microplate 1.6.2

A package for dealing with microtiter plates

microplate: A class for manipulating microtiter plate data

Use Cases

The goal of microplate is to make working with microplate data in Python more convenient. Well data storage/retrieval is done through standard microtiter plate labels ('A1' or 'P5') rather than through array indices.

Features

• Import directly from files without any needed manipulation
• Arbitrary plate sizes supported (any number of rows/columns)
• Simplified storage/retrieval of microplate data by using traditional well labels, rows/columns, or ranges.
• Multiple data block support for assays with multiple reads.
• Store arbitrary regions in plates to simplify calculating plate statistics.
• Simple data normalization and statistic functions.
• Iterator support to retrieve all data.
• Easy view of plate data by printing the plate object.
• Metadata storage by well for plate.
• Hit cutoff and hit list retrieval based on microplate data.

Source/Installation

The source code is currently hosted on GitHub at: https://github.com/shumatejr/microplate

Binary installers for the latest released version are available at the Python Package Index (PyPI).

# PyPi
pip install microplate

Then just import the MTP class in your Python script.

from microplate import MTP

Dependencies

• NumPy

Example Usage

Plate Creation

# Create an empty plate
plate_ratio = MTP(name = "Test Plate", rows=2, columns=3, blocks=3)

# Or define it from a file
# Multiple input files can be passed to add multiple data blocks to the plate.
# input_files input is in the format (filename, delimiter, file_row, file_column)
plate = MTP(
    name = "384-Well Test Plate", 
    rows = 16, 
    columns = 24,
    input_files = [ 
        ('test.txt', ',', 1, 1),  # Comma delimited, Row 1, Column 1
        ('test.txt', '\t', 22, 2), # Tab delimited, Row 22, Column 2
    ]
)

# View entire plate contents
print(plate)

Well Manipulation/Retrieval

# Plate Access (empty string)
plate['']

# Well Access (well B3)
plate['B3'] = -1

# Row Access (row B)
plate['B']

# Column Access (column 3)
plate['3'] = 2

# Range Access
plate["A2:A3"] = -5

# If plates have multiple data blocks, add comma and value to specify block
plate['']    # Whole plate, implicitly data block 1
plate['',2]  # Whole plate, data block 2

plate["A2:A3",2] # Range from data block 2

# Values retrieved are numpy arrays, so 'matrix' math can be performed
# Make data block 3 a ratio of the other two data blocks
plate_ratio['',2] = 1
plate_ratio['',1] = 2

# Block 3 would store 2/1 in all wells
plate_ratio['',3] = plate_ratio['',2] / plate_ratio['',1]

Regions

# Define regions
plate.set_region("high_ctrl", "A1:P1")
plate.set_region("low_ctrl", "A12:P12")
plate.set_region("full_plate", "A1:P12")

# Regions can be wells, ranges, or lists of any combination of the two
plate.set_region("corners", ["A1", "A12", "P1", "P12"])
plate.set_region("edges", ["A1:A12", "P1:P12"])
plate.set_region("A1+Right", ["A1", "A12:P12"])

# And then retrieve their values
plate.get_region("full_plate")

# Or retrieve their wells labels
plate.get_labels("high_ctrl")

Normalization

# Normalize entire plate by zscore (returns a copy)
plate = plate.normalize_zscore()

# Normalize to a percent basis based on high/low control
plate_normalized = plate.normalize_percent(
    region_high = 'high_ctrl', 
    region_low = 'low_ctrl',
    method = 'median'
)

# All data blocks are normalized unless specified
# Scales data block 2 from 0-100%
plate_normalized = plate.normalize_percent(
    region_high = 'full_plate', 
    region_low = 'full_plate',
    block = 2,
    method = 'minmax'
)

Plate Calculations

# Calculate some basic plate statistics based on the defined regions
z_prime = plate.calc_z(region_high='high_ctrl', region_low='low_ctrl', block=1)
z = plate.calc_z(region_high='high_ctrl', region_low='sample',   block=1)
window = plate.calc_sw(region_high='high_ctrl', region_low='sample', block=1)
drift_row, drift_col = plate.calc_drift(region='sample', block=2)

Cutoffs

# Calculate avg+3sd hit cutoffs, then return a list of hits
hit_cutoff = plate.calc_cutoff_sd('sample', block=1)
# calc_cutoff_excluded removes outliers from the hit cutoff calculation
hit_cutoff_excluded = plate.calc_cutoff_excluded(
    'sample', block=1, region_high='high_ctrl', region_low='low_ctrl'
)
hit_list = plate.get_hits(region="sample", cutoff=hit_cutoff_excluded, block=2)

# Print the hit information.
# The get_region method can be passed a well list to get the well results for a list of wells.
print(f"Hits: {hit_list}")
print(f"Raw: {plate.get_region(wells=hit_list, block=1)}")
print(f"Activity: {plate.get_region(wells=hit_list, block=2).round(2)}")

Set Metadata

# Add a hit_flag key to the metadata dictionary indicating whether well is a hit
for well in plate.metadata:
    plate.metadata[well]['hit_flag'] = well in hit_list 

# The metadata dictionary can be initialzied with default keys for each well by
setting the metadata_keys for the class with the key and default value:
plate_metatest = MTP(rows=2, columns=3, metadata_keys={'concentration': None})

Iterator Support

# Sequential acess of all wells through an iterator
for (label, row, column, value) in plate():
    print(f"Well:{label} Row:{row} Column:{column} Value:{value}")

# A specific block alone can be accessed if passed
for (label, row, column, value) in plate(block=2):
    print(f"Well:{label} Row:{row} Column:{column} Value:{value}")

License

MIT

Credits

Developed by Justin Shumate