https://github.com/burgerga/cptrackr

R package to create unique track identifiers for CellProfiler tracking output
https://github.com/burgerga/cptrackr

Last synced: 16 days ago
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R package to create unique track identifiers for CellProfiler tracking output

• Host: GitHub
• URL: https://github.com/burgerga/cptrackr
• Owner: burgerga
• License: other
• Created: 2020-06-26T14:12:09.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-12-13T21:38:19.000Z (about 1 year ago)
• Last Synced: 2024-11-15T15:44:24.961Z (3 months ago)
• Language: R
• Size: 397 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

        
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# CPTrackR

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4725472.svg)](https://doi.org/10.5281/zenodo.4725472)

[![R-CMD-check](https://github.com/burgerga/CPTrackR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/burgerga/CPTrackR/actions/workflows/R-CMD-check.yaml)

The goal of CPTrackR is to add unique track ids to CellProfiler tracking output.

**NB:** No support for LAP tracking with temporal gaps (yet)

## Installation

You can install the development version of CPTrackR with:

``` r

remotes::install_github("burgerga/CPTrackR")

```

## Usage

### Creating a lookup table (LUT)

```{r, echo=F, message = FALSE, warning=FALSE}

library(tidyverse)

library(future)

plan(multisession)

# Example data is generated from a dataset from Britt (shared by Muriel)

# all_data <- read_tsv(file.path("~/../../Downloads","20190925_BrittData_singleCell_1hrDelay_withoutT01.txt"))

# data = all_data |>

#   filter(groupNumber <= 2) |>

#   select(groupNumber,

#          groupInd,

#          Nuclei_TrackObjects_ParentObjectNumber_30,

#          Nuclei_Number_Object_Number,

#          Nuclei_Intensity_MeanIntensity_image_green,

#          starts_with("Nuclei_Location_Center_"))

theme_set(theme_classic(base_size = 13))

```

Show some example uncorrected data extracted from a CellProfiler tsv:

```{r}

library(CPTrackR)

library(tidyverse)

data <- read_tsv(system.file("extdata", "cptrackr_example_data.tsv.xz", package="CPTrackR"), show_col_types = F)

data %>%

  select(groupNumber, 

         groupInd, 

         Nuclei_TrackObjects_ParentObjectNumber_30, 

         Nuclei_Number_Object_Number,

         Nuclei_Intensity_MeanIntensity_image_green)

```

We can create a lookup table (LUT) for a single group using `createLUTGroup`:

```{r}

library(CPTrackR)

lut <- createLUTGroup(data %>% filter(groupNumber == 1),

                      frame_var = groupInd, 

                      obj_var = Nuclei_Number_Object_Number,

                      par_obj_var = Nuclei_TrackObjects_ParentObjectNumber_30) 

lut %>% 

  arrange(Nuclei_Number_Object_Number, groupInd)

```

Three new columns are added:

* `cid`: id of the original cell (daughter cells share `cid` with parent)

* `uid`: unique id (daughter cells don't share `uid` with parent)

* `alt_uid`: character id of cells that show lineage with suffixes

For illustration here the second frame, where we can see `alt_uid`s for daughter cells:

```{r}

lut %>% 

  filter(groupInd == 2) %>%

  arrange(Nuclei_Number_Object_Number, groupInd)

```

We can also enable a progress bar (will be visible if you run this code in R):

```{r}

library(progressr)

with_progress({

  lut <- createLUTGroup(data %>% filter(groupNumber == 1),

                        frame_var = groupInd, 

                        obj_var = Nuclei_Number_Object_Number,

                        par_obj_var = Nuclei_TrackObjects_ParentObjectNumber_30) 

})

lut %>% 

  arrange(Nuclei_Number_Object_Number, groupInd)

```

We can create a LUT for multiple groups (=movies) using `createLut`, the `group_vars` are used to denote the different groups (can be multiple columns):

```{r}

with_progress({

  lut_all <- createLUT(data, 

                       group_vars = groupNumber, 

                       frame_var = groupInd, 

                       obj_var = Nuclei_Number_Object_Number, 

                       par_obj_var = Nuclei_TrackObjects_ParentObjectNumber_30) 

})

lut_all 

```

Now we can join the LUT to the original data

```{r}

fixed <- data %>% 

  left_join(lut_all) 

fixed %>%

  select(groupNumber, groupInd, uid, alt_uid, Nuclei_Intensity_MeanIntensity_image_green)

```

#### Parallelisation

We can also enable parallelisation using the `future` package and specifying a `plan`, this will give a considerable speed improvement if you have many movies:

```{r, eval = F}

library(future)

plan(multisession)

with_progress({

  lut_all <- createLUT(data, 

                       group_vars = groupNumber, 

                       frame_var = groupInd, 

                       obj_var = Nuclei_Number_Object_Number, 

                       par_obj_var = Nuclei_TrackObjects_ParentObjectNumber_30) 

})

lut_all

```

```{r, echo = F}

lut_all

```

### Plotting

With our `uid` per cell we can now plot the tracks:

```{r}

ggplot(fixed %>% filter(groupNumber == 1), 

       aes(Nuclei_Location_Center_X, Nuclei_Location_Center_Y, 

           group = uid, color = as.factor(uid))) + 

  geom_path() + 

  guides(color = F) +

  coord_fixed()

```