LemnaMiner

Data extraction with a user friendly interface

The LemnaMiner software module enables the researcher to display, filter and export resulting data from LemnaGrid.

To ensure data integrity within the database, LemnaMiner saves a local copy of the queried results in a SQLite File on the workstation. The SQLite file tabulates measured parameters in columns and assays in rows.  

Main Window with Image View

Column Operations

As a first step, columns of experimental parameters can be filtered or rearranged to those that are meaningful for the current application.

Often different columns need to be combined with each other to express ratios or to calculate averages. Consequently, there are different column functions that can be applied to create mathematical formulas or calculation of averages and standard deviations. Column functions can be created by the researcher using a graphical dataflow chart that allows the combination of numerical columns, the comparison of character strings or the use of boolean operations. A lot of common column functions are already available. To apply these, the researcher selects the appropriate input columns and labels the result. In addition, more advanced functions like calculation of digital biomass from 2 side views and a top view area are also predefined and can be easily applied.

Reduced Table Layout

Reproducible Table Layout

The researchers current layout can be saved and later applied to other datasets to create the similar tabulated formats. All calculations, filters and other operations that have been applied to the current dataset can be performed on any other loaded dataset within the LemnaMiner without any user input.

To filter the rows of the resultant table, various groups can be created. A group of rows is defined by some common properties in any column. By using groups the rows can for example be filtered to a certain timespan or by any other column in the table.

Logarithmic Line Plot of Plant Growth

Plotting

LemnaMiner offers different options such as line- or scatter-lots to display the data.

The Scatter plot can compare different columns on the Y-Axis over a common X-Axis. The plot can display all column values by just dragging and dropping them on the axis. Scatter plots can help to identify correlations between different columns or to see the spread of the data. The Line plot selects rows out of a column by criteria like ID-Tag and draws a line for all rows that match the same criteria to show relative changes in the data. Similar to this the Growth Plot can display any value over time and save average slope rate as a column in the table. When the Growth plot is applied to parameters like size it can calculate growth rates for each plant according to different models such as linear, exponential or gamma functions.  

Export Results

All data can be exported as a CSV- or SQLite-file and therefore be further analyzed if necessary.