GITBOOK-27: No subject

cafferychen777 · Aug 6, 2023 · f9f9aca · f9f9aca
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diff --git a/...ign/cross-sectional-reporting-microbial-analysis-reports-with-microbiomestat.md b/...ign/cross-sectional-reporting-microbial-analysis-reports-with-microbiomestat.md
@@ -41,11 +41,13 @@ mStat_generate_report_single(
   feature.dat.type = "count",  
   theme.choice = "bw",
   base.size = 12,
-  output.file = "report.pdf"
+  output.file = "path/report.pdf"
 )
 
 ```
 
+{% file src="../.gitbook/assets/mStat_generate_report_single_example.pdf" %}
+
 The automated report relieves the burden of **manual analysis** and promotes consistency. By **integrating results** from diverse analytical dimensions, it strengthens evidence and tells a cohesive story.
 
 MicrobiomeStat powers researchers with **automated workflows** to swiftly synthesize and report discoveries from cross-sectional studies. The standardized reports integrate multidimensional perspectives for comprehensive insights.
diff --git a/...gn/longitudinal-reporting-microbiome-analysis-automation-with-microbiomestat.md b/...gn/longitudinal-reporting-microbiome-analysis-automation-with-microbiomestat.md
@@ -24,11 +24,28 @@ It then compiles the results into a publication-ready PDF report containing:
 We provide the longitudinal data and analysis parameters:
 
 ```r
-mStat_generate_report_long(
-  data.obj = long_data, 
-  time.var = "week",
-  output.file = "report.pdf"
-) 
+ mStat_generate_report_long(
+  data.obj = subset_T2D.obj,
+  dist.obj = NULL,
+  alpha.obj = NULL,
+  group.var = "sample_body_site",
+  adj_vars = c("subject_race"),
+  subject.var = "subject_id",
+  time.var = "visit_number",
+  alpha.name = c("shannon","simpson"),
+  dist.name = c("BC",'Jaccard'),
+  t0.level = unique(sort(subset_T2D.obj$meta.dat$visit_number))[1],
+  ts.levels = unique(sort(subset_T2D.obj$meta.dat$visit_number))[-1],
+  strata.var = "subject_race",
+  feature.level = c("Phylum"),
+  feature.dat.type = "count",
+  prev.filter = 0.0001,
+  abund.filter = 0.0001,
+  Transform = "log",
+  theme.choice = "bw",
+  base.size = 12,
+  output.file = "path/report.pdf"
+)
 ```
 
 This automated workflow streamlines analysis and reporting for longitudinal studies. The integrated report synthesizes temporal dynamics across analytical dimensions.

diff --git a/...ted-reporting-for-paired-studies-microbiomestats-integrated-analysis-reports.md b/...ted-reporting-for-paired-studies-microbiomestats-integrated-analysis-reports.md
@@ -46,10 +46,12 @@ mStat_generate_report_change_pair(
   theme.choice = "bw",
   base.size = 12,
   palette = NULL,
-  output.file = "change_report.pdf" 
+  output.file = "path/change_report.pdf" 
 )
 ```
 
+{% file src="../.gitbook/assets/mStat_generate_report_change_pair_example.pdf" %}
+
 This reports biological variations between timepoints, facilitating discoveries.
 
 ### Paired Sample Analysis Report
@@ -87,8 +89,10 @@ mStat_generate_report_pair(
   feature.dat.type = "count",
   theme.choice = "bw",
   base.size = 12,
-  output.file = "report.pdf"
+  output.file = "path/report.pdf"
 )
 ```
 
+{% file src="../.gitbook/assets/mStat_generate_report_pair_example.pdf" %}
+
 The two functions enable automated paired sample analysis and reporting with MicrobiomeStat. Researchers can swiftly synthesize multidimensional dynamics through integrated reports.