---
title: "Sojourner: an R package for statistical analysis of single molecule trajectories"

author:
- name: Sheng Liu
  affiliation:
      - Department of Biology, Krieger School of Arts and Sciences, Johns Hopkins University
- name: Sun Jay Yoo
  affiliation:
      - Department of Biomedical Engineering, Johns Hopkins University
      - Department of Computer Science, Johns Hopkins University
- name: Xiaona Tang
  affiliation:
      - Department of Biology, Krieger School of Arts and Sciences, Johns Hopkins University
- name: Young Soo Sung
  affiliation:
      - Department of Computer Science, Johns Hopkins University
- name: Carl Wu
  affiliation:
      - Department of Molecular Biology and Genetics, School of Medicine, Johns Hopkins University
      - Department of Biology, Krieger School of Arts and Sciences, Johns Hopkins University

package: sojourner
date: "`r Sys.Date()`"
output:
    # rmarkdown::html_vignette:
        BiocStyle::html_document:
          toc: true
          toc_depth: 3
          toc_float: true
          number_sections: true

# output: 
# BiocStyle::pdf_document
          
          
vignette: >
    %\VignetteIndexEntry{Sojourner: an R package for statistical analysis of single molecule trajectories}
    %\VignetteEngine{knitr::rmarkdown}
    %\VignetteEncoding{UTF-8}
---

```{r style, echo = FALSE, results = 'asis'}
  BiocStyle::markdown()
```

![](sojourner_logo.png){width=30%}

# Introduction

A basic task in 2D single molecule tracking is to determine diffusion
coefficient from molecule trajectories identified after initial image data
acquisition. The trajectory (term used interchangeably with "tracks") of a
molecule is presented as a table of $x$,$y$-coordinates in the unit of pixel,
which then can be converted to other measurement, such as $\mu$m, according to
the resolution of the camera. The *sojourner* package provides methods to
import/export, process, and analyze such data. Analysis techniques include the
diffusion coefficient using the mean square displacement (MSD), displacement
cumulative distribution function (CDF), as well as hidden Markov model (HMM)
methods (in next release) from such input data. This vignette covers basic usage
of the package.

## Installation

Using BiocManager:

```{r, eval = FALSE}
if(!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("sojourner")
```

Or, using the release version from GitHub:

```{r, eval = FALSE}
devtools::install_github("sheng-liu/sojourner", build_vignettes = TRUE)
```

To load the package:

```{r, eval=TRUE, messsage = FALSE, warning=FALSE, results = 'hide'}
library(sojourner)
```

# Basic Track Data Structure

The input file for *sojourner* is the Diatrack (.txt or .mat), ImageJ Particle
Tracker (.csv), SLIMfast (.txt), or u-track (.mat) output file. Tracks are
extracted from the output files and then stored in a `trackll`, a list of track
lists. A track denotes a single trajectory stored in a `data.frame` recording
$x$, $y$, $z$ and (video) frame number. A `trackl` track list is a collection of
track data from one video file output. Lastly, a "trackll" is a collection of
"trackl" in one folder.

In this example, the folder "SWR1_2" contains one track list with 207 tracks and
one track list with 139 tracks:

```{r, eval = TRUE, results = 'hide'}
folder=system.file("extdata","SWR1_2",package="sojourner")
trackll = createTrackll(folder=folder, input=3)
```

```{r, eval = TRUE}
str(trackll,max.level=2, list.len=2)
```

Multiple folders can also be stored in a single trackll. Here, `trackll` denotes
a list of folders, and each `trackl` contains all tracks from every file in the
folder. This structure can be constructed as such:

```{r, eval = FALSE}
trackll=list(FOLDER=list(track=track))
```

```{r, eval = FALSE}
# Construct trackll from data.frame
trackl=list(track_1=dataframe_1,
            track_2=dataframe_2,
            track_3=dataframe_3,
            ...
            track_n=dataframe_n)

trackll=list(FOLDER_1=trackl_1,
             FOLDER_2=trackl_2,
             FOLDER_3=trackl_3,
             ...
             FOLDER_n=trackl_n,)
```

Merging all tracks from every file in the folder into a single `trackl` in a
`trackll` can also be done through `mergeTracks()`. In this example, both track
lists from "SWR1_2" were merged together to form a list of 346 tracks.

```{r, eval = TRUE, results = 'hide'}
folder=system.file("extdata","SWR1_2",package="sojourner")
trackll = createTrackll(folder=folder, input=3)
trackll <- mergeTracks(folder=folder, trackll=trackll)
```

```{r, eval = TRUE}
str(trackll,max.level=2, list.len=2)
```

To see the coordinates of an individual track:

```{r, eval = TRUE, warning=FALSE, echo = TRUE, results = 'hold'}
# Specify the folder name and the track name
trackll[["SWR1_2"]]["mage6.1.4.1.1"]
# Alternatively, specify the index of the folder and the track
# trackll[[1]][1]
```

# Track Data Manipulation

*sojourner* provides tools to manage and manipulate track data. Below are a few
examples of the commonly used functions to import, process, and export single
molecule track data.

## Reading Track Data

First, read in track data taken from Diatrack, ImageJ Particle Tracker,
SLIMfast, or u-track. In this example, we read data from ImageJ .csv-formatted
files from "SWR1_2".

```{r, eval=TRUE, warning=FALSE, echo=TRUE, results = 'hold'}
# Designate a folder and then create trackll from ImageJ .csv data
folder=system.file("extdata","SWR1_2",package="sojourner")
trackll = createTrackll(folder=folder, input=3)

# Alternatively, use interact to open file browser and select input data type
# trackll = createTrackll(interact = TRUE)
```

## Processing Track Data

### Linking Skipped Frames

Different tracking softwares (Diatrack, ImageJ Particle Tracker, SLIMfast, 
u-track) often use varying algorithms to stitch together single 
molecule localizations into trajectories. Sometimes, a molecule can disappear 
for several frames and can classify single trajectories as two. 
`linkSkippedFrames()` allows users to link trajectories that skip (or do not 
appear for) a number of frames. In this example, all tracks that skip a 
maximum of 10 frames and reappear within 5 pixels are linked:


```{r, eval=TRUE, warning=FALSE, echo = TRUE}
# Basic function call of linkSkippedFrames
trackll.linked <- linkSkippedFrames(trackll, tolerance = 5, maxSkip = 10)
```

### Filtering Tracks by Length

`filterTrack()` can be used to filter out track that have lengths (defined by 
number of frames) that fall within a specified range. In this example, only
tracks that appear for at least 7 frames are kept:

```{r, eval=TRUE, warning=FALSE, echo = TRUE}
trackll.filter=filterTrack(trackll ,filter=c(7,Inf))

# See the min and max length of the trackll
# trackLength() is a helper function output track length of trackll
lapply(trackLength(trackll),min)
lapply(trackLength(trackll.filter),min)
```

### Trim Tracks to a Length

`trimTrack()` is used to to trim/cutoff tracks. Given a specified range of 
track lengths (defined by number of frames), only tracks that fall within the 
range are kept, otherwise trimmed to the upper limit. In this example, all 
tracks with lengths greater than 20 have their lengths trimmed to 20:

```{r, eval=TRUE, warning=FALSE, echo = TRUE}
trackll.trim=trimTrack(trackll,trimmer=c(1,20))

# See the min and max length of the trackll
# trackLength() is a helper function output track length of trackll
lapply(trackLength(trackll),max)
lapply(trackLength(trackll.trim),max)
```

### Applying Image Masks

In order to apply a binary image mask to track data, one can use 
`maskTracks()`. In this example, we use image masks for each track file 
generated by thresholding the initial fluorescent glow of the nucleus. See man 
pages for `indexCell()`, `filterOnCell()`, and `sampleTracks()` for more 
advanced functionality (e.g. separating tracks, filtering, and sampling by 
masked cells).

```{r, eval=TRUE, warning=FALSE, echo=TRUE, results = 'hide'}
# Basic masking with folder path with image masks
folder = system.file("extdata","ImageJ",package="sojourner")
trackll = createTrackll(folder, input = 3)
trackll.filter=filterTrack(trackll ,filter=c(7,Inf))
trackll.masked <- maskTracks(folder = folder, trackll = trackll.filter)
```

To plot the masks:
```{r, eval=TRUE, warning=FALSE, echo=TRUE, results = 'hide',fig.show="hide"}
# Plot mask
plotMask(folder)
```

To plot the nuclear overlay of the un-masked data:
```{r, eval=TRUE, warning=FALSE, echo=TRUE, results = 'hide',fig.show="hide"}
# If nuclear image is available
plotNucTrackOverlay(folder=folder,trackll=trackll)
```

To plot the nuclear overlay of the masked data:
```{r, eval=TRUE, warning=FALSE, echo=TRUE, results = 'hide',fig.show="hide"}
# If nuclear image is available
plotNucTrackOverlay(folder=folder,trackll=trackll.masked)
```

## Exporting Track Data

To export a `trackll`, use `exportTrackll()` to save track data into a .csv file
in the working directory. This function saves the data into the same format used
by ImageJ Particle Tracker, fully preserving all track information, and
maintaining short read/write computation time and readability in Excel/etc.

```{r, eval=FALSE}
# Basic function call to exportTrackll into working directory
exportTrackll(trackll)

# Read exported trackll saved in working directory
trackll.2 <- createTrackll(folder = getwd(), input = 3)
```

# Plotting Tracks

The first thing one may want to do is to see how the tracks look like in 2-D
space.  The track name is useful in this case if one wants to see specific
trajectories and its associated movies.

All one needs to do is to create a .csv file contains trajectory names in its
first column. *sojourner* package contains such an example .csv file.

```{r, eval = FALSE}
# specify the path of the file containing trajectory index names, index file
index.file2=system.file("extdata","INDEX","indexFile2.csv",
package="sojourner")
# specify the folders containing the output files
folder1=system.file("extdata","SWR1",package="sojourner")
folder2=system.file("extdata","HTZ1",package="sojourner")
# plot trajectories specified in the trajectory index file
plotTrackFromIndex(index.file=index.file2,
                   movie.folder=c(folder1,folder2),input=3)
```


The output plots the tracks based on its name, with the information contained
in its name (i.e. start frame and length/duration), one can also pull out its
movie. See `? plotTrack` for more plotting options.

# Distribution of Trajectory Lengths

One may be interested to see the distribution of the (time) length of the
tracks. This can be done by calculating and plotting the dwell times:

```{r, eval = TRUE, warning=FALSE}
dwellTime(trackll,plot=TRUE) # default t.interval=10, x.scale=c(0,250)
```

# Calculating Diffusion Coefficient Using MSD-Based Method

Below is an example of simple analysis workflow using diffusion coefficient. 

## Data Import

We will need to first create a basic data structure (trackll, i.e. list of track list) for all analysis. It contains the trajectory information of all tracks in a specified folder, using createTrackll() function. 

```{r, eval = TRUE, warning=FALSE, echo=TRUE, results = 'hide'}

# Specify folder with data
folder=system.file("extdata","HSF",package="sojourner")

# Create track list
trackll<-createTrackll(folder=folder, interact=FALSE, input=3, ab.track=FALSE, cores=1, frameRecord=TRUE)

# Take a look at the list by 
str(trackll,1)

```

## Data Clean Up

We then clean up the data with filters and masks. In this case, we select only the tracks with 3 frames or more, and remove tracks that is outside of the nuclei.

```{r, eval = TRUE, warning=FALSE, echo=TRUE, results = 'hide'}
# Filter/choose tracks 3 frames or longer for all analysis
trackll.fi<-filterTrack(trackll=trackll, filter=c(min=3,max=Inf))

# Apply mask and remove tracks outside nuclei
trackll.fi.ma<-maskTracks(folder,trackll.fi)

```

## Visulization of Trajectories

To see how the tracks look like, we can plot the tracks over cell image (e.g. bright field image or nuclei overall fluorescence) to see the distribution of tracks.

```{r, eval = TRUE, warning=FALSE, echo=TRUE, results = 'hide',fig.show="hide"}

# Overlay all tracks on nuclei
plotNucTrackOverlay(folder=folder,trackll=trackll.fi,cores=1, max.pixel=128,nrow=2,ncol=2,width=16,height=16)

# Overlay tracks after nuclear mask
plotNucTrackOverlay(folder=folder,trackll=trackll.fi.ma,cores=1, max.pixel=128,nrow=2,ncol=2,width=16,height=16)
```

```{r, eval = TRUE, warning=FALSE, echo=TRUE, results = 'hide'}
# Overlay tracks color coded for diffusion coefficient
plotTrackOverlay_Dcoef(trackll=trackll.fi.ma, Dcoef.range=c(-2,1), rsquare=0.8, t.interval=0.01, dt=6, resolution=0.107)

```

We used Dcoef.range to select tracks whose Dcoef is within the range. Other tracks will not be plotted.

## Analysis

We then move on to the analysis of the biophysical properties of a molecule, e.g. mean square displacement (MSD), diffusion coefficient (Dcoef), residence time (RT) etc. 

For an overall view of one factor, we may want to merge all tracks in a folder. 

```{r, eval = TRUE, warning=FALSE, echo=TRUE, results = 'hide'}

# Merge tracks from different image files in the folder
trackll.fi.ma.me=c(mergeTracks(folder, trackll.fi.ma))


```

And sometimes, we may have data of same type in multiple folders and want to combine them together. 

```{r, eval = TRUE, warning=FALSE, echo=TRUE, results = 'hide'}
folder2=system.file('extdata','HSF_2',package='sojourner')
trackll2=createTrackll(folder2,input=2, cores = 1)
trackll2=maskTracks(folder2,trackll2)
trackll2=mergeTracks(folder2,trackll2)

# Combine the tracklls together
Trackll.combine=combineTrackll(trackll=c(trackll,trackll2),merged=TRUE)
```

Now we can calculate msd, Dcoef, and residence time of a molecule. 

```{r, eval = TRUE, warning=FALSE, echo=TRUE, results = 'hide'}
# calculate MSD for all tracks longer than 3 frames
msd(Trackll.combine,dt=20,resolution=0.107,summarize=TRUE,cores=1,plot=TRUE,output=TRUE)

```

For some analysis, we may just want to a portion of the trajectory, this can be done by using trimTrack() function.

```{r, eval = TRUE, warning=FALSE, echo=TRUE, results = 'hide',fig.show="hide"}
trackll.combine.trim=trimTrack(Trackll.combine,trimmer=c(1,11))

msd(trackll.combine.trim,dt=10,resolution=0.107,summarize=TRUE,cores=1,plot=TRUE,output=TRUE)
```

```{r, eval = TRUE, warning=FALSE, echo=TRUE, results = 'hide'}

Dcoef(trackll=trackll.combine.trim,dt=5, filter=c(min=6,max=Inf), method="static", plot=TRUE, output=TRUE)

```


```{r, eval = TRUE, warning=FALSE, echo=TRUE, results = 'hide',fig.show="hide"}
## calculating displacement CDF

# Calculate all dislacement frequency and displacement CDF for all tracks longer than 3 frames
displacementCDF(trackll.combine.trim, dt=1, plot=TRUE, output=TRUE)

```

```{r, eval = TRUE, warning=FALSE, echo=TRUE, results = 'hide',fig.show="hide"}
## Analysis for Residence time. 

# Calculate 1-CDF (survival probability) of the dwell time/residence time of all tracks (not trimmed) longer than 3 frames
compare_RT_CDF(trackll= Trackll.combine, x.max=100, filter=c(min=3,max=Inf), t.interval=0.5, output=FALSE)
```


```{r, eval = TRUE, warning=FALSE, echo=TRUE, results = 'hide'}
# Calculate residence time of tracks by 2-component exponential decay fitting of the 1-CDF curve
fitRT(trackll= trackll.fi.ma.me, x.max=100, N.min=1.5, t.interval=0.5)

```

This is just a brief example of analysis one can do using the statistic tools provided in sojourner package. Further information regarding function usage or analysis techniques can be found in function help docs and on https://sheng-liu.github.io/sojourner/ website.

# sojournerGUI: A Shiny Interface

A Shiny app implementation of many of the core features of sojourner. Namely,
the basic abilities of reading trackll video files (of all supported types),
processing tracks (linking, filtering, trimming, masking, merging), and
analyzing tracks (MSD, Dcoef, CDF, and dwell time). The application interface
provides a code-free GUI, suited with dynamic and interactive plots, that is
relatively easy to use. The app is still in alpha development and only supports
the base functions needed for educational capabilities and such. A command
history log, named *command_history.R* in the working directory, will be
continuously updated each time a command is called for diagnostic, replication,
and tracking purposes.

## Launching

```{r, eval=FALSE, echo=TRUE, results='hide', warning=FALSE}
sojournerGUI()
```

## Helpful Tips

* If in doubt, check the console output of each command inputted through the 
GUI. These will show error and warning messages as needed.

* Normal distribution, compare folder, and kernel density masking features are 
currently not supported.

* Reading tracks depends on a **running session of R**, as it uses its 
native `file.choose()` function.

# SessionInfo

Here is the sessionInfo() on which this document was compiled:

```{r, eval = TRUE}
sessionInfo()
```



