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mdftracks reads and writes MTrackJ Data Files (.mdf). Supports clusters, 2D data, and channel information. If desired, generates unique track identifiers based on cluster and id data from the .mdf file.
To get a bug fix or to use a feature from the development version, you can install the development version from GitHub.
First load the package with
mdf.file <- system.file("extdata", "example.mdf", package = 'mdftracks')
data <- read.mdf(mdf.file)
head(data, 10)
#> cluster id time x y z
#> 1.1.1 1 1 1 782 43 1
#> 1.1.2 1 1 2 784 45 1
#> 1.1.3 1 1 3 780 47 1
#> 1.1.4 1 1 4 786 56 1
#> 1.1.5 1 1 5 794 65 1
#> 1.1.6 1 1 6 800 69 1
#> 1.1.7 1 1 7 805 88 1
#> 1.1.8 1 1 8 804 100 1
#> 1.1.9 1 1 9 814 110 1
#> 1.1.10 1 1 10 823 125 1data <- read.mdf(mdf.file, drop.Z = T)
head(data, 10)
#> cluster id time x y
#> 1.1.1 1 1 1 782 43
#> 1.1.2 1 1 2 784 45
#> 1.1.3 1 1 3 780 47
#> 1.1.4 1 1 4 786 56
#> 1.1.5 1 1 5 794 65
#> 1.1.6 1 1 6 800 69
#> 1.1.7 1 1 7 805 88
#> 1.1.8 1 1 8 804 100
#> 1.1.9 1 1 9 814 110
#> 1.1.10 1 1 10 823 125(id, t, x, y, z) format (e.g., from celltrackR)#> id t x y
#> 1 1 48 90.8534 65.3943
#> 2 1 72 89.5923 64.9042
#> 3 1 96 88.6958 67.1125
#> 4 1 120 87.3437 68.2392
#> 5 1 144 86.2740 67.9236
#> 6 1 168 84.0549 68.2502
#> 7 1 192 85.9669 68.5470
#> 8 1 216 86.5280 69.0346
#> 9 1 240 84.6638 69.4034
#> 10 1 264 81.7699 69.9115#> Using the following column mapping:
#> cluster id time x y z channel point
#> NA "id" "t" "x" "y" NA NA NA
#> Converting factor to numeric in columns: id
#> MTrackJ 1.5.1 Data File
#> Assembly 1
#> Cluster 1
#> Track 1
#> Point 1 90.8534 65.3943 1 48 1
#> Point 2 89.5923 64.9042 1 72 1
#> Point 3 88.6958 67.1125 1 96 1
#> Point 4 87.3437 68.2392 1 120 1
#> Point 5 86.274 67.9236 1 144 1
#> Point 6 84.0549 68.2502 1 168 1
#> Point 7 85.9669 68.547 1 192 1
#> Point 8 86.528 69.0346 1 216 1
#> Point 9 84.6638 69.4034 1 240 1
#> Point 10 81.7699 69.9115 1 264 1
#> End of MTrackJ Data File#> cl id p x y z t ch uid
#> 1 1 1 1 187.1 263.2 27.4 1 2 1
#> 2 1 1 3 309.2 264.4 15.8 2 2 1
#> 3 1 2 1 18.4 438.5 28.1 1 2 2
#> 4 1 2 2 142.9 58.6 28.2 2 2 2
#> 5 1 2 5 290.1 197.5 18.8 3 2 2
#> 6 2 1 1 310.1 15.4 5.8 1 2 3
#> 7 2 2 1 99.1 33.5 22.5 1 2 4
#> 8 2 2 2 220.2 396.0 16.4 2 2 4
#> 9 2 3 1 8.4 305.8 30.2 1 2 5
#> 10 2 3 2 84.7 227.7 21.1 2 2 5write.mdf(mdftracks.example.data, cluster.column = 'cl', id.column = 'id',
pos.columns = letters[24:26], channel.column = 'ch',
point.column = "p")#> Using the following column mapping:
#> cluster id time x y z channel point
#> "cl" "id" "id" "x" "y" "z" "ch" "p"
#> MTrackJ 1.5.1 Data File
#> Assembly 1
#> Cluster 1
#> Track 1
#> Point 1 187.1 263.2 27.4 1 2
#> Point 3 309.2 264.4 15.8 1 2
#> Track 2
#> Point 1 18.4 438.5 28.1 2 2
#> Point 2 142.9 58.6 28.2 2 2
#> Point 5 290.1 197.5 18.8 2 2
#> Cluster 2
#> Track 1
#> Point 1 310.1 15.4 5.8 1 2
#> Track 2
#> Point 1 99.1 33.5 22.5 2 2
#> Point 2 220.2 396 16.4 2 2
#> Track 3
#> Point 1 8.4 305.8 30.2 3 2
#> Point 2 84.7 227.7 21.1 3 2
#> End of MTrackJ Data FileFor more information, please consult the package documentation.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.