| Title: | Automated Persomics Array Image Extraction |
|---|---|
| Description: | Automated identification of printed array positions from high content microscopy images and the export of those positions as individual images written to output as multi-layered tiff files. |
| Authors: | John Smestad [aut, cre] |
| Maintainer: | John Smestad <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0 |
| Built: | 2024-11-15 04:06:28 UTC |
| Source: | https://github.com/cran/PersomicsArray |
The PersomicsArray package contains functions to sequentially read multi-channel jpeg or tiff high content microscopy image files, identify coordinates of printed siRNAs via one of the image channels, and then export individual multi-channel tiff images for each identified array position. The names of the exported images are supplied by an input csv file containing the array annotations. Images are exported as tiff files to automatically-created sub-directories named after the input image files.
| Package: | PersomicsArray |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2016-09-23 |
| License: | GPL-3 |
| LazyLoad: | yes |
This is version 1.0 of the PersomicsArray package.
John Smestad
[email protected]
<spot_id>
<plot_low_res>
<example_annotation>
<example_plate>
This is a very small example of the expected array annotation format taken as input by the spot_id function in the PersomicsArray pacakge.
example_annotationexample_annotation
data.frame
<PersomicsArray-package>
<spot_id>
<plot_low_res>
<example_plate>
This is a very small example image used for teaching functionality in the PersomicsArray pacakge.
example_plateexample_plate
data.frame
<PersomicsArray-package>
<spot_id>
<plot_low_res>
<example_annotation>
This function generates low resolution plots of pixel values stored in arrays, and is called by function spot_id.
plot_low_res(plot.image=NULL, rescale.factor=1000, add=FALSE, pallete=gray.colors(20),main=NULL)plot_low_res(plot.image=NULL, rescale.factor=1000, add=FALSE, pallete=gray.colors(20),main=NULL)
plot.image |
Array-type object containing image data. |
rescale.factor |
Numeric value specifying the fold reduction of resolution from original image. Larger values make the function run faster, but at the cost of image resolution. |
add |
If TRUE, adds the plot to the current active device. If FALSE, a new plot is generated. |
pallete |
Specifies the color pallete to be used for generating the plot. |
main |
Character string containing the plot title. |
John Smestad
[email protected]
<PersomicsArray-package>
<spot_id>
<example_annotation>
<example_plate>
# load example image data from package data("PersomicsPlate") # isolate single color channel red <- example_plate[,,1] # plot low resolution image of plot_low_res(red, rescale.factor=10)# load example image data from package data("PersomicsPlate") # isolate single color channel red <- example_plate[,,1] # plot low resolution image of plot_low_res(red, rescale.factor=10)
This function sequentially reads multi-channel jpeg or tiff high content microscopy image files, identifies coordinates of printed siRNAs via one of the image channels, and then exports individual multi-channel tiff images for each identified array position. The names of the exported images are supplied by an input csv file containing the array annotations. Images are exported to automatically-created sub-directories named after the input image files.
spot_id(files, annotation, channel.num=3, spot.channel=1, smooth.cycle=4, binary.cut= 0.3, channel.scaling=TRUE, scale.percentiles=c(0.01,0.99))spot_id(files, annotation, channel.num=3, spot.channel=1, smooth.cycle=4, binary.cut= 0.3, channel.scaling=TRUE, scale.percentiles=c(0.01,0.99))
files |
Vector containing the names of desired files to be read from the current working directory. All array images must have invariable numbers of rows and columns. |
annotation |
Name of the csv file in the current working directly that contains array annotations. File must have invariable number of rows and columns. |
channel.num |
Numeric value specifying the number of color channels contained by the input image. jpeg images contain a max of 3. tiff files can contain more than this. The maximum number of channels used by this function is 6. |
spot.channel |
Numeric value specifying the channel number used for identification of grid array spots. |
smooth.cycle |
Numeric value specifying the number of iterations of smoothing operation applied to the extracted binary image prior to identification of pixel clusters. Designed for elimination of rogue pixels from binary image. |
binary.cut |
Numeric value in the range of 0-1 defining the cut-off pixel intensity for defining the binary image. |
channel.scaling |
If TRUE, all color channels are scaled according to the values supplied to scale.percentiles. This parameter scales the input data to optimize coverage of the dynamic range of displayed pixel intensity values. If FALSE, input data is used "as is" with no scaling operation applied. |
scale.percentiles |
Vector of two numeric values in the range of 0-1 that specify the percentiles of pixel intensity values used to define min and max for individual channel signal. |
John Smestad
[email protected]
<PersomicsArray-package>
<plot_low_res>
<example_annotation>
<example_plate>
# get names of all plate image files in current working directory tif.files <- list.files(path = getwd(), pattern = ".tif", all.files = FALSE, full.names = FALSE, recursive = FALSE, ignore.case = TRUE, include.dirs = FALSE) # read images, process, and extract individual image files for each array position spot_id(files=tif.files,ann="annotation.csv",channel.num=3, spot.channel=1, smooth.cycle=4, binary.cut= 0.3, channel.scaling=TRUE, scale.percentiles=c(0.01,0.99))# get names of all plate image files in current working directory tif.files <- list.files(path = getwd(), pattern = ".tif", all.files = FALSE, full.names = FALSE, recursive = FALSE, ignore.case = TRUE, include.dirs = FALSE) # read images, process, and extract individual image files for each array position spot_id(files=tif.files,ann="annotation.csv",channel.num=3, spot.channel=1, smooth.cycle=4, binary.cut= 0.3, channel.scaling=TRUE, scale.percentiles=c(0.01,0.99))