[ome-users] OMERO and CellProfilerAnalyst

Wed Jul 26 16:09:02 BST 2017

Dear list,

We are using the LoadData module in CellProfiler 2.2 to analyse images 
fetched directly from our OMERO and this is working really well! I am 
super happy with that :)

What I'd like to find out now is if I could use CellProfilerAnalyst 
2.2.1 in the next step, have it connect to our OMERO and get the images 
within CPA as well via "access CellProfiler Analyst images via URL" from 
the "ExportToDatabase" module in CellProfiler.

Basically this gives me a field into which I can enter the URL to my 
image on the OMERO server, but I have not figured out the correct way to 
enter the URL , as you can see from the screenshot.

I've attached my LoadData module input csv and the CPA properties file 
for references.

Does anyone have experience setting this up?

Thanks and cheers,
Kai

-- 
>>Please note my NEW PHONE NUMBERS: +41 61 207 57 31 (direct) +41 61 207 22 50 (central)<<
Kai Schleicher, PhD | Research Associate in Advanced Light Microscopy | Biozentrum, University of Basel | Klingelbergstrasse 50/70 | CH-4056 Basel |
Phone: +41 61 207 57 31 (direct) +41 61 207 22 50 (central) | kai.schleicher at unibas.ch | www.biozentrum.unibas.ch | www.microscopynetwork.unibas.ch

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#Wed Jul 26 16:42:24 2017
# ==============================================
#
# CellProfiler Analyst 2.0 properties file
#
# ==============================================

# ==== Database Info ====
db_type         = sqlite
db_sqlite_file  = V:\simon_bepC\kaistDB.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      = 
well_id       = 
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    = Nucleifiltered_Location_Center_X
cell_y_loc    = Nucleifiltered_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_DAPI,Image_PathName_RFP,Image_PathName_Cy5,Image_PathName_IllumDAPI,Image_PathName_IllumRFP,Image_PathName_IllumCy5
image_file_cols = Image_FileName_DAPI,Image_FileName_RFP,Image_FileName_Cy5,Image_FileName_IllumDAPI,Image_FileName_IllumRFP,Image_FileName_IllumCy5

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

# Give short names for each of the channels (respectively)...
image_names = DAPI,RFP,Cy5,IllumDAPI,IllumRFP,IllumCy5

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

# ==== Image Accesss Info ====
image_url_prepend = https://omero.biozentrum.unibas.ch/webclient/img_detail/

# ==== 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  = 96

# ==== 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  = infected

# ======== 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

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