Update readme.md

readme.md

@@ -5,20 +5,23 @@ This repository contains code and example data for LC-MS/MS and amplicon sequenc
 
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-**List of Notebooks provided in this repository and its use:**
+# List of Notebooks provided in this repository and its use:
 1. ASV_raw_data_processing.ipynb <br>
      - The ASV 16S and 18SV9 files are used here. The sample names are renamed similar to that of the mzxml file names. Also, the taxonomic information for each feature is combined with the OTU table.
 2. Elemental_composition.ipynb <br>
      - The summary files (.tsv format) from SIRIUS and CANOPUS outputs are used to get the elemental composition and some elemental ratios for each feature. Also van-Krevelen plots can be made.
 3. Data cleanup of feature table (combine SIRIUS, Canopus info) and PCoA & PermANOVA<br>
      - _to be updated_
-5. After PCoA, we went Stacked Bar Plots (with Phyla level binning for Sequencing data; and SuperClass level binning for Metabolites data)<br>
+5. StackedBarPlot_ASV.ipynb <br>
+     - Using the ASV table (16S and 18SV9) and metadata, different stacked bar plots can be made for a particular taxonomic level binning (Phylum level or Genus level) according to different cycles (Cycle 1 Day 1 --> Cycle 4 Day 2)
+6. StackedBarPlot_Metabolites.ipynb <br>
+     - _to be updated_ Currently working on
+     - Using the feature table, different stacked bar plots can be made for a SuperClass level binning (Superclass as given by SIRIUS) according to different cycles . In addition to that, the binned information are saved and will be used further for Sunburst visualization
+7. Sunburst visualisation (in Python Notebook) - until Genus level for sequencing data <br>
      - _to be updated_
-6. Sunburst visualisation (in Python Notebook) - until Genus level for sequencing data <br>
+8. Correlation Analysis script: (Focus on explanation of FDR, how you do it and justification for the method)<br>
      - _to be updated_
-7. Correlation Analysis script: (Focus on explanation of FDR, how you do it and justification for the method)<br>
-     - _to be updated_
-8. To list the important features, we tried RF and XGBoost (But we should focus on surface samples with different cycles) <br>
+9. To list the important features, we tried RF and XGBoost (But we should focus on surface samples with different cycles) <br>
      - _to be updated_
 
 ## FBMN links: