Merge pull request #491 from genestack/feature/ODM-12816-add-exQuery-exFilter

Feature/odm 12816 add ex query ex filter

Author: eeliane

openapi/v1/integrationCurator.yaml

@@ -4509,6 +4509,53 @@ paths:
           name: cellQuery
           schema:
             type: string
+        - description: "Search for objects linked to cell expression data and originally\
+            \ uploaded in TSV format via data query, e.g., `feature=ENSG00000230368,ENSG00000188976\
+            \ value>=1.50`\n For the case when the original data is represented by multiple\
+            \ attributes per feature scenarios, extended filtering syntax is as follows:\
+            \ \n1. By feature attribute value for numeric and string attributes: `feature.NAME=1007_s_at`\
+            \ or `feature.\"Average Mz\"=2.218`. As in the case of sample metadata queries,\
+            \ single words can be supplied as is; otherwise, use speech marks (`\"`)\
+            \ to quote queries that include whitespace. \n2. It is possible to specify\
+            \ a set of possible values, separated by comma: `feature.NAME=1007_s_at,121_at`.\
+            \ \n3. Utilize range filters to search numeric attributes. Apply `<` (less\
+            \ than), `>` (greater than), and `=` (equal to) symbols to specify the desired\
+            \ ranges as follows: \n`feature.Name_1 > 3`: Select all rows where the feature\
+            \ attribute Name_1 values are greater than 3. \n`feature.Name_2 >= 6`: Select\
+            \ all rows where the feature attribute Name_2 values are greater than or\
+            \ equal to 6. \n`-3 < feature.Name_3 < 3`: Select all rows where the feature\
+            \ attribute Name_3 values lie within the interval between -3 and 3. \n4.\
+            \ Use substring search to get the records where the attribute field contains\
+            \ the provided substring: `feature.attribute1 =~ \"some text\"`. \n5. The\
+            \ first column for each original data file is identified as feature accession\
+            \ (typically, it contains gene or protein names, accession IDs, etc.). Searching\
+            \ by such feature accession would significantly outperform more complex\
+            \ queries by combining the other feature attributes. To enable such a search,\
+            \ use `feature` without any attribute extension, e.g., `feature=ENSG00000230368,ENSG00000188976`.\n\
+            \ \nThe `value` keyword can be used to select rows where the cell
+            \ measurements has values from a certain range. Examples:\
+            \ `value = 3`, `value > 3`, `-3 < value < 3`.\n
+            \ \nCombine multiple filters for different feature attributes and measurements\
+            \ using `AND` (`&&`), `OR` (`||`), `NOT` (`!`) logical operators and parentheses: \n\
+            * `NOT feature.Name_1=A`: Select all rows where Name_1 is not A. \n* `feature.Name_1!=A,B,C`:\
+            \ Select all rows where Name_1 is not A, B, or C. \n* `feature.Name_1=A\
+            \ AND feature.Name_2=B`: Select all rows where Name_1 is A and Name_2 is\
+            \ B. \n* `feature.Name_1=A && feature.Name_2=B`: Equivalent to the example\
+            \ above. \n* `feature.Name_1=A OR feature.Name_2=B`: Select all rows where\
+            \ Name_1 is A or Name_2 is B. \n* `feature.Name_1=A || feature.Name_2=B`:\
+            \ Equivalent to the example above. \n* `feature.Name_1=A AND (feature.Name_2=B\
+            \ OR value>=1.0)`: Select all rows where Name_1 is A and either Name_2 is\
+            \ B or minimal possible measurement value is 1.0."
+          in: query
+          name: exQuery
+          schema:
+            type: string
+        - description: "Filter by expression metadata (key-value metadata pair(s)).\
+            \ E.g. `\"Genome Version\"=hg19-BROAD`."
+          in: query
+          name: exFilter
+          schema:
+            type: string
         - description: The page tag to resume results from (see paging above).
           in: query
           name: cursor

openapi/v1/integrationUser.yaml

@@ -2956,6 +2956,53 @@ paths:
           name: cellQuery
           schema:
             type: string
+        - description: "Search for objects linked to cell expression data and originally\
+            \ uploaded in TSV format via data query, e.g., `feature=ENSG00000230368,ENSG00000188976\
+            \ value>=1.50`\n For the case when the original data is represented by multiple\
+            \ attributes per feature scenarios, extended filtering syntax is as follows:\
+            \ \n1. By feature attribute value for numeric and string attributes: `feature.NAME=1007_s_at`\
+            \ or `feature.\"Average Mz\"=2.218`. As in the case of sample metadata queries,\
+            \ single words can be supplied as is; otherwise, use speech marks (`\"`)\
+            \ to quote queries that include whitespace. \n2. It is possible to specify\
+            \ a set of possible values, separated by comma: `feature.NAME=1007_s_at,121_at`.\
+            \ \n3. Utilize range filters to search numeric attributes. Apply `<` (less\
+            \ than), `>` (greater than), and `=` (equal to) symbols to specify the desired\
+            \ ranges as follows: \n`feature.Name_1 > 3`: Select all rows where the feature\
+            \ attribute Name_1 values are greater than 3. \n`feature.Name_2 >= 6`: Select\
+            \ all rows where the feature attribute Name_2 values are greater than or\
+            \ equal to 6. \n`-3 < feature.Name_3 < 3`: Select all rows where the feature\
+            \ attribute Name_3 values lie within the interval between -3 and 3. \n4.\
+            \ Use substring search to get the records where the attribute field contains\
+            \ the provided substring: `feature.attribute1 =~ \"some text\"`. \n5. The\
+            \ first column for each original data file is identified as feature accession\
+            \ (typically, it contains gene or protein names, accession IDs, etc.). Searching\
+            \ by such feature accession would significantly outperform more complex\
+            \ queries by combining the other feature attributes. To enable such a search,\
+            \ use `feature` without any attribute extension, e.g., `feature=ENSG00000230368,ENSG00000188976`.\n\
+            \ \nThe `value` keyword can be used to select rows where the cell
+            \ measurements has values from a certain range. Examples:\
+            \ `value = 3`, `value > 3`, `-3 < value < 3`.\n
+            \ \nCombine multiple filters for different feature attributes and measurements\
+            \ using `AND` (`&&`), `OR` (`||`), `NOT` (`!`) logical operators and parentheses: \n\
+            * `NOT feature.Name_1=A`: Select all rows where Name_1 is not A. \n* `feature.Name_1!=A,B,C`:\
+            \ Select all rows where Name_1 is not A, B, or C. \n* `feature.Name_1=A\
+            \ AND feature.Name_2=B`: Select all rows where Name_1 is A and Name_2 is\
+            \ B. \n* `feature.Name_1=A && feature.Name_2=B`: Equivalent to the example\
+            \ above. \n* `feature.Name_1=A OR feature.Name_2=B`: Select all rows where\
+            \ Name_1 is A or Name_2 is B. \n* `feature.Name_1=A || feature.Name_2=B`:\
+            \ Equivalent to the example above. \n* `feature.Name_1=A AND (feature.Name_2=B\
+            \ OR value>=1.0)`: Select all rows where Name_1 is A and either Name_2 is\
+            \ B or minimal possible measurement value is 1.0."
+          in: query
+          name: exQuery
+          schema:
+            type: string
+        - description: "Filter by expression metadata (key-value metadata pair(s)).\
+            \ E.g. `\"Genome Version\"=hg19-BROAD`."
+          in: query
+          name: exFilter
+          schema:
+            type: string
         - description: The page tag to resume results from (see paging above).
           in: query
           name: cursor