PlateViiewer fails

Hi there,
I plan to run CP-CPA workshop for students in WIS soon, and I wish to show the PlateViewer but I get this error message:
In the past I was able to choose plate type in CellProfiler in ExportToDatabase module, but this option does not exist anymore.

Thanks,
Noga
Noga1.cpproj (651 KB)

Hi Noga!

This was a bug which we fixed, but it still exists in the current release (github.com/CellProfiler/CellPro … ssues/1204). So you have at least two options:
(1) The setting does appear if you choose “Access CPA images by URL”, so you could modify your exercise to use URL, but that is likely more work than you want.
(2) Upgrade your CellProfiler version to a “trunk build” (cellprofiler.org/cgi-bin/trunk_build.cgi) but since that is not the current “standard” CP, that is perhaps not the bets option for a workshop.
(3) The most straightforward solution is to edit the *.properties file directly, after you run CP without entering the plate_type. If you open the .properties file in a simple text editor, you will see a “plate_type” field where the user can enter 6, 24, 96, 384, 1536, or 5600. For your workshop, you might best just provide this pre-configured .properties file.

Hope that helps!
David

Thank you for your reply.
I tried to add 96 in the properties file as you suggested, but the plate viewer still wont open.
I copied the text in the properties file here for you review.

please advise.
Thanks,
Noga

#Mon Apr 13 14:27:55 2015

==============================================

CellProfiler Analyst 2.0 properties file

==============================================

==== Database Info ====

db_type = sqlite
db_sqlite_file = C:\Users\student\Desktop\CellProfiler Workshop\output1\DefaultDB.db

==== Database Tables ====

image_table = MyExpt_Per_Image
object_table = MyExpt_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 =

Also specify the column names that contain X and Y coordinates for each

object within an image.

cell_x_loc = Nuclei_Location_Center_X
cell_y_loc = Nuclei_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_rawDNA,Image_PathName_rawGFP
image_file_cols = Image_FileName_rawDNA,Image_FileName_rawGFP

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

image_thumbnail_cols = Image_Thumbnail_rawDNA,Image_Thumbnail_rawGFP

Give short names for each of the channels (respectively)…

image_names = rawDNA,rawGFP

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

==== 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 = 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

======== 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 =

======== 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

Hi Noga,

Sorry, I forgot, but you’d also have to fill in these two properties file entries as well:

plate_id = <your_plate_id_column> well_id = <your_well_id_column>
with the metadata names that you extracted for these from the Metadata module. These would be likely “Image_Metadata_Plate” and “Image_Metadata_Well” if you named your extracted metadata for plate and well “Plate” and “Well”.

Does that help?
David