all-plates.properties

#Wed Jan  4 22:42:40 2017
# ==============================================
#
# CellProfiler Analyst 2.0 properties file
#
# ==============================================

# ==== Database Info ====
db_type         = sqlite
db_sqlite_file  = /work/pan14001/cellprofiler_all-plates/all-plates.db

# ==== Database Tables ====
image_table   = Per_Image
object_table  = Per_Object

# ==== Database Columns ====
# Specify the database column names that contain unique IDs for images and
# objects (and optionally tables).
#
# table_id (OPTIONAL): This field lets Classifier handle multiple tables if
#          you merge them into one and add a table_number column as a foreign
#          key to your per-image and per-object tables.
# image_id: must be a foreign key column between your per-image and per-object
#           tables
# object_id: the object key column from your per-object table

image_id      = ImageNumber
object_id     = ObjectNumber
plate_id      = Image_Metadata_Plate
well_id       = Image_Metadata_Well
series_id     = Image_Group_Number
group_id      = Image_Group_Number
timepoint_id  = Image_Group_Index

# Also specify the column names that contain X and Y coordinates for each
# object within an image.
cell_x_loc    = nuc_Location_Center_X
cell_y_loc    = nuc_Location_Center_Y

# ==== Image Path and File Name Columns ====
# Classifier needs to know where to find the images from your experiment.
# Specify the column names from your per-image table that contain the image
# paths and file names here.
#
# Individual image files are expected to be monochromatic and represent a single
# channel. However, any number of images may be combined by adding a new channel
# path and filename column to the per-image table of your database and then
# adding those column names here.
#
# NOTE: These lists must have equal length!
image_path_cols = Image_PathName_nuc_raw,Image_PathName_ect_raw,Image_PathName_cen_raw
image_file_cols = Image_FileName_nuc_raw,Image_FileName_ect_raw,Image_FileName_cen_raw

# CPA will now read image thumbnails directly from the database, if chosen in ExportToDatabase.
image_thumbnail_cols = Image_Thumbnail_cen,Image_Thumbnail_ect,Image_Thumbnail_im_outlines,Image_Thumbnail_nuc

# Give short names for each of the channels (respectively)...
image_names = nuc_raw,ect_raw,cen_raw

# Specify a default color for each of the channels (respectively)
# Valid colors are: [red, green, blue, magenta, cyan, yellow, gray, none]
image_channel_colors = red, green, blue, cyan, magenta, yellow, gray

# ==== Image Accesss Info ====
image_url_prepend = 

# ==== Dynamic Groups ====
# Here you can define groupings to choose from when classifier scores your experiment.  (eg: per-well)
# This is OPTIONAL, you may leave "groups = ".
# FORMAT:
#   group_XXX  =  MySQL select statement that returns image-keys and group-keys.  This will be associated with the group name "XXX" from above.
# EXAMPLE GROUPS:
#   groups               =  Well, Gene, Well+Gene,
#   group_SQL_Well       =  SELECT Per_Image_Table.TableNumber, Per_Image_Table.ImageNumber, Per_Image_Table.well FROM Per_Image_Table
#   group_SQL_Gene       =  SELECT Per_Image_Table.TableNumber, Per_Image_Table.ImageNumber, Well_ID_Table.gene FROM Per_Image_Table, Well_ID_Table WHERE Per_Image_Table.well=Well_ID_Table.well
#   group_SQL_Well+Gene  =  SELECT Per_Image_Table.TableNumber, Per_Image_Table.ImageNumber, Well_ID_Table.well, Well_ID_Table.gene FROM Per_Image_Table, Well_ID_Table WHERE Per_Image_Table.well=Well_ID_Table.well



# ==== Image Filters ====
# Here you can define image filters to let you select objects from a subset of your experiment when training the classifier.
# FORMAT:
#   filter_SQL_XXX  =  MySQL select statement that returns image keys you wish to filter out.  This will be associated with the filter name "XXX" from above.
# EXAMPLE FILTERS:
#   filters           =  EMPTY, CDKs,
#   filter_SQL_EMPTY  =  SELECT TableNumber, ImageNumber FROM CPA_per_image, Well_ID_Table WHERE CPA_per_image.well=Well_ID_Table.well AND Well_ID_Table.Gene="EMPTY"
#   filter_SQL_CDKs   =  SELECT TableNumber, ImageNumber FROM CPA_per_image, Well_ID_Table WHERE CPA_per_image.well=Well_ID_Table.well AND Well_ID_Table.Gene REGEXP 'CDK.*'



# ==== Meta data ====
# What are your objects called?
# FORMAT:
#   object_name  =  singular object name, plural object name,
object_name  =  cell, cells,

# What size plates were used?  96, 384 or 5600?  This is for use in the PlateViewer. Leave blank if none
plate_type  = 384

# ==== Excluded Columns ====
# OPTIONAL
# Classifier uses columns in your per_object table to find rules. It will
# automatically ignore ID columns defined in table_id, image_id, and object_id
# as well as any columns that contain non-numeric data.
#
# Here you may list other columns in your per_object table that you wish the
# classifier to ignore when finding rules.
#
# You may also use regular expressions here to match more general column names.
#
# Example: classifier_ignore_columns = WellID, Meta_.*, .*_Position
#   This will ignore any column named "WellID", any columns that start with
#   "Meta_", and any columns that end in "_Position".
#
# A more restrictive example:
# classifier_ignore_columns = ImageNumber, ObjectNumber, .*Parent.*, .*Children.*, .*_Location_Center_.*,.*_Metadata_.*

classifier_ignore_columns  =  table_number_key_column, image_number_key_column, object_number_key_column

# ==== Other ====
# Specify the approximate diameter of your objects in pixels here.
image_tile_size   =  50

# Provides the image width and height. Used for per-image classification.
# If not set, it will be obtained from the Image_Width and Image_Height
# measurements in CellProfiler.

# image_width  = 1000
# image_height = 1000

# OPTIONAL
# Image Gallery can use a different tile size (in pixels) to create thumbnails for images
# If not set, it will be the same as image_tile_size

image_size =

# ======== Classification type ========
# OPTIONAL
# CPA 2.2.0 allows image classification instead of object classification.
# If left blank or set to "object", then Classifier will fetch objects (default).
# If set to "image", then Classifier will fetch whole images instead of objects.

classification_type  = 

# ======== Auto Load Training Set ========
# OPTIONAL
# You may enter the full path to a training set that you would like Classifier
# to automatically load when started.

training_set  =

# ======== Area Based Scoring ========
# OPTIONAL
# You may specify a column in your per-object table which will be summed and
# reported in place of object-counts when scoring.  The typical use for this
# is to report the areas of objects on a per-image or per-group basis.

area_scoring_column =

# ======== Output Per-Object Classes ========
# OPTIONAL
# Here you can specify a MySQL table in your Database where you would like
# Classifier to write out class information for each object in the
# object_table

class_table  = colocalization

# ======== Check Tables ========
# OPTIONAL
# [yes/no]  You can ask classifier to check your tables for anomalies such
# as orphaned objects or missing column indices.  Default is on.
# This check is run when Classifier starts and may take up to a minute if
# your object_table is extremely large.

check_tables = yes