This dataset contains around 234 time-series with 121 time points each. The set comes from a microfluidic time-lapse microscopy experiment. In this experiment, PC-12 cells were stimulated with 3 dosages of fibroblast growth factor 2 (FGF2): 2.5 ng/ml, 25 ng/ml, and 250 ng/ml. The stimulation was performed with 3' pulses of FGF2 at 20' intervals. The readout is the measurement of ERK activity using a FRET biosensor.
The set comes from a publication Blum *et al.* (2019) *Temporal perturbation of Erk dynamics reveals network architecture of FGF2-MAPK signaling* (**preprint**), available in [bioRxiv](https://www.biorxiv.org/content/10.1101/629287v1"bioRxiv")].
Files in this set:
**`mp3-20_FGF_ekar.csv.gz`**
Main dataset in the long format with following columns:
-`intensity_ekar` - measurement of the biosensor
-`realtime` - time points in minutes
-`id` - identifiers of time-series
-`group` - grouping according to treatment with 3 concentrations of FGF2
-`fov` - additional grouping according to the field of view during experiment acquisition.
Note, the `id` column contains identifiers that are unique only within a single field of view. Thus, in order to create data-wide unique identifiers, you need to combine the `fov` and `id` columns.
The `csv.gz` file can be uploaded directly into Time Course Inspector (TCI).
**`mp3-20_FGF_badTraj.csv`**
A single-column csv file with identifiers of outlier trajectories. This file can be uploaded into TCI, and listed time series will be removed from further analysis. The IDs need to be in the same format as in the app. If a unique identifier was created within the app (e.g. by combining ''fov'' and ''id'' columns), then the IDs in this file need to be of the same form.
**`mp3-20_FGF_pulsey_y0-97.csv`** and **`mp3-20_FGF_pulse_y1100`**
Files with coordinates of segments to plot under time-series. Each row should contain x and y coordinates of the start and end of segments (4 columns: `tstart`, `tend`, `ystart`, `yend`), `group` column used for grouping of the main dataset in the app, a dummy `id` column.
'Accepts CSV file with a column of cell IDs for removal.
IDs should correspond to those used for plotting.
Say, the main data file contains columns Metadata_Site and TrackLabel.
These two columns should be then selected in UI to form a unique cell ID, e.g. 001_0001 where former part corresponds to Metadata_Site and the latter to TrackLabel.',
'Plotting and data processing requires a unique cell ID across entire dataset. A typical dataset from CellProfiler assigns unique cell ID (TrackLabel) within each field of view (Metadata_Site).
Therefore, a unique ID is created by concatenating these two columns. If the dataset already contains a unique ID, UNcheck this box and select a single column only.',
'This option allows to interpolate NAs or missing data. Some rows in the input file might be missing because a particular time point might not had been acquired.
This option, interpolates such missing points as well as points with NAs in the measurement column. When this option is checked, the interval of time column must be provided!',
'Accepts CSV file with 5 columns: grouping (e.g. condition), start and end time points of stimulation, start and end points of y-position, dummy column with id.'
)
help.text.short=c(
'Load CSV file with a column of cell IDs for removal. IDs should correspond to those used for plotting.',
'Load CSV file with a column of track IDs for removal. IDs should correspond to those used for plotting.',
'If the track ID is unique only within a group, make it unique globally by combining with the grouping column.',
'Interpolate missing tpts and pre-existing NAs. When checked, the interval of time column must be provided!',
'Load CSV file with 5 columns: grouping, start and end tpts of stimulation, start and end of y-position, dummy column with id.',
'Interpolate missing time points and pre-existing NAs. The interval of the time column must be provided!',
'Load CSV file with 5 columns: grouping, start and end tpts of stimulation, start and end of y-position, dummy column with ID.',
'Select columns to group data according to treatment, condition, etc.',
'Select math operation to perform on a single or two columns,',
'Select range of time for further processing.',
'Normalise data to a selected region.',
'Download data after modification in this section.'
'Normalise time series to a selected region.',
'Download time series after modification in this section.'