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@cafferychen777
cafferychen777 committed Aug 14, 2023
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23 changes: 18 additions & 5 deletions R/generate_taxa_test_long.R
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Original file line number Diff line number Diff line change
Expand Up @@ -19,8 +19,7 @@
#' \dontrun{
#' # Load example data
#' data(peerj32.obj)
#'
#' results <- generate_taxa_test_long(
#' generate_taxa_test_long(
#' data.obj = peerj32.obj,
#' subject.var = "subject",
#' time.var = "time",
Expand All @@ -34,7 +33,7 @@
#' feature.dat.type = "count"
#' )
#' data(ecam.obj)
#' results <- generate_taxa_test_long(
#' generate_taxa_test_long(
#' data.obj = ecam.obj,
#' subject.var = "studyid",
#' time.var = "month",
Expand All @@ -47,7 +46,20 @@
#' abund.filter = 0.001,
#' feature.dat.type = "proportion"
#' )
#'
#' data("subset_T2D.obj")
#' generate_taxa_test_long(
#' data.obj = subset_T2D.obj,
#' subject.var = "subject_id",
#' time.var = "visit_number",
#' t0.level = NULL,
#' ts.levels = NULL,
#' group.var = "subject_gender",
#' adj.vars = c("subject_race"),
#' feature.level = "Phylum",
#' prev.filter = 0.0001,
#' abund.filter = 0.0001,
#' feature.dat.type = "count"
#' )
#' }
#' @export
generate_taxa_test_long <-
Expand Down Expand Up @@ -116,7 +128,8 @@ generate_taxa_test_long <-
otu_tax_agg_numeric <-
otu_tax_agg %>%
dplyr::mutate_at(vars(-!!sym(feature.level)), as.numeric) %>%
dplyr::mutate(!!sym(feature.level) := tidyr::replace_na(!!sym(feature.level), "unclassified")) %>% column_to_rownames(feature.level)
dplyr::mutate(!!sym(feature.level) := tidyr::replace_na(!!sym(feature.level), "unclassified")) %>%
column_to_rownames(feature.level)

linda.obj <- linda(feature.dat = otu_tax_agg_numeric, meta.dat = meta_tab,
formula = paste("~",formula), feature.dat.type = "proportion",
Expand Down
151 changes: 143 additions & 8 deletions R/mStat_generate_report_long.R
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Original file line number Diff line number Diff line change
Expand Up @@ -158,7 +158,7 @@ if (is.null(pc.obj)){
### 2.1 Alpha Diversity Boxplots
```{r alpha-boxplot-generation, message=FALSE, fig.align='center'}
```{r alpha-boxplot-generation, message=FALSE, fig.align='center', fig.width = 20, fig.height = 8}
alpha_boxplot_results <- generate_alpha_boxplot_long(data.obj = data.obj,
alpha.obj = alpha.obj,
alpha.name = alpha.name,
Expand Down Expand Up @@ -273,6 +273,11 @@ alpha_trend_test_results <- generate_alpha_trend_test_long(data.obj = data.obj,
adj.vars = adj.vars)
```
```{r alpha-trend-test-results-print, echo=FALSE, message=FALSE}
cat('## Alpha Diversity Trend Test Results \n')
pander::pander(alpha_trend_test_results)
```
### 2.5 Alpha Diversity Volatility Test Results
```{r alpha-volatility-test-generation, message=FALSE}
Expand All @@ -285,11 +290,16 @@ alpha_volatility_test_results <- generate_alpha_volatility_test_long(data.obj =
adj.vars = adj.vars)
```
```{r alpha-volatility-test-results-print, echo=FALSE, message=FALSE}
cat('## Alpha Diversity Volatility Test Results \n')
pander::pander(alpha_volatility_test_results)
```
## 3. Beta Diversity Analysis
### 3.1 Beta Diversity Ordination
```{r beta-ordination-generation, message=FALSE, fig.align='center', warning = FALSE}
```{r beta-ordination-generation, message=FALSE, fig.align='center', warning = FALSE, fig.width = 18, fig.height = 8}
beta_ordination_results <- generate_beta_ordination_long(data.obj = data.obj,
dist.obj = dist.obj,
pc.obj = pc.obj,
Expand All @@ -313,7 +323,7 @@ beta_ordination_results
### 3.2 Beta Diversity PC Boxplot
```{r pc-boxplot-longitudinal-generation, message=FALSE, fig.align='center'}
```{r pc-boxplot-longitudinal-generation, message=FALSE, fig.align='center', fig.width = 20, fig.height = 8}
pc_boxplot_longitudinal_results <- generate_beta_pc_boxplot_long(
data.obj = data.obj,
dist.obj = dist.obj,
Expand Down Expand Up @@ -365,7 +375,112 @@ spaghettiplot_longitudinal_results <- generate_beta_change_spaghettiplot_long(
spaghettiplot_longitudinal_results
```
### 3.4 Beta Diversity Test Results
```{r beta-test-longitudinal-generation, message=FALSE, fig.align='center'}
beta_test_longitudinal_results <- generate_beta_test_long(data.obj = data.obj,
dist.obj = dist.obj,
time.var = time.var,
t0.level = t0.level,
ts.levels = ts.levels,
subject.var = subject.var,
group.var = group.var,
adj.vars = adj.vars,
dist.name = dist.name)
```
```{r beta-diversity-permanova-analysis, echo=FALSE, message=FALSE, results='asis'}
cat('## P-Tab Results \n')
pander::pander(beta_test_longitudinal_results$p.tab)
for (i in 1:nrow(beta_test_longitudinal_results$p.tab)) {
# 提取变量名称
term <- beta_test_longitudinal_results$p.tab$Term[i]
cat(paste0('\n### Beta Diversity PERMANOVA Analysis for Variable: ', term, '\n'))
# 提取并解释每个距离矩阵的结果
for (j in 1:length(dist.name)) {
# 提取相关统计结果
p_value <- beta_test_longitudinal_results$p.tab[paste0('D', j, '.p.value')][i,]
# 根据P值决定输出的消息
if (p_value < 0.05) {
message <- paste0('The variable ', term, ' has a statistically significant impact on the ',
'beta diversity according to the PERMANOVA test with the ', dist.name[j], ' distance matrix.')
} else {
message <- paste0('The variable ', term, ' does not appear to have a statistically significant effect on the ',
'beta diversity according to the PERMANOVA test with the ', dist.name[j], ' distance matrix.')
}
# 使用strwrap函数将消息断行，并在每行末尾添加两个空格
message_lines <- paste0(strwrap(message, width = 100), ' ')
# 打印分析结果
cat(paste(message_lines, collapse = '\n'), '\n')
}
# 提取omnibus检验的结果
p_value <- beta_test_longitudinal_results$p.tab['omni.p.value'][i,]
# 根据P值决定输出的消息
if (p_value < 0.05) {
message <- paste0('The variable ', term, ' has a statistically significant impact on the ',
'beta diversity according to the omnibus PERMANOVA test.')
} else {
message <- paste0('The variable ', term, ' does not appear to have a statistically significant effect on the ',
'beta diversity according to the omnibus PERMANOVA test.')
}
# 使用strwrap函数将消息断行，并在每行末尾添加两个空格
message_lines <- paste0(strwrap(message, width = 100), ' ')
# 打印分析结果
cat(paste(message_lines, collapse = '\n'), '\n')
}
cat('\n## AOV-Tab Results \n\n')
pander::pander(beta_test_longitudinal_results$aov.tab)
# 遍历aov.tab并生成解析报告
for (variable in unique(beta_test_longitudinal_results$aov.tab$Variable)) {
# 提取当前变量的分析结果
aov_results <- subset(beta_test_longitudinal_results$aov.tab, Variable == variable)
# 打印变量名称的标题
cat(paste0('\n### ', variable, ' Variable Analysis\n'))
# 提取并解释每个距离矩阵的结果
for (i in 1:nrow(aov_results)) {
# 提取距离矩阵名称和相关统计结果
distance <- aov_results$Distance[i]
p_value <- as.numeric(aov_results$P_Value[i])
if (is.na(p_value)){
next
}
# 根据P值决定输出的消息
if (p_value < 0.05) {
message <- paste0('The variable ', variable, ' has a statistically significant impact on the beta diversity ',
'according to the PERMANOVA test with the ', distance, ' distance matrix.\n')
} else {
message <- paste0('The variable ', variable, ' does not appear to have a statistically significant effect on the beta diversity ',
'according to the PERMANOVA test with the ', distance, ' distance matrix.\n')
}
# 使用strwrap函数将消息断行，并在每行末尾添加两个空格
message_lines <- paste0(strwrap(message, width = 100), ' ')
# 打印分析结果
cat(paste(message_lines, collapse = '\n'), '\n')
}
}
```
### 3.5 Beta Diversity Trend Test Results
Expand All @@ -379,6 +494,11 @@ beta_trend_test_longitudinal_results <- generate_beta_trend_test_long(data.obj =
dist.name = dist.name)
```
```{r beta-trend-test-results-print, echo=FALSE, message=FALSE}
cat('## Beta Diversity Trend Test Results \n')
pander::pander(beta_trend_test_longitudinal_results)
```
### 3.6 Beta Diversity PC Trend Test Results
```{r beta-pc-trend-test-longitudinal-generation, message=FALSE, fig.align='center'}
Expand All @@ -392,6 +512,11 @@ beta_pc_trend_test_longitudinal_results <- generate_beta_pc_trend_test_long(data
dist.name = dist.name)
```
```{r beta-pc-trend-test-results-print, echo=FALSE, message=FALSE}
cat('## Beta Diversity PC Trend Test Results \n')
pander::pander(beta_pc_trend_test_longitudinal_results)
```
### 3.7 Beta Diversity Volatility Test Results
```{r beta-volatility-test-longitudinal-generation, message=FALSE, fig.align='center'}
Expand All @@ -404,6 +529,11 @@ beta_volatility_test_longitudinal_results <- generate_beta_volatility_test_long(
dist.name = dist.name)
```
```{r beta-volatility-test-results-print, echo=FALSE, message=FALSE}
cat('## Beta Diversity Volatility Test Results \n')
pander::pander(beta_volatility_test_longitudinal_results)
```
### 3.8 Beta Diversity PC Volatility Test Results
```{r beta-pc-volatility-test-longitudinal-generation, message=FALSE, fig.align='center'}
Expand All @@ -417,11 +547,16 @@ beta_pc_volatility_test_longitudinal_results <- generate_beta_pc_volatility_test
dist.name = dist.name)
```
```{r beta-pc-volatility-test-results-print, echo=FALSE, message=FALSE}
cat('## Beta Diversity Volatility Test Results \n')
pander::pander(beta_pc_volatility_test_longitudinal_results)
```
## 4. Taxonomic Feature Analysis
### 4.1 Taxa Areaplot Longitudinal
```{r taxa-areaplot-longitudinal-generation, message=FALSE, fig.align='center', fig.width = 15, fig.height = 8}
```{r taxa-areaplot-longitudinal-generation, message=FALSE, fig.align='center', fig.width = 20, fig.height = 8}
taxa_areaplot_long_results <- generate_taxa_areaplot_long(
data.obj = data.obj,
subject.var = subject.var,
Expand Down Expand Up @@ -596,7 +731,7 @@ pander::pander(taxa_volatility_test_results)
### 4.8 Taxa Boxplot for Significant Taxa
```{r taxa-test-boxplot-longitudinal-generation, message=FALSE, fig.height=20, fig.width=15, fig.align='center'}
```{r taxa-test-boxplot-longitudinal-generation, message=FALSE, fig.height=15, fig.width=10, fig.align='center'}
taxa_test_results <- do.call('rbind', taxa_test_results)
significant_taxa <- taxa_test_results$Variable[taxa_test_results$Adjusted.P.Value < 0.05]
Expand Down Expand Up @@ -624,7 +759,7 @@ if (!is.null(significant_taxa)){
file.ann = file.ann,
pdf.wid = pdf.wid,
pdf.hei = pdf.hei)
taxa_boxplot_results
print(taxa_boxplot_results)
taxa_indiv_boxplot_results <- generate_taxa_indiv_boxplot_long(data.obj = data.obj,
subject.var = subject.var,
Expand Down Expand Up @@ -694,7 +829,7 @@ cat(paste0('The boxplot results for individual taxa or features can be found in
### 4.9 Taxa Spaghettiplot for Significant Taxa
```{r taxa-spaghettiplot-longitudinal-generation, message=FALSE, fig.height=20, fig.width=15, fig.align='center'}
```{r taxa-spaghettiplot-longitudinal-generation, message=FALSE, fig.height=15, fig.width=10, fig.align='center'}
if (!is.null(significant_taxa)){
taxa_spaghettiplot_results <- generate_taxa_spaghettiplot_long(data.obj = data.obj,
Expand All @@ -719,7 +854,7 @@ if (!is.null(significant_taxa)){
file.ann = file.ann,
pdf.wid = pdf.wid,
pdf.hei = pdf.hei)
taxa_spaghettiplot_results
print(taxa_spaghettiplot_results)
taxa_indiv_spaghettiplot_results <- generate_taxa_indiv_spaghettiplot_long(data.obj = data.obj,
subject.var = subject.var,
Expand Down

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