Readme

PIPELINE.md

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+# Pipeline Information
+This pipeline is the backbone of sPARcRNA_Viz and provides the coordinates required to create the scRNA-seq visualizations. 
+
+## Input
+It takes the barcodes, features, and matrix files as inputs. The files need to either be in .csv/.tsv and .mtx format or in an R data format.
+
+## Output
+A json file with all the coordinates of the points in a tSNE that is used by the frontend to visualize it in an interactive way.
+
+## Workflow
+### 1. Setup
+Load libraries, set options, validate and prepare the directories, find and read raw data files, configure based on inputs
+### 2. Create Seurat object
+Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data.
+- Seurat was chosen because the gene expression data analyzed through this pipeline is single-cell RNA-seq, and it provides ways to normalize, scale, and visualize this data.
+### 3. Normalize and preprocess the data
+Normalize (so that data reflects true biological differences), find variable features, scale (to standardize the data), perform PCA (Principal Component Analysis to reduce dimensionality), cluster cells with similar profiles together
+### 4. t-SNE
+t-SNE allows us to visualize statistically significant genes based on these clusters. From these, researchers can determine potential gene ontologies arising from their sample(s).
+### 5. Differential Gene Expression Analysis
+Differential gene expression analysis takes the normalized gene read counts and allows researchers to determine quantitative changes in gene expression. 
+### 6. GSEA
+GSEA, or Gene set enrichment analysis, helps determine the gene groups that are highly represented in the data.
+### 7. Combine t-SNE and GSEA results
+All the cluster results after running GSEA are saved, and the top pathways are saved as well.
+### 8. Export and Display Results
+All values from the previous steps and top clusters, pathways, etc are saved in a json file that is later visualized
+
+## Overview of Functions
+- `make_options()`: allows for user input through command line, allows to input data files from local machine
+- `Read_MTX()`: reads the data from barcodes, features, and matrix files after patterns have been made and properly found from the input files given by the user
+- `CreateSeuratObject()`: Seurat object created from data saved and user inputs on the name, cells, and features
+- Cleaning the data and making it standardized so that it can be used for a tSNE and GSEA:
+    - `NormalizeData()`
+    - `ScaleData()`
+- Reducing the dimensionality, clustering, and running the tSNE and saving it:
+    - `RunPCA()`
+    - `FindNeighbors()`
+    - `FindClusters()`
+    - `RunTSNE()`
+    - `DimPlot()`
+    - `ggsave()`
+- `FindAllMarkers()`: performs the differential expression analysis
+- `GetAssayData()`: saves the normalized gene expression data, which makes sure that the data is not due to technical biases
+- tSNE coordinates, top 10 markers, top pathways, cluster results, cluster centroids, cluster average expression data, and more are saved and exported as a json file