knitr::opts_chunk$set(echo = TRUE)
This plate contains the same samples as 0000 1204 9003 (1047T) but was in addition stained with Hoechst dye 12h after seeding in 384 well plates. The cell were stained and imaged 30 minutes afterwards. More information can be found on the Tracer sheet.
The plate was imaged using an additional DAPI channel. The DAPI channel has the number:
ch1 = DPC ch2 = BF ch3 = Hoechst ch4 = CE ch5 = TMRM
library(tidyverse) library(dcphelper)
new_path_base = "/home/ubuntu/bucket/metadata/000012077803__2019-01-29T12_50_08-Measurement_2/" new_json_path_flat = "/home/ubuntu/bucket/metadata/job_flatfield_template.json" new_json_path_max = "/home/ubuntu/bucket/metadata/job_maxproj_template.json"
dir.create(new_path_base) # Do not execute this from a local machine if you expect other AWS services to access the directory later on
channel_v <- c("ch2", "ch3", "ch4", "ch5") channel_n <- c("bf", "dp", "ce", "tm") #channel_v <- c("ch1") #channel_n <- c("pc")
for(i in 1:length(channel_n)){ metadata_split_path <- create_flatfield_metadata_split( path = "/home/ubuntu/bucket/inbox/000012077803__2019-01-29T12_50_08-Measurement_2/Images/", channel_of_interest = channel_v[i], #brightfield name = channel_n[i], json_path = new_json_path, #not needed path_base = new_path_base, force = FALSE) }
```{python, eval = TRUE} python ~/bucket/metadata/ManualMetadata_dir.py /home/ubuntu/bucket/metadata/000012077803__2019-01-29T12_50_08-Measurement_2/ "['Metadata_parent','Metadata_timepoint', 'Metadata_well', 'Metadata_fld', 'Metadata_channel']" "bf"
python /home/ubuntu/bucket/metadata/ManualMetadata_dir.py ~/bucket/metadata/000012077803__2019-01-29T12_50_08-Measurement_2/ "['Metadata_parent','Metadata_timepoint', 'Metadata_well', 'Metadata_fld', 'Metadata_channel']" "ce"
python /home/ubuntu/bucket/metadata/ManualMetadata_dir.py ~/bucket/metadata/000012077803__2019-01-29T12_50_08-Measurement_2/ "['Metadata_parent','Metadata_timepoint', 'Metadata_well', 'Metadata_fld', 'Metadata_channel']" "tm"
python /home/ubuntu/bucket/metadata/ManualMetadata_dir.py ~/bucket/metadata/000012077803__2019-01-29T12_50_08-Measurement_2/ "['Metadata_parent','Metadata_timepoint', 'Metadata_well', 'Metadata_fld', 'Metadata_channel']" "dp"
## Writing job files ```r link_json_metadata(metadata_split_path = list.files(new_path_base, pattern = "metadata_", full.names = TRUE) %>% stringr::str_subset(pattern = ".csv") %>% stringr::str_subset(pattern = "bf"), json_path = new_json_path_flat, path_base = new_path_base) channel_n_mod <- channel_n[2:4] for(i in 1:length(channel_n_mod)){ link_json_metadata(metadata_split_path = list.files(new_path_base, pattern = "metadata_", full.names = TRUE) %>% stringr::str_subset(pattern = ".csv") %>% stringr::str_subset(pattern = channel_n_mod[i]), json_path = new_json_path_max, path_base = new_path_base) }
for(i in 1:length(channel_n)){ group_jobs_bash(path_base = new_path_base, name = channel_n[i], letter_row_interval = c(1:16), number_col_interval = c(1:24)) }
Something went wrong when running jobs - I think it is related to the metadata files/ DAPI images.
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