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rnai-screen-tf/results/cellprofiler/2017-01-04/all-plates.properties
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#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 | |