measured = TRUE,
binwidth = binwidth)
r if (missingValue) {"## Histogram Features along variable\n"}
if (!is.null(params$mev_binwidthC)) {
called <- TRUE
binwidth <- params$mev_binwidthC
mev_hist_category <- params$mev_hist_category
} else {
called <- FALSE
binwidth <- 1
mev_hist_category <- colnames(SummarizedExperiment::colData(se_r))[1]
}
r if (missingValue & !called) {"The 'Histogram Features along variable' plot was not called within the shiny application. Use default settings."}
r if (missingValue) {"Use the settings"}
r if (missingValue) {paste("- *Variable for stratification =*", mev_hist_category, "and")}
r if (missingValue) {paste("- *Binwidth =* ", binwidth, ".", sep = "")}
hist_feature_category(se_r, measured = TRUE, category = mev_hist_category,
binwidth = binwidth)
r if (missingValue) {"## Upset\n"}
if (!is.null(params$mev_upset_category)) {
called <- TRUE
mev_upset_category <- params$mev_upset_category
} else {
called <- FALSE
mev_upset_category <- colnames(SummarizedExperiment::colData(se_r))[1]
}
r if (missingValue & !called) {"The 'Upset' plot was not called within the shiny application. Use default settings."}
r if (missingValue) {"Use the settings"}
r if (missingValue) {paste("- *Variable for stratification =* ", mev_upset_category, ".", sep = "")}
upset_category(se_r, category = mev_upset_category, measured = TRUE)
if (shiny::isRunning())
shiny::setProgress(0.2)
r if (missingValue) {"# Tab: Missing Values\n"}
r if (missingValue) {"## Barplot for samples\n"}
tbl <- samples_memi(se_r)
barplot_samples_memi(tbl, measured = FALSE)
r if (missingValue) {"## Histogram Features\n"}
if (!is.null(params$miv_binwidth)) {
called <- TRUE
binwidth <- params$miv_binwidth
} else {
called <- FALSE
binwidth <- 1
}
r if (missingValue & !called) {"The 'Histogram Features' plot was not called within the shiny application. Use default settings."}
r if (missingValue) {"Use the settings"}
r if (missingValue) {paste("- *Binwidth =* ", binwidth, ".", sep = "")}
hist_feature(SummarizedExperiment::assay(se_r), measured = FALSE,
binwidth = binwidth)
r if (missingValue) {"## Histogram Features along variable\n"}
if (!is.null(params$miv_binwidthC)) {
called <- TRUE
binwidth <- params$miv_binwidthC
miv_hist_category <- params$miv_hist_category
} else {
called <- FALSE
binwidth <- 1
miv_hist_category <- colnames(SummarizedExperiment::colData(se_r))[1]
}
r if (missingValue & !called) {"The 'Histogram Features along variable' plot was not called within the shiny application. Use default settings."}
r if (missingValue) {"Use the settings"}
r if (missingValue) {paste("- *Variable for stratification =*", miv_hist_category, "and")}
r if (missingValue) {paste("- *Binwidth =* ", binwidth, ".", sep = " ")}
hist_feature_category(se_r, measured = FALSE, category = miv_hist_category,
binwidth = binwidth)
r if (missingValue) {"## Upset\n"}
if (!is.null(params$miv_upset_category)) {
called <- TRUE
miv_upset_category <- params$miv_upset_category
} else {
called <- FALSE
miv_upset_category <- colnames(SummarizedExperiment::colData(se_r))[1]
}
r if (missingValue & !called) {"The 'Upset' plot was not called within the shiny application. Use default settings."}
r if (missingValue) {"Use the settings"}
r if (missingValue) {paste("- *Variable for stratification =* ", mev_upset_category, ".", sep = " ")}
upset_category(se_r, category = miv_upset_category, measured = FALSE)
if (shiny::isRunning())
shiny::setProgress(0.25)
Tab: Values
Boxplot/Violin plot
if (!is.null(params$int_violin_orderCategory)) {
called <- TRUE
int_box_type <- int_violin <- params$int_violin
int_violin_orderCategory <- params$int_violin_orderCategory
int_log <- params$int_log
int_violin <- if (int_violin == "violin") TRUE else FALSE
int_log <- if (int_log == "log2") TRUE else FALSE
} else {
called <- FALSE
int_violin <- TRUE
int_violin_orderCategory <- colnames(SummarizedExperiment::colData(se_r))[1]
int_log <- FALSE
int_box_type <- "violin"
}
r if (!called) {"The 'Boxplot/Violin plot' was not called within the shiny application. Use default settings."}
Use the settings
- *Display log2 values? = *
r int_log,
- *Type of display = *
r int_box_type, and
- Select variable to order samples =
r int_violin_orderCategory.
createBoxplot(se_r, orderCategory = int_violin_orderCategory,
title = "raw", log2 = int_log, violin = int_violin)
createBoxplot(se_n, orderCategory = int_violin_orderCategory,
title = "normalized", log2 = int_log, violin = int_violin)
createBoxplot(se_b, orderCategory = int_violin_orderCategory,
title = "batch corrected", log2 = FALSE, violin = int_violin)
createBoxplot(se_t, orderCategory = int_violin_orderCategory,
title = "transformed", log2 = FALSE, violin = int_violin)
createBoxplot(se_i, orderCategory = int_violin_orderCategory,
title = "imputed", log2 = FALSE, violin = int_violin)
if (shiny::isRunning())
shiny::setProgress(0.32)
Trend/drift
if (!is.null(params$int_drift_data)) {
called <- TRUE
int_drift_data <- params$int_drift_data
int_drift_aggregation <- params$int_drift_aggregation
int_drift_category <- params$int_drift_category
int_drift_orderCategory <- params$int_drift_orderCategory
int_drift_level <- params$int_drift_level
int_drift_method <- params$int_drift_method
if (int_drift_data == "raw") {
se_tmp <- se_r
}
if (int_drift_data == "normalized") {
se_tmp <- se_n
}
if (int_drift_data == "batch corrected") {
se_tmp <- se_b
}
if (int_drift_data == "transformed") {
se_tmp <- se_t
}
if (int_drift_data == "imputed") {
se_tmp <- se_i
}
} else {
called <- FALSE
int_drift_data <- "raw"
se_tmp <- se_r
int_drift_aggregation <- "sum"
int_drift_category <- colnames(SummarizedExperiment::colData(se_r))[1]
int_drift_orderCategory <- colnames(SummarizedExperiment::colData(se_r))[1]
int_drift_level <- "all"
int_drift_method <- "loess"
}
r if (!called) {"The 'Trend/drift' plot was not called within the shiny application. Use default settings."}
Use the settings
- *Select variable = *
r int_drift_category,
- *Select level to highlight = *
r int_drift_level,
- *Select data input = *
r int_drift_data,
- *Select aggregation = *
r int_drift_aggregation,
- *Select smoothing method = *
r int_drift_method, and
- *Select variable to order samples = *
r int_drift_orderCategory.
driftPlot(se_tmp, aggregation = int_drift_aggregation,
category = int_drift_category,
orderCategory = int_drift_orderCategory,
level = int_drift_level,
method = params$int_drift_method)
Coefficient of variation
a_r <- SummarizedExperiment::assay(se_r)
a_n <- SummarizedExperiment::assay(se_n)
a_b <- SummarizedExperiment::assay(se_b)
a_t <- SummarizedExperiment::assay(se_t)
a_i <- SummarizedExperiment::assay(se_i)
## calculate cv values
cv_r <- cv(a_r, "raw")
cv_n <- cv(a_n, "normalized")
cv_b <- cv(a_b, "batch corrected")
cv_t <- cv(a_t, "transformed")
cv_i <- cv(a_i, "imputed")
df <- data.frame(cv_r, cv_n, cv_b, cv_t, cv_i)
plotCV(df)
Mean-sd plot
vsn::meanSdPlot(a_t)
vsn::meanSdPlot(a_i)
if (shiny::isRunning())
shiny::setProgress(0.4)
MA plot
if (!is.null(params$int_ma_group)) {
called <- TRUE
int_ma_group <- params$int_ma_group
int_ma_data <- params$int_ma_data
int_ma_plot <- params$int_ma_plot
int_hD_lines <- params$int_hD_lines
} else {
called <- FALSE
int_ma_group <- "all"
int_ma_data <- "raw"
int_ma_plot <- "all"
int_hD_lines <- FALSE
}
r if (!called) {"The 'MA plot' was not called within the shiny application. Use default settings."}
Use the settings
- *group = *
r int_ma_group,
- *plot = *
r int_ma_plot,
- *Data set for the MA plot = *
r int_ma_data, and
- *lines = *
r int_hD_lines.
tbl_r <- MAvalues(se_r, log2 = TRUE, group = int_ma_group)
tbl_n <- MAvalues(se_n, log2 = TRUE, group = int_ma_group)
tbl_b <- MAvalues(se_b, log2 = TRUE, group = int_ma_group)
tbl_t <- MAvalues(se_t, log2 = FALSE, group = int_ma_group)
if (shiny::isRunning())
shiny::setProgress(0.5)
tbl_i <- MAvalues(se_i, log2 = FALSE, group = int_ma_group)
if (int_ma_data == "raw") {
tbl <- tbl_r
}
if (int_ma_data == "normalized") {
tbl <- tbl_n
}
if (int_ma_data == "batch corrected") {
tbl <- tbl_b
}
if (int_ma_data == "transformed") {
tbl <- tbl_t
}
if (int_ma_data == "imputed") {
tbl <- tbl_i
}
MAplot(tbl, group = int_ma_group,
plot = int_ma_plot)
d_r <- hoeffDValues(tbl_r, name = "raw")
d_n <- hoeffDValues(tbl_n, name = "normalized")
d_b <- hoeffDValues(tbl_b, name = "batch corrected")
d_t <- hoeffDValues(tbl_t, name = "transformed")
if (missingValue) {
d_i <- hoeffDValues(tbl_i, name = "imputed")
df <- data.frame(d_r, d_n, d_b, d_t, d_i)
} else {
df <- data.frame(d_r, d_n, d_b, d_t)
}
hoeffDPlot(df, lines = int_hD_lines)
if (shiny::isRunning())
shiny::setProgress(0.55)
ECDF
if (!is.null(params$int_ecdf_sample)) {
called <- TRUE
int_ecdf_data <- params$int_ecdf_data
int_ecdf_sample <- params$int_ecdf_sample
int_ecdf_group <- params$int_ecdf_group
if (int_ecdf_data == "raw") {
se_tmp <- se_r
}
if (int_ecdf_data == "normalized") {
se_tmp <- se_n
}
if (int_ecdf_data == "batch corrected") {
se_tmp <- se_b
}
if (int_ecdf_data == "transformed") {
se_tmp <- se_t
}
if (int_ecdf_data == "imputed") {
se_tmp <- se_i
}
} else {
called <- FALSE
int_ecdf_data <- "raw"
int_ecdf_sample <- colnames(se_r)[1]
int_ecdf_group <- "all"
se_tmp <- se_r
}
r if (!called) {"The 'ECDF' plot was not called within the shiny application. Use default settings."}
Use the settings
- *Data set for the ECDF plot = *
r int_ecdf_data,
- *Sample = *
r int_ecdf_sample, and
- *group = *
r int_ecdf_group.
ECDF(se_tmp, sample = int_ecdf_sample,
group = int_ecdf_group)
Distance matrix
if (!is.null(params$int_ecdf_label)) {
called <- TRUE
int_dist_label <- params$int_dist_label
int_dist_method <- params$int_dist_method
} else {
called <- FALSE
int_dist_label <- colnames(SummarizedExperiment::colData(se_r))[1]
int_dist_method <- "euclidean"
}
r if (!called) {"The 'Distance matrix' plots were not called within the shiny application. Use default settings."}
Use the settings
- *annotation = *
r int_dist_label and
- *distance method = *
r int_dist_method.
dist_mat_r <- distShiny(a_r, method = int_dist_method)
dist_mat_n <- distShiny(a_n, method = int_dist_method)
dist_mat_b <- distShiny(a_b, method = int_dist_method)
dist_mat_t <- distShiny(a_t, method = int_dist_method)
## plotting
distSample(d = dist_mat_r, se = se_r,
label = int_dist_label, title = "raw")
sumDistSample(dist_mat_r, title = "raw")
distSample(d = dist_mat_n, se = se_n,
label = int_dist_label, title = "normalized")
sumDistSample(dist_mat_n, title = "normalized")
distSample(d = dist_mat_b, se = se_b,
label = int_dist_label, title = "batch corrected")
sumDistSample(dist_mat_b, title = "batch corrected")
distSample(d = dist_mat_t, se = se_t,
label = int_dist_label, title = "transformed")
sumDistSample(dist_mat_t, title = "transformed")
if (missingValue) {
dist_mat_i <- distShiny(a_i, method = int_dist_method)
distSample(d = dist_mat_i, se = se_i,
label = int_dist_label, title = "imputed")
sumDistSample(dist_mat_i, title = "imputed")
}
Features
if (!is.null(params$int_feat_selectFeat)) {
called <- TRUE
int_feat_selectFeat <- params$int_feat_selectFeat
int_feat_featLine <- params$int_feat_featLine
} else {
called <- FALSE
int_feat_selectFeat <- rownames(se_r)[1]
int_feat_featLine <- FALSE
}
r if (!called) {"The 'Features' plots were not called within the shiny application. Use default settings."}
Use the settings
- *Select feature = *
r int_feat_selectFeat and
- *lines = *
r int_feat_featLine.
if (missingValue) {
l_assays <- list(raw = a_r, normalized = a_n, batch.corrected = a_b,
transformed = a_t, imputed = a_i)
} else {
l_assays <- list(raw = a_r, normalized = a_n, batch.corrected = a_b,
transformed = a_t)
}
df_feature <- createDfFeature(l_assays, feature = int_feat_selectFeat)
## plotting
featurePlot(df_feature)
cvFeaturePlot(l_assays, lines = int_feat_featLine)
if (shiny::isRunning())
shiny::setProgress(0.6)
Tab: Dimension Reduction
PCA
if (!is.null(params$dr_pca_color)) {
called <- TRUE
dr_pca_center <- params$dr_pca_center
dr_pca_scale <- params$dr_pca_scale
dr_pca_color <- params$dr_pca_color
dr_pca_x <- params$dr_pca_x
dr_pca_y <- params$dr_pca_y
} else {
called <- FALSE
dr_pca_center <- TRUE
dr_pca_scale <- TRUE
dr_pca_color <- "none"
dr_pca_x <- "PC1"
dr_pca_y <- "PC2"
}
r if (!called) {"The 'PCA' plot was not called within the shiny application. Use default settings."}
Use the settings
- *x-axis = *
r dr_pca_x,
- *y-axis = *
r dr_pca_y,
- *scale = *
r dr_pca_scale,
- *center = *
r dr_pca_center, and
- *Color = *
r dr_pca_color.
tbl <- dimensionReduction(a_i, type = "PCA",
params = list(center = dr_pca_center, scale = dr_pca_scale))
explainedVar <- explVar(x = a_i,
params = list(center = dr_pca_center, scale = dr_pca_scale))
dimensionReductionPlot(tbl = tbl, se = se_r, color = dr_pca_color,
explainedVar = explainedVar, x_coord = dr_pca_x, y_coord = dr_pca_y)
PCoA
if (!is.null(params$dr_pcoa_color)) {
called <- TRUE
dr_pcoa_method <- params$dr_pcoa_method
dr_pcoa_color <- params$dr_pcoa_color
dr_pcoa_x <- params$dr_pcoa_x
dr_pcoa_y <- params$dr_pcoa_y
} else {
called <- FALSE
dr_pcoa_method <- "euclidean"
dr_pcoa_color <- "none"
dr_pcoa_x <- "Axis.1"
dr_pcoa_y <- "Axis.2"
}
r if (!called) {"The 'PCoA' plot was not called within the shiny application. Use default settings."}
Use the settings
- *x-axis = *
r dr_pcoa_x,
- *y-axis = *
r dr_pcoa_y,
- *Distance measure = *
r dr_pcoa_method, and
- *Color = *
r dr_pcoa_color.
tbl <- dimensionReduction(a_i, type = "PCoA",
params = list(method = dr_pcoa_method))
dimensionReductionPlot(tbl = tbl, se = se_r, color = dr_pcoa_color,
explainedVar = NULL, x_coord = dr_pcoa_x, y_coord = dr_pcoa_y)
if (shiny::isRunning())
shiny::setProgress(0.65)
NMDS
if (!is.null(params$dr_nmds_color)) {
called <- TRUE
dr_nmds_method <- params$dr_pcoa_method
dr_nmds_color <- params$dr_nmds_color
dr_nmds_x <- params$dr_nmds_x
dr_nmds_y <- params$dr_nmds_y
} else {
called <- FALSE
dr_nmds_method <- "euclidean"
dr_nmds_color <- "none"
dr_nmds_x <- "MDS1"
dr_nmds_y <- "MDS2"
}
r if (!called) {"The 'NMDS' plot was not called within the shiny application. Use default settings."}
Use the settings
- *x-axis = *
r dr_nmds_x,
- *y-axis = *
r dr_nmds_y,
- *Distance measure = *
r dr_nmds_method, and
- *Color = *
r dr_nmds_color.
tbl <- dimensionReduction(a_i, type = "NMDS",
params = list(method = dr_nmds_method))
dimensionReductionPlot(tbl = tbl, se = se_r, color = dr_nmds_color,
explainedVar = NULL, x_coord = dr_nmds_x, y_coord = dr_nmds_y)
tSNE
if (!is.null(params$dr_tsne_color)) {
called <- TRUE
dr_tsne_perplexity <- params$dr_tsne_perplexity
dr_tsne_max_iter <- params$dr_tsne_max_iter
dr_tsne_initial_dims <- params$dr_tsne_initial_dims
dr_tsne_dims <- params$dr_tsne_dims
dr_tsne_pca_center <- params$dr_tsne_pca_center
dr_tsne_pca_scale <- params$dr_tsne_pca_scale
dr_tsne_color <- params$dr_tsne_color
dr_tsne_x <- params$dr_tsne_x
dr_tsne_y <- params$dr_tsne_y
} else {
called <- FALSE
dr_tsne_perplexity <- 1
dr_tsne_max_iter <- 1000
dr_tsne_initial_dims <- 10
dr_tsne_dims <- 3
dr_tsne_pca_center <- TRUE
dr_tsne_pca_scale <- TRUE
dr_tsne_color <- "none"
dr_tsne_x <- "X1"
dr_tsne_y <- "X2"
}
if (shiny::isRunning())
shiny::setProgress(0.73)
r if (!called) {"The 'tSNE' plot was not called within the shiny application. Use default settings."}
Use the settings
- *x-axis = *
r dr_tsne_x,
- *y-axis = *
r dr_tsne_y,
- *Perplexity = *
r dr_tsne_perplexity,
- *Number of iterations = *
r dr_tsne_max_iter,
- *Number of retained dimensions in intial PCA = *
r dr_tsne_initial_dims,
- *Output dimensionality = *
r dr_tsne_dims, scale = r dr_tsne_pca_scale,
- *center = *
r dr_tsne_pca_center, and
- *Color = *
r dr_tsne_color.
tbl <- dimensionReduction(a_i, type = "tSNE",
params = list(perplexity = dr_tsne_perplexity,
max_iter = dr_tsne_max_iter, initial_dims = dr_tsne_initial_dims,
dims = dr_tsne_dims, pca_center = dr_tsne_pca_center,
pca_scale = dr_tsne_pca_scale))
dimensionReductionPlot(tbl = tbl, se = se_r, color = dr_tsne_color,
explainedVar = NULL, x_coord = dr_tsne_x, y_coord = dr_tsne_y)
UMAP
if (!is.null(params$dr_umap_color)) {
called <- TRUE
dr_umap_min_dist = params$dr_umap_min_dist
dr_umap_n_neighbors = params$dr_umap_n_neighbors
dr_umap_spread = params$dr_umap_spread
dr_umap_color = params$dr_umap_color
dr_umap_x = params$dr_umap_x
dr_umap_y = params$dr_umap_y
} else {
called <- FALSE
dr_umap_min_dist = 0.1
dr_umap_n_neighbors = min(15, ncol(se_r))
dr_umap_spread = 1
dr_umap_color = "none"
dr_umap_x = "X1"
dr_umap_y = "X2"
}
r if (!called) {"The 'UMAP' plot was not called within the shiny application. Use default settings."}
Use the settings
- *x-axis = *
r dr_umap_x,
- *y-axis = *
r dr_umap_y,
- Minimum distance =
r dr_umap_min_dist,
- Number of neighbors =
r dr_umap_n_neighbors,
- *Spread = *
r dr_umap_spread, and
- *Color = *
r dr_umap_color.
tbl <- dimensionReduction(a_i, type = "UMAP",
params = list(min_dist = dr_umap_min_dist, n_neighbors = dr_umap_n_neighbors,
spread = dr_umap_spread))
dimensionReductionPlot(tbl = tbl, se = se_r, color = dr_umap_color,
explainedVar = NULL, x_coord = dr_umap_x, y_coord = dr_umap_y)
if (shiny::isRunning())
shiny::setProgress(0.87)
m_formula <- params$de_m_formula
valid_m <- if (!is.null(m_formula)) TRUE else FALSE
c_formula <- params$de_c_formula
valid_c <- if (!is.null(c_formula)) TRUE else FALSE
r if (valid_m | (valid_m & valid_c)) {"# Tab: DE\n~\n"}
r if (valid_m) {"The differential expression analysis was called with the model matrix formula:"}
r if (valid_m) {c_formula}
r if (valid_m & valid_c) {"The differential expression analysis was called with the contrast expression:"}
r if (valid_m) {c_formula}
if (valid_m & valid_c) {
mM <- stats::model.matrix(m_formula, data = colData(se_r))
cM <- limma::makeContrasts(contrasts = c_formula, levels = mM)
if (params$de_method == "limma") {
tT <- limma::topTable(params$de_fit_ttest, number = Inf,
adjust.method = "fdr", p.value = 0.05)
tT <- cbind(name = rownames(tT), tT)
volcanoPlot(tT, type = "ttest")
}
if (params$de_method == "proDA") {
tT <- proDA::test_diff(fit = params$de_fit_proDA,
contrast = params$de_c_formula,
sort_by = "adj_pval")
volcanoPlot(tT, type = "proDA")
}
}
if (shiny::isRunning())
shiny::setProgress(0.98)
tnaake/MatrixQCvis documentation built on June 20, 2024, 7:22 a.m.
R Package Documentation
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BiocStyle::markdown()
library("knitr") opts_chunk$set(stop_on_error = 1L) suppressPackageStartupMessages(library("MatrixQCvis"))
se_r <- params$se_r se_n <- params$se_n se_b <- params$se_b se_t <- params$se_t se_i <- params$se_i missingValue <- params$missingValue
This is an automatically-produced report from shinyQC. The analyzed data
contains r nrow(se_r) features and r ncol(se_r) samples.
Tab: Samples
Histogram
Use the settings
- *Categorical variable = *
r params$sample_hist.
tbl <- hist_sample_num(se = se_r, category = params$sample_hist) hist_sample(tbl, category = params$category_hist)
Mosaic
if (!is.null(params$sample_mosaic_f1)) { called <- TRUE sample_mosaic_f1 <- params$sample_mosaic_f1 } else { called <- FALSE sample_mosaic_f1 <- colnames(SummarizedExperiment::colData(se_r))[1] } if (!is.null(params$sample_mosaic_f2)) { sample_mosaic_f2 <- params$sample_mosaic_f2 } else { sample_mosaic_f2 <- colnames(SummarizedExperiment::colData(se_r))[1] }
r if (!called) {"The mosaic plot was not called within the shiny application. Use default settings."}
Use the settings
- Categorical variable 1 =
r sample_mosaic_f1and - Categorical variable 2 =
r sample_mosaic_f2.
mosaic(se = params$se_r, f1 = sample_mosaic_f1, f2 = sample_mosaic_f2)
if (shiny::isRunning()) shiny::setProgress(0.1)
r if (missingValue) {"# Tab: Measured Values\n"}
r if (missingValue) {"## Barplot for samples\n"}
tbl <- samples_memi(se_r) barplot_samples_memi(tbl, measured = TRUE)
r if (missingValue) {"## Histogram Features\n"}
if (!is.null(params$mev_binwidth)) { called <- TRUE binwidth <- params$mev_binwidth } else { called <- FALSE binwidth <- 1 }
r if (missingValue & !called) {"The 'Histogram Features' plot was not called within the shiny application. Use default settings."}
r if (missingValue) {"Use the settings"}
r if (missingValue) {"- *Binwidth =*"} r if (missingValue) paste(binwidth, ".", sep = "")
hist_feature(SummarizedExperiment::assay(se_r), measured = TRUE, binwidth = binwidth)
r if (missingValue) {"## Histogram Features along variable\n"}
if (!is.null(params$mev_binwidthC)) { called <- TRUE binwidth <- params$mev_binwidthC mev_hist_category <- params$mev_hist_category } else { called <- FALSE binwidth <- 1 mev_hist_category <- colnames(SummarizedExperiment::colData(se_r))[1] }
r if (missingValue & !called) {"The 'Histogram Features along variable' plot was not called within the shiny application. Use default settings."}
r if (missingValue) {"Use the settings"}
r if (missingValue) {paste("- *Variable for stratification =*", mev_hist_category, "and")}
r if (missingValue) {paste("- *Binwidth =* ", binwidth, ".", sep = "")}
hist_feature_category(se_r, measured = TRUE, category = mev_hist_category, binwidth = binwidth)
r if (missingValue) {"## Upset\n"}
if (!is.null(params$mev_upset_category)) { called <- TRUE mev_upset_category <- params$mev_upset_category } else { called <- FALSE mev_upset_category <- colnames(SummarizedExperiment::colData(se_r))[1] }
r if (missingValue & !called) {"The 'Upset' plot was not called within the shiny application. Use default settings."}
r if (missingValue) {"Use the settings"}
r if (missingValue) {paste("- *Variable for stratification =* ", mev_upset_category, ".", sep = "")}
upset_category(se_r, category = mev_upset_category, measured = TRUE)
if (shiny::isRunning()) shiny::setProgress(0.2)
r if (missingValue) {"# Tab: Missing Values\n"}
r if (missingValue) {"## Barplot for samples\n"}
tbl <- samples_memi(se_r) barplot_samples_memi(tbl, measured = FALSE)
r if (missingValue) {"## Histogram Features\n"}
if (!is.null(params$miv_binwidth)) { called <- TRUE binwidth <- params$miv_binwidth } else { called <- FALSE binwidth <- 1 }
r if (missingValue & !called) {"The 'Histogram Features' plot was not called within the shiny application. Use default settings."}
r if (missingValue) {"Use the settings"}
r if (missingValue) {paste("- *Binwidth =* ", binwidth, ".", sep = "")}
hist_feature(SummarizedExperiment::assay(se_r), measured = FALSE, binwidth = binwidth)
r if (missingValue) {"## Histogram Features along variable\n"}
if (!is.null(params$miv_binwidthC)) { called <- TRUE binwidth <- params$miv_binwidthC miv_hist_category <- params$miv_hist_category } else { called <- FALSE binwidth <- 1 miv_hist_category <- colnames(SummarizedExperiment::colData(se_r))[1] }
r if (missingValue & !called) {"The 'Histogram Features along variable' plot was not called within the shiny application. Use default settings."}
r if (missingValue) {"Use the settings"}
r if (missingValue) {paste("- *Variable for stratification =*", miv_hist_category, "and")}
r if (missingValue) {paste("- *Binwidth =* ", binwidth, ".", sep = " ")}
hist_feature_category(se_r, measured = FALSE, category = miv_hist_category, binwidth = binwidth)
r if (missingValue) {"## Upset\n"}
if (!is.null(params$miv_upset_category)) { called <- TRUE miv_upset_category <- params$miv_upset_category } else { called <- FALSE miv_upset_category <- colnames(SummarizedExperiment::colData(se_r))[1] }
r if (missingValue & !called) {"The 'Upset' plot was not called within the shiny application. Use default settings."}
r if (missingValue) {"Use the settings"}
r if (missingValue) {paste("- *Variable for stratification =* ", mev_upset_category, ".", sep = " ")}
upset_category(se_r, category = miv_upset_category, measured = FALSE)
if (shiny::isRunning()) shiny::setProgress(0.25)
Tab: Values
Boxplot/Violin plot
if (!is.null(params$int_violin_orderCategory)) { called <- TRUE int_box_type <- int_violin <- params$int_violin int_violin_orderCategory <- params$int_violin_orderCategory int_log <- params$int_log int_violin <- if (int_violin == "violin") TRUE else FALSE int_log <- if (int_log == "log2") TRUE else FALSE } else { called <- FALSE int_violin <- TRUE int_violin_orderCategory <- colnames(SummarizedExperiment::colData(se_r))[1] int_log <- FALSE int_box_type <- "violin" }
r if (!called) {"The 'Boxplot/Violin plot' was not called within the shiny application. Use default settings."}
Use the settings
- *Display log2 values? = *
r int_log, - *Type of display = *
r int_box_type, and - Select variable to order samples =
r int_violin_orderCategory.
createBoxplot(se_r, orderCategory = int_violin_orderCategory, title = "raw", log2 = int_log, violin = int_violin) createBoxplot(se_n, orderCategory = int_violin_orderCategory, title = "normalized", log2 = int_log, violin = int_violin) createBoxplot(se_b, orderCategory = int_violin_orderCategory, title = "batch corrected", log2 = FALSE, violin = int_violin) createBoxplot(se_t, orderCategory = int_violin_orderCategory, title = "transformed", log2 = FALSE, violin = int_violin) createBoxplot(se_i, orderCategory = int_violin_orderCategory, title = "imputed", log2 = FALSE, violin = int_violin)
if (shiny::isRunning()) shiny::setProgress(0.32)
Trend/drift
if (!is.null(params$int_drift_data)) { called <- TRUE int_drift_data <- params$int_drift_data int_drift_aggregation <- params$int_drift_aggregation int_drift_category <- params$int_drift_category int_drift_orderCategory <- params$int_drift_orderCategory int_drift_level <- params$int_drift_level int_drift_method <- params$int_drift_method if (int_drift_data == "raw") { se_tmp <- se_r } if (int_drift_data == "normalized") { se_tmp <- se_n } if (int_drift_data == "batch corrected") { se_tmp <- se_b } if (int_drift_data == "transformed") { se_tmp <- se_t } if (int_drift_data == "imputed") { se_tmp <- se_i } } else { called <- FALSE int_drift_data <- "raw" se_tmp <- se_r int_drift_aggregation <- "sum" int_drift_category <- colnames(SummarizedExperiment::colData(se_r))[1] int_drift_orderCategory <- colnames(SummarizedExperiment::colData(se_r))[1] int_drift_level <- "all" int_drift_method <- "loess" }
r if (!called) {"The 'Trend/drift' plot was not called within the shiny application. Use default settings."}
Use the settings
- *Select variable = *
r int_drift_category, - *Select level to highlight = *
r int_drift_level, - *Select data input = *
r int_drift_data, - *Select aggregation = *
r int_drift_aggregation, - *Select smoothing method = *
r int_drift_method, and - *Select variable to order samples = *
r int_drift_orderCategory.
driftPlot(se_tmp, aggregation = int_drift_aggregation, category = int_drift_category, orderCategory = int_drift_orderCategory, level = int_drift_level, method = params$int_drift_method)
Coefficient of variation
a_r <- SummarizedExperiment::assay(se_r) a_n <- SummarizedExperiment::assay(se_n) a_b <- SummarizedExperiment::assay(se_b) a_t <- SummarizedExperiment::assay(se_t) a_i <- SummarizedExperiment::assay(se_i) ## calculate cv values cv_r <- cv(a_r, "raw") cv_n <- cv(a_n, "normalized") cv_b <- cv(a_b, "batch corrected") cv_t <- cv(a_t, "transformed") cv_i <- cv(a_i, "imputed") df <- data.frame(cv_r, cv_n, cv_b, cv_t, cv_i) plotCV(df)
Mean-sd plot
vsn::meanSdPlot(a_t) vsn::meanSdPlot(a_i)
if (shiny::isRunning()) shiny::setProgress(0.4)
MA plot
if (!is.null(params$int_ma_group)) { called <- TRUE int_ma_group <- params$int_ma_group int_ma_data <- params$int_ma_data int_ma_plot <- params$int_ma_plot int_hD_lines <- params$int_hD_lines } else { called <- FALSE int_ma_group <- "all" int_ma_data <- "raw" int_ma_plot <- "all" int_hD_lines <- FALSE }
r if (!called) {"The 'MA plot' was not called within the shiny application. Use default settings."}
Use the settings
- *group = *
r int_ma_group, - *plot = *
r int_ma_plot, - *Data set for the MA plot = *
r int_ma_data, and - *lines = *
r int_hD_lines.
tbl_r <- MAvalues(se_r, log2 = TRUE, group = int_ma_group) tbl_n <- MAvalues(se_n, log2 = TRUE, group = int_ma_group) tbl_b <- MAvalues(se_b, log2 = TRUE, group = int_ma_group) tbl_t <- MAvalues(se_t, log2 = FALSE, group = int_ma_group) if (shiny::isRunning()) shiny::setProgress(0.5) tbl_i <- MAvalues(se_i, log2 = FALSE, group = int_ma_group) if (int_ma_data == "raw") { tbl <- tbl_r } if (int_ma_data == "normalized") { tbl <- tbl_n } if (int_ma_data == "batch corrected") { tbl <- tbl_b } if (int_ma_data == "transformed") { tbl <- tbl_t } if (int_ma_data == "imputed") { tbl <- tbl_i } MAplot(tbl, group = int_ma_group, plot = int_ma_plot) d_r <- hoeffDValues(tbl_r, name = "raw") d_n <- hoeffDValues(tbl_n, name = "normalized") d_b <- hoeffDValues(tbl_b, name = "batch corrected") d_t <- hoeffDValues(tbl_t, name = "transformed") if (missingValue) { d_i <- hoeffDValues(tbl_i, name = "imputed") df <- data.frame(d_r, d_n, d_b, d_t, d_i) } else { df <- data.frame(d_r, d_n, d_b, d_t) } hoeffDPlot(df, lines = int_hD_lines)
if (shiny::isRunning()) shiny::setProgress(0.55)
ECDF
if (!is.null(params$int_ecdf_sample)) { called <- TRUE int_ecdf_data <- params$int_ecdf_data int_ecdf_sample <- params$int_ecdf_sample int_ecdf_group <- params$int_ecdf_group if (int_ecdf_data == "raw") { se_tmp <- se_r } if (int_ecdf_data == "normalized") { se_tmp <- se_n } if (int_ecdf_data == "batch corrected") { se_tmp <- se_b } if (int_ecdf_data == "transformed") { se_tmp <- se_t } if (int_ecdf_data == "imputed") { se_tmp <- se_i } } else { called <- FALSE int_ecdf_data <- "raw" int_ecdf_sample <- colnames(se_r)[1] int_ecdf_group <- "all" se_tmp <- se_r }
r if (!called) {"The 'ECDF' plot was not called within the shiny application. Use default settings."}
Use the settings
- *Data set for the ECDF plot = *
r int_ecdf_data, - *Sample = *
r int_ecdf_sample, and - *group = *
r int_ecdf_group.
ECDF(se_tmp, sample = int_ecdf_sample, group = int_ecdf_group)
Distance matrix
if (!is.null(params$int_ecdf_label)) { called <- TRUE int_dist_label <- params$int_dist_label int_dist_method <- params$int_dist_method } else { called <- FALSE int_dist_label <- colnames(SummarizedExperiment::colData(se_r))[1] int_dist_method <- "euclidean" }
r if (!called) {"The 'Distance matrix' plots were not called within the shiny application. Use default settings."}
Use the settings
- *annotation = *
r int_dist_labeland - *distance method = *
r int_dist_method.
dist_mat_r <- distShiny(a_r, method = int_dist_method) dist_mat_n <- distShiny(a_n, method = int_dist_method) dist_mat_b <- distShiny(a_b, method = int_dist_method) dist_mat_t <- distShiny(a_t, method = int_dist_method) ## plotting distSample(d = dist_mat_r, se = se_r, label = int_dist_label, title = "raw") sumDistSample(dist_mat_r, title = "raw") distSample(d = dist_mat_n, se = se_n, label = int_dist_label, title = "normalized") sumDistSample(dist_mat_n, title = "normalized") distSample(d = dist_mat_b, se = se_b, label = int_dist_label, title = "batch corrected") sumDistSample(dist_mat_b, title = "batch corrected") distSample(d = dist_mat_t, se = se_t, label = int_dist_label, title = "transformed") sumDistSample(dist_mat_t, title = "transformed") if (missingValue) { dist_mat_i <- distShiny(a_i, method = int_dist_method) distSample(d = dist_mat_i, se = se_i, label = int_dist_label, title = "imputed") sumDistSample(dist_mat_i, title = "imputed") }
Features
if (!is.null(params$int_feat_selectFeat)) { called <- TRUE int_feat_selectFeat <- params$int_feat_selectFeat int_feat_featLine <- params$int_feat_featLine } else { called <- FALSE int_feat_selectFeat <- rownames(se_r)[1] int_feat_featLine <- FALSE }
r if (!called) {"The 'Features' plots were not called within the shiny application. Use default settings."}
Use the settings
- *Select feature = *
r int_feat_selectFeatand - *lines = *
r int_feat_featLine.
if (missingValue) { l_assays <- list(raw = a_r, normalized = a_n, batch.corrected = a_b, transformed = a_t, imputed = a_i) } else { l_assays <- list(raw = a_r, normalized = a_n, batch.corrected = a_b, transformed = a_t) } df_feature <- createDfFeature(l_assays, feature = int_feat_selectFeat) ## plotting featurePlot(df_feature) cvFeaturePlot(l_assays, lines = int_feat_featLine)
if (shiny::isRunning()) shiny::setProgress(0.6)
Tab: Dimension Reduction
PCA
if (!is.null(params$dr_pca_color)) { called <- TRUE dr_pca_center <- params$dr_pca_center dr_pca_scale <- params$dr_pca_scale dr_pca_color <- params$dr_pca_color dr_pca_x <- params$dr_pca_x dr_pca_y <- params$dr_pca_y } else { called <- FALSE dr_pca_center <- TRUE dr_pca_scale <- TRUE dr_pca_color <- "none" dr_pca_x <- "PC1" dr_pca_y <- "PC2" }
r if (!called) {"The 'PCA' plot was not called within the shiny application. Use default settings."}
Use the settings
- *x-axis = *
r dr_pca_x, - *y-axis = *
r dr_pca_y, - *scale = *
r dr_pca_scale, - *center = *
r dr_pca_center, and - *Color = *
r dr_pca_color.
tbl <- dimensionReduction(a_i, type = "PCA", params = list(center = dr_pca_center, scale = dr_pca_scale)) explainedVar <- explVar(x = a_i, params = list(center = dr_pca_center, scale = dr_pca_scale)) dimensionReductionPlot(tbl = tbl, se = se_r, color = dr_pca_color, explainedVar = explainedVar, x_coord = dr_pca_x, y_coord = dr_pca_y)
PCoA
if (!is.null(params$dr_pcoa_color)) { called <- TRUE dr_pcoa_method <- params$dr_pcoa_method dr_pcoa_color <- params$dr_pcoa_color dr_pcoa_x <- params$dr_pcoa_x dr_pcoa_y <- params$dr_pcoa_y } else { called <- FALSE dr_pcoa_method <- "euclidean" dr_pcoa_color <- "none" dr_pcoa_x <- "Axis.1" dr_pcoa_y <- "Axis.2" }
r if (!called) {"The 'PCoA' plot was not called within the shiny application. Use default settings."}
Use the settings
- *x-axis = *
r dr_pcoa_x, - *y-axis = *
r dr_pcoa_y, - *Distance measure = *
r dr_pcoa_method, and - *Color = *
r dr_pcoa_color.
tbl <- dimensionReduction(a_i, type = "PCoA", params = list(method = dr_pcoa_method)) dimensionReductionPlot(tbl = tbl, se = se_r, color = dr_pcoa_color, explainedVar = NULL, x_coord = dr_pcoa_x, y_coord = dr_pcoa_y)
if (shiny::isRunning()) shiny::setProgress(0.65)
NMDS
if (!is.null(params$dr_nmds_color)) { called <- TRUE dr_nmds_method <- params$dr_pcoa_method dr_nmds_color <- params$dr_nmds_color dr_nmds_x <- params$dr_nmds_x dr_nmds_y <- params$dr_nmds_y } else { called <- FALSE dr_nmds_method <- "euclidean" dr_nmds_color <- "none" dr_nmds_x <- "MDS1" dr_nmds_y <- "MDS2" }
r if (!called) {"The 'NMDS' plot was not called within the shiny application. Use default settings."}
Use the settings
- *x-axis = *
r dr_nmds_x, - *y-axis = *
r dr_nmds_y, - *Distance measure = *
r dr_nmds_method, and - *Color = *
r dr_nmds_color.
tbl <- dimensionReduction(a_i, type = "NMDS", params = list(method = dr_nmds_method))
dimensionReductionPlot(tbl = tbl, se = se_r, color = dr_nmds_color, explainedVar = NULL, x_coord = dr_nmds_x, y_coord = dr_nmds_y)
tSNE
if (!is.null(params$dr_tsne_color)) { called <- TRUE dr_tsne_perplexity <- params$dr_tsne_perplexity dr_tsne_max_iter <- params$dr_tsne_max_iter dr_tsne_initial_dims <- params$dr_tsne_initial_dims dr_tsne_dims <- params$dr_tsne_dims dr_tsne_pca_center <- params$dr_tsne_pca_center dr_tsne_pca_scale <- params$dr_tsne_pca_scale dr_tsne_color <- params$dr_tsne_color dr_tsne_x <- params$dr_tsne_x dr_tsne_y <- params$dr_tsne_y } else { called <- FALSE dr_tsne_perplexity <- 1 dr_tsne_max_iter <- 1000 dr_tsne_initial_dims <- 10 dr_tsne_dims <- 3 dr_tsne_pca_center <- TRUE dr_tsne_pca_scale <- TRUE dr_tsne_color <- "none" dr_tsne_x <- "X1" dr_tsne_y <- "X2" }
if (shiny::isRunning()) shiny::setProgress(0.73)
r if (!called) {"The 'tSNE' plot was not called within the shiny application. Use default settings."}
Use the settings
- *x-axis = *
r dr_tsne_x, - *y-axis = *
r dr_tsne_y, - *Perplexity = *
r dr_tsne_perplexity, - *Number of iterations = *
r dr_tsne_max_iter, - *Number of retained dimensions in intial PCA = *
r dr_tsne_initial_dims, - *Output dimensionality = *
r dr_tsne_dims,scale =r dr_tsne_pca_scale, - *center = *
r dr_tsne_pca_center, and - *Color = *
r dr_tsne_color.
tbl <- dimensionReduction(a_i, type = "tSNE", params = list(perplexity = dr_tsne_perplexity, max_iter = dr_tsne_max_iter, initial_dims = dr_tsne_initial_dims, dims = dr_tsne_dims, pca_center = dr_tsne_pca_center, pca_scale = dr_tsne_pca_scale)) dimensionReductionPlot(tbl = tbl, se = se_r, color = dr_tsne_color, explainedVar = NULL, x_coord = dr_tsne_x, y_coord = dr_tsne_y)
UMAP
if (!is.null(params$dr_umap_color)) { called <- TRUE dr_umap_min_dist = params$dr_umap_min_dist dr_umap_n_neighbors = params$dr_umap_n_neighbors dr_umap_spread = params$dr_umap_spread dr_umap_color = params$dr_umap_color dr_umap_x = params$dr_umap_x dr_umap_y = params$dr_umap_y } else { called <- FALSE dr_umap_min_dist = 0.1 dr_umap_n_neighbors = min(15, ncol(se_r)) dr_umap_spread = 1 dr_umap_color = "none" dr_umap_x = "X1" dr_umap_y = "X2" }
r if (!called) {"The 'UMAP' plot was not called within the shiny application. Use default settings."}
Use the settings
- *x-axis = *
r dr_umap_x, - *y-axis = *
r dr_umap_y, - Minimum distance =
r dr_umap_min_dist, - Number of neighbors =
r dr_umap_n_neighbors, - *Spread = *
r dr_umap_spread, and - *Color = *
r dr_umap_color.
tbl <- dimensionReduction(a_i, type = "UMAP", params = list(min_dist = dr_umap_min_dist, n_neighbors = dr_umap_n_neighbors, spread = dr_umap_spread)) dimensionReductionPlot(tbl = tbl, se = se_r, color = dr_umap_color, explainedVar = NULL, x_coord = dr_umap_x, y_coord = dr_umap_y)
if (shiny::isRunning()) shiny::setProgress(0.87)
m_formula <- params$de_m_formula valid_m <- if (!is.null(m_formula)) TRUE else FALSE c_formula <- params$de_c_formula valid_c <- if (!is.null(c_formula)) TRUE else FALSE
r if (valid_m | (valid_m & valid_c)) {"# Tab: DE\n~\n"}
r if (valid_m) {"The differential expression analysis was called with the model matrix formula:"}
r if (valid_m) {c_formula}
r if (valid_m & valid_c) {"The differential expression analysis was called with the contrast expression:"}
r if (valid_m) {c_formula}
if (valid_m & valid_c) { mM <- stats::model.matrix(m_formula, data = colData(se_r)) cM <- limma::makeContrasts(contrasts = c_formula, levels = mM) if (params$de_method == "limma") { tT <- limma::topTable(params$de_fit_ttest, number = Inf, adjust.method = "fdr", p.value = 0.05) tT <- cbind(name = rownames(tT), tT) volcanoPlot(tT, type = "ttest") } if (params$de_method == "proDA") { tT <- proDA::test_diff(fit = params$de_fit_proDA, contrast = params$de_c_formula, sort_by = "adj_pval") volcanoPlot(tT, type = "proDA") } }
if (shiny::isRunning()) shiny::setProgress(0.98)
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