---
title: "Quick start of flowSpy"
author: "Yuting Dai"
date: "`r Sys.Date()`"
output:
prettydoc::html_pretty:
highlight: github
theme: cayman
toc: yes
pdf_document:
toc: yes
html_document:
df_print: paged
toc: yes
package: flowSpy
vignette: |
%\VignetteIndexEntry{Quick_start}
\usepackage[utf8]{inputenc}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
---
```{r echo = TRUE}
knitr::opts_chunk$set(echo = TRUE, cache = FALSE, eval = TRUE,
warning = TRUE, message = TRUE)
```
## Note
----------------
Dear flowSpy users:
To improve the identification of this package and avoid awkward duplication of names in some situations, we decided to change the name of `flowSpy` to `CytoTree`. The package name of `CytoTree` more fits the functional orientation of this software. The usage and update of `flowSpy` and `CytoTree` will be consistent until the end of Bioc 3.11. And for the 3.12 devel, flowSpy will be deprecated.
The package `CytoTree` has been added to Bioconductor (https://bioconductor.org/packages/CytoTree/), we recommend that users can download this package and replace `flowSpy` as soon as possible.
We apologized for the inconvenience.
flowSpy team
2020-07-09
----------------
## Link to the tutorial
See the quick start tutorial of flowSpy, please visit [Quick start of flowSpy](https://ytdai.github.io/flowSpy/Quick_start.html).
See the basic tutorial of flowSpy, please visit [Tutorial of flowSpy](https://ytdai.github.io/flowSpy/basic.html).
See time-course data analysis of flowSpy, please visit [Time-course workflow of flowSpy](https://ytdai.github.io/flowSpy/Time_course.html).
## Introduction
Although multidimensional single-cell-based flow and mass cytometry have been increasingly applied to microenvironmental composition and stem-cell research, integrated analysis workflows to facilitate the interpretation of experimental cytometry data remain underdeveloped. We present flowSpy, a comprehensive R package designed for the analysis and interpretation of flow and mass cytometry data. We applied flowSpy to mass cytometry and time-course flow cytometry data to demonstrate the usage and practical utility of its computational modules. flowSpy is a reliable tool for multidimensional cytometry data workflows and produces compelling results for trajectory construction and pseudotime estimation.
## Overview of flowSpy workflow
The flowSpy package is developed to complete the majority of standard analysis and visualization workflow for FCS data. In flowSpy workflow, an S4 object in R is built to implement the statistical and computational approach, and all computational modules are integrated into one single channel which only requires a specified input data format.
`flowSpy` can help you to perform four main types of analysis:
- **Clustering**. `flowSpy` can help you to discover and identify subtypes of cells.
- **Dimensionality Reduction**. Several dimensionality reduction methods are provided in `flowSpy` package such as Principal Components Analysis (PCA), t-distributed Stochastic Neighbor Embedding (tSNE), Diffusion Maps and Uniform Manifold Approximation and Projection (UMAP). flowSpy provides both cell-based and cluster-based dimensionality reduction.
- **Trajectory Inference**. `flowSpy` can help you to construct the cellular differential based on minimum spanning tree (MST) algorithm.
- **Pseudotime and Intermediate states definition**. The root cells need to be defined by users. The trajctroy value will be calculated based on Shortest Path from root cells and leaf cells using R `igraph` package. Subset FCS data set in `flowSpy` and find the key intermediate cell states based on trajectory value.
**Fig. 1 Workflow of flowSpy**
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