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Quantitative and qualitative meta-analysis results
In amanida: Meta-Analysis for Non-Integral Data
title: "Quantitative and qualitative meta-analysis results"
author: "Amanida R package"
date: "r format(Sys.time(), '%d %B, %Y')"
output:
html_document:
toc: true
toc_float: true
theme: lumen
params:
file_name: input_file
separator: ";"
analysis_type: "quan-qual"
column_id: coln
pvalue_cutoff: 0.05
fc_cutoff: 4
votecount_lim: 1
comp_inf: FALSE
show_code: FALSE
set.seed(123)
knitr::opts_chunk$set(echo = TRUE)
library(knitr)
library(kableExtra, include.only = c('kbl', 'kable_styling', 'scroll_box', 'footnote'))
library(amanida)
Data exploration
Data is loaded using amanida_read function:
coln = c("Compound Name", "P-value", "Fold-change", "N total", "References")
input_file <- system.file("extdata", "dataset2.csv", package = "amanida")
datafile <- amanida_read(input_file, mode = "quan", coln, separator=";")
Your data:
datafile <- amanida_read(params$file_name, mode = "quan", params$column_id, params$separator)
Here is shown the imported table:
data_imp <- datafile |>
mutate(foldchange = round(foldchange,2),
pvalue = format(pvalue, scientific = T, digits = 3),
trend = case_when(trend == -1 ~ "Down-regulated",
T ~ "Up-regulated")) |>
rename("ID" = id,
"P-value" = pvalue,
"Fold-Change" = foldchange,
"Number of participants" = N,
"Reference" = ref,
"Behaviour" = trend)
kbl(data_imp,
booktabs = T,
caption = "Table 1: Data exploration",
align = "c") |>
kable_styling(latex_options = c("basic", "scale_down")) |>
scroll_box(width = "100%", height = "400px")
Quantitative analysis
Quantitative analysis is done for each different identifier. It combines the statistical significance and the effect size reported on the different studies, as follows:
- Statistical significance (p-value): Fisher's method weigthed by number of participants on the study.
- Effect size (fold-change): logarithmic transformation for average with weighting by number of participants.
compute_amanida function is used to combine p-values and fold-change.
amanida_result <- compute_amanida(datafile, comp.inf = params$comp_inf)
Here are shown your results:
amanida_result@stat
kbl(amanida_result@stat |>
arrange(pval) |>
group_by(id) |>
mutate(id = case_when((trend == 1 & fc > params$fc_cutoff & pval < params$pvalue_cutoff)
~ paste(id, "*"),
(trend == -1 & fc < (1/params$fc_cutoff) &
pval < params$pvalue_cutoff) ~ paste(id, "*"),
T ~ id)) |>
ungroup() |>
mutate(pval = format(pval, scientific = T, digits = 3),
fc = round(fc, 2)) |>
rename("ID" = id,
"Trend" = trend,
"P-value combined" = pval,
"Fold-change combined" = fc,
"Total sample size" = N_total),
booktabs = T,
caption = "Table 2: Quantitative analysis",
align = "c") |>
kable_styling(latex_options = c("basic", "scale_down")) |>
scroll_box(width = "100%", height = "400px") |>
footnote(symbol = c("Value over cut-offs"))
Note: * indicates the result is statistical significant
Significant results for quantitative analysis
sig_res <- amanida_result@stat |>
filter(pval < params$pvalue_cutoff,
fc > params$fc_cutoff | fc < 1/params$fc_cutoff)
The cut-off selected by the user for significant results are:
- p-value:
r params$pvalue_cutoff.
- fold-change:
r params$fc_cutoff for up-regulated and r round(1/params$fc_cutoff, 2) for down-regulated.
Using indicated cut-offs there are r nrow(sig_res) compounds significant, r sum(sig_res$trend == 1) up-regulated and r sum(sig_res$trend == -1) down-regulated compounds.
Graphical results
To see graphically the results obtained is used volcano_plot:
With r params$file_name and a p-value cut-off of r params$pvalue_cutoff and fold-change cut-off of r params$fc_cutoff (up-regulated) or r round(1/params$fc_cutoff, 2) (down-regulated):
volcano_plot <- function(mets, cutoff = NULL, names = T) {
articles = NULL; pval = NULL; fc = NULL; lfc = NULL; lpval = NULL; . = NULL;
label = NULL; sig = NULL;
set.seed(123)
col_palette <- amanida_palette()
# Search for cutoff argument
if (hasArg(cutoff)) {
cuts <- cutoff
if (length(cuts) != 2) {
stop( "Please indicate one cut-off for p-value and one for fold-change")
}
cut_pval <- -log10(cuts[1])
cut_fc <- log2(cuts[2])
# If not cutoff argument convention values are established:
} else {
# Alpha < 0.05
cut_pval <- -log10(0.05)
# Log(fold-change) = 1.5
cut_fc <- log2(2.83)
}
message(paste("The cut-off used are "), (10^-cut_pval), " for p-value (", round(cut_pval,2),
" in log10 scale) and ", 2^cut_fc,
" for fold-change (", round(cut_fc,2), " in log2 scale).", sep = "")
# Compounds with 2 or more reports
cont <- as_tibble(mets@vote) |>
mutate(articles = as.numeric(articles)) |>
filter(articles >= 2)
cont_ids <- cont |> pull(id)
# Function for labels
case_character_type <- function(lfc, lpval) {
case_when(
(lfc < -cut_fc & lpval > cut_pval) ~ paste("p-value < ", 10^-cut_pval,
"& fold-change < ", -2^cut_fc),
(lpval > cut_pval & abs(lfc) < cut_fc) ~ paste("p-value < ", 10^-cut_pval),
(lfc > cut_fc & lpval > cut_pval) ~ paste("p-value <", 10^-cut_pval,
"& fold-change >", 2^cut_fc),
T ~ "under cut-offs"
)}
## Volcano plot
# Scatter plot for logarithmic fold-change vs. -logarithmic p-value
dt_p <- as_tibble(mets@stat) |>
mutate(
# Format data needed
across(c(pval,fc), as.numeric),
# Negative logarithm of p-value for plot
lpval = -log10(pval),
# Logarithm of fold-change
lfc = log2(fc)) |>
mutate(sig = case_character_type(lfc, lpval),
label = case_when(
sig == paste("p-value < ", 10^-cut_pval) ~ "",
sig == "under cut-offs" ~ "",
T ~ id),
reports = case_when(
id %in% cont_ids ~ "> 1 report",
T ~ "single report" )) |>
dplyr::group_by('sig')
if ( names == T) {
ggplot(dt_p, aes(lfc, lpval, label = label, colour = sig)) +
geom_point(aes(shape = dt_p$reports), size = 2.5) +
scale_shape_manual(values = c(8, 16), name = "") +
theme_minimal() +
ggrepel::geom_text_repel(size = 4,
fontface = "bold",
segment.size = 0.4,
point.padding = (unit(0.3, "lines")),
box.padding = unit(0.3, "lines"),
colour = "black",
max.overlaps = Inf) +
# Axis titles
xlab( "log2(Fold-change)") +
ylab(expression(paste("-log10(", italic(p), "-value)"))) +
labs(colour = "", tag = "Created with amanida") +
# X axis breaks
scale_x_continuous(breaks = seq(round(min(dt_p$lfc),0) - 1,
round(max(dt_p$lfc),0) + 1, 1),
limits = c(min(dt_p$lfc), max(dt_p$lfc))) +
# Cutoff marks
geom_hline(yintercept = cut_pval,
colour = "black",
linetype = "dashed") +
geom_vline(xintercept = c(cut_fc, -cut_fc),
colour = "black",
linetype = "dashed") +
theme(legend.position = "bottom", plot.title = element_text(hjust = 0.5),
legend.text = element_text(size = 10),
plot.tag = element_text(size = 9, colour = "grey"),
plot.tag.position = "bottomright") +
guides(col = guide_legend(nrow = 2, byrow = T)) +
guides(shape = guide_legend(nrow = 2, byrow = T)) +
scale_color_manual(values = col_palette) +
ggtitle("Volcano plot of adapated meta-analysis results")
} else {
ggplot(dt_p, aes(lfc, lpval, colour = sig)) +
geom_point(aes(shape = dt_p$reports), size = 2.5) +
scale_shape_manual(values = c(8, 16), name = "") +
theme_minimal() +
# Axis titles
xlab( "log2(Fold-change)") +
ylab(expression(paste("-log10(", italic(p), "-value)"))) +
labs(colour = "", tag = "Created with amanida") +
# X axis breaks
scale_x_continuous(breaks = seq(round(min(dt_p$lfc),0) - 1,
round(max(dt_p$lfc),0) + 1, 1),
limits = c(min(dt_p$lfc), max(dt_p$lfc))) +
# Cutoff marks
geom_hline(yintercept = cut_pval, colour = "black", linetype = "dashed") +
geom_vline(xintercept = c(cut_fc, -cut_fc), colour = "black", linetype = "dashed") +
theme(legend.position = "bottom", plot.title = element_text(hjust = 0.5),
legend.text = element_text(size = 10),
plot.tag = element_text(size = 9, colour = "grey"),
plot.tag.position = "bottomright") +
guides(col = guide_legend(nrow = 2, byrow = T)) +
guides(shape = guide_legend(nrow = 2, byrow = T)) +
scale_color_manual(values = col_palette) +
ggtitle("Volcano plot of adapated meta-analysis results")
}
}
volcano_plot(amanida_result, cutoff = c(params$pvalue_cutoff, params$fc_cutoff))
Note: labeled compounds are the ones found significant for the cut-off indicated
Qualitative results
With qualitative analysis can be inspected the behaviour to see if there are discrepancies on the results obtained. This is evaluated comparing the number of articles where an element is found up-regulated and down-regulated. It is also ploted the total results, as vote-counting, obtained by the difference between up-regulated and down-regulated number of reports.
Here, the explore_plot will only display the relevant identifiers (elements bigger than number of votes specified) or those with discrepancies between studies.
As dataset is big, on the following plot will be only displayed elements bigger than r params$votecount_lim votes and the elements with discrepancies between studies:
explore_plot(datafile, type = "mix", params$votecount_lim)
Note: Plots are limited to 30 compounds for readability, for complete plot download from ubidi.shinyapps.io/easy-amanida
It is important to check if our significant compounds have discrepancies on the reports.
To see the complete results for vote-counting use:
amanida_result@vote
kbl(amanida_result@vote |>
arrange(desc(abs(votes))) |>
select("ID" = id, "Votes" = votes, "Articles reporting" = articles),
booktabs = T,
caption = "Table 3: Qualitative analysis",
align = "c") |>
kable_styling(latex_options = c("basic", "scale_down")) |>
scroll_box(width = "100%", height = "400px")
Qualitative results can be also graphically inspected without trend division. Here the vote_plot is set to show the identifiers with a minimum of a vote-counting of r params$votecount_lim
vote_plot <- function(mets, counts = NULL) {
votes = NULL; . = NULL;
set.seed(123)
col_palette <- amanida_palette()
if (hasArg(counts)) {
cuts <- counts
if (length(counts) != 1) {
stop( "Please indicate one cut-off only")
}
} else {
cuts <- 1
}
message("Cut-off for votes is ", cuts, ".", sep = "")
# Subset vote-couting data
tb <- as_tibble(mets@vote) |>
mutate(
votes = as.numeric(votes),
test = case_when(
votes > 0 ~ 0,
T ~ 1)) |>
filter (abs(votes) >= cuts)
if (nrow(tb) < 1) {
stop( "Cut-off value out of limit")
}
if(nrow(tb) > 30) {
message("Too much values, only showing 30 highest values. Please check counts parameter.")
tb <- tb |>
slice_max(abs(votes), n = 30, with_ties = FALSE)
}
max_p <- max(tb$votes)
ggplot(tb, aes(reorder(id, votes), votes, fill = votes)) +
geom_bar(stat = "identity", show.legend = F, width = .5
) +
geom_text(aes(label = reorder(id, votes)), vjust = 0.2, size = 3.5,
hjust = tb$test) +
scale_fill_gradient(low = col_palette[3], high = col_palette[5]) +
theme_light() +
theme(axis.text.y = element_blank(),
axis.text.x = element_text(size = 10),
axis.title = element_text(size = 10),
axis.ticks.y = element_blank(),
plot.title = element_text(size = 12),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
panel.grid.major.x = element_line(linetype = "dashed"),
plot.tag = element_text(size = 9, colour = "grey"),
plot.tag.position = "bottomright") +
labs(tag = "Created with amanida") +
coord_flip() +
ylab("Vote-counting") +
xlab('')+
ggtitle("Total vote count of compounds behaviour") +
scale_y_continuous(expand = c(0.6, 0),
breaks = seq(max_p*-1, max_p, by = 1),
limits = c(max_p*-1,
max_p + 1)
)
}
vote_plot(amanida_result, counts = params$votecount_lim)
Note: Plots are limited to 30 compounds for readability, for complete plot download from ubidi.shinyapps.io/easy-amanida
Qualitative analysis using Amanida R package
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title: "Quantitative and qualitative meta-analysis results"
author: "Amanida R package"
date: "r format(Sys.time(), '%d %B, %Y')"
output:
html_document:
toc: true
toc_float: true
theme: lumen
params:
file_name: input_file
separator: ";"
analysis_type: "quan-qual"
column_id: coln
pvalue_cutoff: 0.05
fc_cutoff: 4
votecount_lim: 1
comp_inf: FALSE
show_code: FALSE
set.seed(123) knitr::opts_chunk$set(echo = TRUE) library(knitr) library(kableExtra, include.only = c('kbl', 'kable_styling', 'scroll_box', 'footnote')) library(amanida)
Data exploration
Data is loaded using amanida_read function:
coln = c("Compound Name", "P-value", "Fold-change", "N total", "References") input_file <- system.file("extdata", "dataset2.csv", package = "amanida") datafile <- amanida_read(input_file, mode = "quan", coln, separator=";")
Your data:
datafile <- amanida_read(params$file_name, mode = "quan", params$column_id, params$separator)
Here is shown the imported table:
data_imp <- datafile |> mutate(foldchange = round(foldchange,2), pvalue = format(pvalue, scientific = T, digits = 3), trend = case_when(trend == -1 ~ "Down-regulated", T ~ "Up-regulated")) |> rename("ID" = id, "P-value" = pvalue, "Fold-Change" = foldchange, "Number of participants" = N, "Reference" = ref, "Behaviour" = trend) kbl(data_imp, booktabs = T, caption = "Table 1: Data exploration", align = "c") |> kable_styling(latex_options = c("basic", "scale_down")) |> scroll_box(width = "100%", height = "400px")
Quantitative analysis
Quantitative analysis is done for each different identifier. It combines the statistical significance and the effect size reported on the different studies, as follows:
- Statistical significance (p-value): Fisher's method weigthed by number of participants on the study.
- Effect size (fold-change): logarithmic transformation for average with weighting by number of participants.
compute_amanida function is used to combine p-values and fold-change.
amanida_result <- compute_amanida(datafile, comp.inf = params$comp_inf)
Here are shown your results:
amanida_result@stat
kbl(amanida_result@stat |> arrange(pval) |> group_by(id) |> mutate(id = case_when((trend == 1 & fc > params$fc_cutoff & pval < params$pvalue_cutoff) ~ paste(id, "*"), (trend == -1 & fc < (1/params$fc_cutoff) & pval < params$pvalue_cutoff) ~ paste(id, "*"), T ~ id)) |> ungroup() |> mutate(pval = format(pval, scientific = T, digits = 3), fc = round(fc, 2)) |> rename("ID" = id, "Trend" = trend, "P-value combined" = pval, "Fold-change combined" = fc, "Total sample size" = N_total), booktabs = T, caption = "Table 2: Quantitative analysis", align = "c") |> kable_styling(latex_options = c("basic", "scale_down")) |> scroll_box(width = "100%", height = "400px") |> footnote(symbol = c("Value over cut-offs"))
Note: * indicates the result is statistical significant
Significant results for quantitative analysis
sig_res <- amanida_result@stat |> filter(pval < params$pvalue_cutoff, fc > params$fc_cutoff | fc < 1/params$fc_cutoff)
The cut-off selected by the user for significant results are:
- p-value:
r params$pvalue_cutoff. - fold-change:
r params$fc_cutofffor up-regulated andr round(1/params$fc_cutoff, 2)for down-regulated.
Using indicated cut-offs there are r nrow(sig_res) compounds significant, r sum(sig_res$trend == 1) up-regulated and r sum(sig_res$trend == -1) down-regulated compounds.
Graphical results
To see graphically the results obtained is used volcano_plot:
With r params$file_name and a p-value cut-off of r params$pvalue_cutoff and fold-change cut-off of r params$fc_cutoff (up-regulated) or r round(1/params$fc_cutoff, 2) (down-regulated):
volcano_plot <- function(mets, cutoff = NULL, names = T) { articles = NULL; pval = NULL; fc = NULL; lfc = NULL; lpval = NULL; . = NULL; label = NULL; sig = NULL; set.seed(123) col_palette <- amanida_palette() # Search for cutoff argument if (hasArg(cutoff)) { cuts <- cutoff if (length(cuts) != 2) { stop( "Please indicate one cut-off for p-value and one for fold-change") } cut_pval <- -log10(cuts[1]) cut_fc <- log2(cuts[2]) # If not cutoff argument convention values are established: } else { # Alpha < 0.05 cut_pval <- -log10(0.05) # Log(fold-change) = 1.5 cut_fc <- log2(2.83) } message(paste("The cut-off used are "), (10^-cut_pval), " for p-value (", round(cut_pval,2), " in log10 scale) and ", 2^cut_fc, " for fold-change (", round(cut_fc,2), " in log2 scale).", sep = "") # Compounds with 2 or more reports cont <- as_tibble(mets@vote) |> mutate(articles = as.numeric(articles)) |> filter(articles >= 2) cont_ids <- cont |> pull(id) # Function for labels case_character_type <- function(lfc, lpval) { case_when( (lfc < -cut_fc & lpval > cut_pval) ~ paste("p-value < ", 10^-cut_pval, "& fold-change < ", -2^cut_fc), (lpval > cut_pval & abs(lfc) < cut_fc) ~ paste("p-value < ", 10^-cut_pval), (lfc > cut_fc & lpval > cut_pval) ~ paste("p-value <", 10^-cut_pval, "& fold-change >", 2^cut_fc), T ~ "under cut-offs" )} ## Volcano plot # Scatter plot for logarithmic fold-change vs. -logarithmic p-value dt_p <- as_tibble(mets@stat) |> mutate( # Format data needed across(c(pval,fc), as.numeric), # Negative logarithm of p-value for plot lpval = -log10(pval), # Logarithm of fold-change lfc = log2(fc)) |> mutate(sig = case_character_type(lfc, lpval), label = case_when( sig == paste("p-value < ", 10^-cut_pval) ~ "", sig == "under cut-offs" ~ "", T ~ id), reports = case_when( id %in% cont_ids ~ "> 1 report", T ~ "single report" )) |> dplyr::group_by('sig') if ( names == T) { ggplot(dt_p, aes(lfc, lpval, label = label, colour = sig)) + geom_point(aes(shape = dt_p$reports), size = 2.5) + scale_shape_manual(values = c(8, 16), name = "") + theme_minimal() + ggrepel::geom_text_repel(size = 4, fontface = "bold", segment.size = 0.4, point.padding = (unit(0.3, "lines")), box.padding = unit(0.3, "lines"), colour = "black", max.overlaps = Inf) + # Axis titles xlab( "log2(Fold-change)") + ylab(expression(paste("-log10(", italic(p), "-value)"))) + labs(colour = "", tag = "Created with amanida") + # X axis breaks scale_x_continuous(breaks = seq(round(min(dt_p$lfc),0) - 1, round(max(dt_p$lfc),0) + 1, 1), limits = c(min(dt_p$lfc), max(dt_p$lfc))) + # Cutoff marks geom_hline(yintercept = cut_pval, colour = "black", linetype = "dashed") + geom_vline(xintercept = c(cut_fc, -cut_fc), colour = "black", linetype = "dashed") + theme(legend.position = "bottom", plot.title = element_text(hjust = 0.5), legend.text = element_text(size = 10), plot.tag = element_text(size = 9, colour = "grey"), plot.tag.position = "bottomright") + guides(col = guide_legend(nrow = 2, byrow = T)) + guides(shape = guide_legend(nrow = 2, byrow = T)) + scale_color_manual(values = col_palette) + ggtitle("Volcano plot of adapated meta-analysis results") } else { ggplot(dt_p, aes(lfc, lpval, colour = sig)) + geom_point(aes(shape = dt_p$reports), size = 2.5) + scale_shape_manual(values = c(8, 16), name = "") + theme_minimal() + # Axis titles xlab( "log2(Fold-change)") + ylab(expression(paste("-log10(", italic(p), "-value)"))) + labs(colour = "", tag = "Created with amanida") + # X axis breaks scale_x_continuous(breaks = seq(round(min(dt_p$lfc),0) - 1, round(max(dt_p$lfc),0) + 1, 1), limits = c(min(dt_p$lfc), max(dt_p$lfc))) + # Cutoff marks geom_hline(yintercept = cut_pval, colour = "black", linetype = "dashed") + geom_vline(xintercept = c(cut_fc, -cut_fc), colour = "black", linetype = "dashed") + theme(legend.position = "bottom", plot.title = element_text(hjust = 0.5), legend.text = element_text(size = 10), plot.tag = element_text(size = 9, colour = "grey"), plot.tag.position = "bottomright") + guides(col = guide_legend(nrow = 2, byrow = T)) + guides(shape = guide_legend(nrow = 2, byrow = T)) + scale_color_manual(values = col_palette) + ggtitle("Volcano plot of adapated meta-analysis results") } }
volcano_plot(amanida_result, cutoff = c(params$pvalue_cutoff, params$fc_cutoff))
Note: labeled compounds are the ones found significant for the cut-off indicated
Qualitative results
With qualitative analysis can be inspected the behaviour to see if there are discrepancies on the results obtained. This is evaluated comparing the number of articles where an element is found up-regulated and down-regulated. It is also ploted the total results, as vote-counting, obtained by the difference between up-regulated and down-regulated number of reports.
Here, the explore_plot will only display the relevant identifiers (elements bigger than number of votes specified) or those with discrepancies between studies.
As dataset is big, on the following plot will be only displayed elements bigger than r params$votecount_lim votes and the elements with discrepancies between studies:
explore_plot(datafile, type = "mix", params$votecount_lim)
Note: Plots are limited to 30 compounds for readability, for complete plot download from ubidi.shinyapps.io/easy-amanida
It is important to check if our significant compounds have discrepancies on the reports.
To see the complete results for vote-counting use:
amanida_result@vote
kbl(amanida_result@vote |> arrange(desc(abs(votes))) |> select("ID" = id, "Votes" = votes, "Articles reporting" = articles), booktabs = T, caption = "Table 3: Qualitative analysis", align = "c") |> kable_styling(latex_options = c("basic", "scale_down")) |> scroll_box(width = "100%", height = "400px")
Qualitative results can be also graphically inspected without trend division. Here the vote_plot is set to show the identifiers with a minimum of a vote-counting of r params$votecount_lim
vote_plot <- function(mets, counts = NULL) { votes = NULL; . = NULL; set.seed(123) col_palette <- amanida_palette() if (hasArg(counts)) { cuts <- counts if (length(counts) != 1) { stop( "Please indicate one cut-off only") } } else { cuts <- 1 } message("Cut-off for votes is ", cuts, ".", sep = "") # Subset vote-couting data tb <- as_tibble(mets@vote) |> mutate( votes = as.numeric(votes), test = case_when( votes > 0 ~ 0, T ~ 1)) |> filter (abs(votes) >= cuts) if (nrow(tb) < 1) { stop( "Cut-off value out of limit") } if(nrow(tb) > 30) { message("Too much values, only showing 30 highest values. Please check counts parameter.") tb <- tb |> slice_max(abs(votes), n = 30, with_ties = FALSE) } max_p <- max(tb$votes) ggplot(tb, aes(reorder(id, votes), votes, fill = votes)) + geom_bar(stat = "identity", show.legend = F, width = .5 ) + geom_text(aes(label = reorder(id, votes)), vjust = 0.2, size = 3.5, hjust = tb$test) + scale_fill_gradient(low = col_palette[3], high = col_palette[5]) + theme_light() + theme(axis.text.y = element_blank(), axis.text.x = element_text(size = 10), axis.title = element_text(size = 10), axis.ticks.y = element_blank(), plot.title = element_text(size = 12), panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(), panel.border = element_blank(), panel.grid.major.x = element_line(linetype = "dashed"), plot.tag = element_text(size = 9, colour = "grey"), plot.tag.position = "bottomright") + labs(tag = "Created with amanida") + coord_flip() + ylab("Vote-counting") + xlab('')+ ggtitle("Total vote count of compounds behaviour") + scale_y_continuous(expand = c(0.6, 0), breaks = seq(max_p*-1, max_p, by = 1), limits = c(max_p*-1, max_p + 1) ) }
vote_plot(amanida_result, counts = params$votecount_lim)
Note: Plots are limited to 30 compounds for readability, for complete plot download from ubidi.shinyapps.io/easy-amanida
Qualitative analysis using Amanida R package
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