This page briefly describes the scripts that are available for common tasks that may be required, but have not been implemented in the main interface. As of version 1.4.10, currently 2 scripts are available. One can be used to convert multi-measurement files generated by ImageJ into a single Excel file. This script supplements the functionality of the template creator, and can be used directly after finishing the steps outlined in the template creator documentation. The other script is used to extract the pool data from the data structure and then export it to a delimited text file. This script depends on data from SynD, and requires that the pool sizes have been added to the data structure via Analysis | Determine Pool size (SynD). See the SynD pool size section in the algorithms section of this documentation for further information. This page will explain the working of these scripts and how they can be used, starting with the extracting of pool size data.
This script is meant to extract relevant data for performing statistics on the pool size and release probability in R or SPSS. The code is placed in the scripts folder in the installation folder of Fusion Analysis 2. It is called script_extract_release_probability.m, and is constructed using separate code sections that can be run individually or all at once. Before running this script, make sure that the following requirements have been met:
If the requirements have been met, all data should be available for the script to be run. The first two code sections (delineated by the double percentage (%%) symbols) set up the logging of errors and ask the user to select a session file. The next code section, labeled with [INIT], sets up the output structure and determines the independent experiments (denoted as 'week') in the output file. The next code section, labeled [POOLS], extracts the pool sizes and adds them to the output data. The next code section, labeled [INTENSITY], is optional and should only be used if dual color data is available. Finally, the last code section exports the data to a user-selected file in a specified location.
The generated file is a tab-delimited text file that can be imported in R as described in the statistics page of this documentation. The structure of the output file is suited for multilevel analysis and allows nesting upto 3 levels: week, coverslip and cell.
Back to topThis script can be used in conjunction with the template creator to make the generation of a data file ready for import into Fusion Analysis 2 a more automated, and less error-prone, affair. It assumes that all the multi-measure files from ImageJ are located in a single folder, and that the file name conforms to the naming conventions imposed by Fusion Analysis 2 (see the protocol page for more information). If you use the ImageJ macro extract_multi-measure_FA2.ijm, these files will be generated in the correct format.
The script is located in the scripts folder inside the Fusion Analysis 2 installation folder. It is called script_combine_multi_measurements.m and consists of several code sections that can be run as one piece of code or in individual sections. The [INIT] section sets two important parameters: whether localization was used (bCyan), and the number of frames each time series has (nFrames). Make sure that the values for these parameters are set correctly before running the script. The script will automatically find the relevant data files in the user-selected folder, and works for single and dual-color imaging.
The [PROCESS] and [EXPORT] code sections will collect the data and generate an Excel file in a user-selected folder, respectively. Please note that this processing my require a long time, depending on the number of frames and the number of cells analyzed. After it is done, the generated Excel file only requires the Infosheet to be added for proper importing in Fusion Analysis 2. This sheet can be copied from a previous data file, or created from scratch using the Template Creator (See the template creator documentation).
Back to topIncluded in the scripts folder is a script to export data about silent and releasing cells, called script_extract_silent_and_releasing_cells. This script requires a complete session file containing a data structure and a silent structure. It generates a delimited text file that contains the following (example) data:
id | week | coverslip | sheetname | condition | status |
1 | 1 | 1 | 180101_some-condition_cs1_001_g | some-condition | releasing |
2 | 1 | 1 | 180101_some-condition_cs1_666_g | some-condition | silent |
... | ... | ... | ... | ... | ... |
n | 3 | 15 | 180115_other-condition_cs2_666_g | other-condition | silent |
The id column contains a unique value for each row, running from 1 to the n number of rows in the table. The week column is a unique number that groups cells from the same week together. The assumption is that data acquired within the same calendar week comes from the same source. The coverslip column adds a unique number to each cover slip in the data set. Silent cells that were acquired from the same cover slip as the corresponding releasing cells will have the same number. The sheetname column is to help identify the original data source, and to make corrections in case those are needed. The condition column specifies the group the cell belongs to. This value is generally the same as the value in the sheetname. However, if the condtions were relabeled in the interface (using Edit | Condition labels), those values will be used. Finally, the status column indicates whether a cell had release (releasing) or was a silent cell (silent).
Back to topThis section will briefly describe a basic work flow that can be used to extract the data as automated as possible using 4 ImageJ macros, the SynD program and the two scripts mentioned in this page. Most of the default settings in this work flow assume a dual-color time series, but by modifying a few select parameters, this can be simplified to a single color time series as well. The following figure shows the general processing work flow to prepare the data for Fusion Analysis 2.
In the work flow, the decision can be made the first recode the files to random values to blind the observer to the underlying groups. This can be done using the recoding_files.ijm macro in ImageJ. The next step is to place the ROIs (either manually or semi-automated) and save the associated ROI files. At this point you can also generate the files required for SynD analysis (based on the ammonium response) using the prepare_synd_data.ijm script in ImageJ. At this point, you need to decode your files back to their original pattern before continuing (using the decoding_files.ijm macro in ImageJ) if you usde the recode macro.
With the measurement files and the time series in the same folder (and matching names), you can use the extract_multi-measure_FA2.ijm macro in ImageJ to generate result files that can be combined in the next step to generate a file for import in Fusion Analysis 2, using the matlab script called script_combine_multi_measurements.m (see above). After adding the Infosheet to that file, you can proceed with importing the data into Fusion Analysis 2.
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