Error message: Traceback (most recent call last): File "read_google_sheet.py", line 20, in <module> wks = gc.open_by_key("1jUJOlICOH6VnyuALdU2VAc_Ci_ZVwdW-xc5kr_qkJHY") File "/usr/lib/python2.7/site-packages/gspread/client.py", line 113, in open_by_key raise SpreadsheetNotFound gspread.exceptions.SpreadsheetNotFound Solved : In the json file, there is a "client_email". You need to share your file with this email.
We got error message shown as below: [/home/omicsacademy/miniconda3] >>> PREFIX=/home/omicsacademy/miniconda3 Unpacking payload ... concurrent.futures.process._RemoteTraceback: ''' Traceback (most recent call last): File "concurrent/futures/process.py", line 368, in _queue_management_worker File "multiprocessing/connection.py", line 251, in recv TypeError: __init__() missing 1 required positional argument: 'msg' ''' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "entry_point.py", line 69, in <module> File "concurrent/futures/process.py", line 484, in _chain_from_iterable_of_lists File "concurrent/futures/_base.py", line 611, in result_iterator File "concurrent/futures/_base.py", line 439, in result File "concurrent/futures/_base.py", line 388, in __get_result concurrent.futures.process.BrokenProc...
The q-value is an adjusted p-value, taking in to account the false discovery rate (FDR). Applying a FDR becomes necessary when we're measuring thousands of variables (e.g. gene expression levels) from a small sample set (e.g. a couple of individuals). A p-value of 0.05 implies that we are willing to accept that 5% of all tests will be false positives. An FDR-adjusted p-value (aka a q-value) of 0.05 implies that we are willing to accept that 5% of the tests found to be statistically significant (e.g. by p-value) will be false positives. Such an adjustment is necessary when we're making multiple tests on the same sample. See, for example, http://www.totallab.com/products/samespots/support/faq/pq-values.aspx. -HomeBrew What are p-values? The object of differential 2D expression analysis is to find those spots which show expression difference between groups, thereby signifying that they may be involved in some biological process of interest to the researcher. Due to chance, the...
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