Pipelines

A. Bulk-RNAseq

This is bulk RNAseq data processing pipe lines.

For data preprocessing (raw fastq files and outputs count matrix):

I have desinged the workflow using simple bash script as well as using Snakemake.

• For bash script follow the folder: bash_script
• For Snakemake follow the folder: snakemake

For Differential Analysis:

The corresponding folder is DESeq2.
This folder implements the workflow for differential gene expression analysis using DESeq2 bioconductor package. Follow the instructions in Bulk RNAseq Analysis.pdf file. The file DESeq2Design.pdf contains few examples to design and get the desired results from DESeq2 (dds) object.

PathwaysAnalysis

This folder contains a pdf file named Functional Pathways Analysis.pdf, which includes a brief introducion to the package clusterProfiler to do GO and KEEG pathways analysis.

B. scRNAseq

In this folder there are two files:

• sc-RNAseq cellrnager.pdf is cellranger workflow for demultiplexing and getting count matrix from scRNAseq BCL files.
• scRNA_data_analysis_complete_workflow.pdf is complete Seurat workflow for scRNAseq data analysis starting from cellranger output.

C. Flow Cytometry Data

The pdf file Flow Cytometry data analysis workflow describes the complete workflow for Flow Cytometry data.

D. Inferential statistics

In the stat folder there is jupyter notebook stat.ipynp which includes detailed explanation of z-test, t-test, ANOVA, correlation, regression and chi-square test with examples incorpotated from Udacity's free statistical inference course

E. nsolver_mRNA

It is a Python Package to QC, normalize and analyze nano string nsolver mRNA data. Look at the jupyter notebook (main.ipynb) in this folder for more detailed information.

F. Pyvis_Network

It is a example of plotting correlatin network graph between gene and metabolite data from sampe cohorts.