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experiment_config_file_BLANK.txt
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experiment_config_file_BLANK.txt
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#############################################################
## Experiment Settings ##
#############################################################
[experiment_settings]
output_folder =
experiment_name =
#############################################################
## Library Settings ##
#############################################################
[library_settings]
#options are CRISPRi_v2_human, CRISPRa_v2_human, CRISPRi_v2_mouse, CRISPRa_v2_mouse, CRISPRi_v1, CRISPRa_v1
library =
#If you are using all sublibraries you don't need to change anything
#Otherwise, uncomment and edit a list from below:
#CRISPRi_v2_human or CRISPRa_v2_human sublibraries
#sublibraries =
# h1_Top5
# h2_Top5
# h3_Top5
# h4_Top5
# h5_Top5
# h6_Top5
# h7_Top5
# h1_Supp5
# h2_Supp5
# h3_Supp5
# h4_Supp5
# h5_Supp5
# h6_Supp5
# h7_Supp5
#CRISPRi_v2_mouse or CRISPRa_v2_mouse sublibraries
#sublibraries =
# m1_Top5
# m2_Top5
# m3_Top5
# m4_Top5
# m5_Top5
# m6_Top5
# m7_Top5
# m1_Supp5
# m2_Supp5
# m3_Supp5
# m4_Supp5
# m5_Supp5
# m6_Supp5
# m7_Supp5
#CRISPRi_v1 sublibraries
#sublibraries =
# Apoptosis+Cancer+Other_Cancer
# Drug_Targets+Kinase_Phosphatase
# Gene_Expression
# Membrane_Proteins
# Stress_Proteostasis
# Trafficking+Mitochondria+Motility
# Unassigned
# Essential_CRISPRi
#CRISPRa_v1 sublibraries
#sublibraries =
# Apoptosis+Cancer+Other_Cancer
# Drug_Targets+Kinase_Phosphatase
# Gene_Expression
# Membrane_Proteins
# Stress_Proteostasis
# Trafficking+Mitochondria+Motility
# Unassigned
#############################################################
## Counts Files ##
#############################################################
[counts_files]
#Enter the paths to counts files from fastqgz_to_counts.py
#followed by :condition|replicate_id
#For example:
#counts_file_string =
# /home/max/counts/Sample1_index6_CRISPRi.counts:treated|Rep1
# /home/max/counts/Sample1_index12_CRISPRi.counts:untreated|Rep1
counts_file_string =
#############################################################
## Filter Settings ##
#############################################################
[filter_settings]
#Do you require greater than or equal to the minimum reads
#for both experiments in a comparison or either experiment?
#Default is either, other option is both
filter_type = either
minimum_reads = 50
#############################################################
## sgRNA Analysis ##
#############################################################
[sgrna_analysis]
#Enter the condition(s) you want to compare
#comparison_name:condition1:condition2
#For example:
#condition_string=
# gamma:T0:untreated
# rho:untreated:treated
# tau:T0:treated
condition_string =
#Different possible treatments for 0 values:
#By default, for any comparison involving a 0, add 1 to both values,
# leaving other values untouched
#Can also add the pseudocount to all values, or filter out any 0s
#'zeros only' or 'all values' or 'filter out'
pseudocount_behavior = zeros only
pseudocount = 1
#############################################################
## Growth Values (phenotype scores only) ##
#############################################################
[growth_values]
#Enter the growth values (population doublings/doubling differences)
#These values are used to normalize log2enrichments;
#default to 1 (un-normalized) if log2e is desired
#comparison_name:replicate_id:value
#For example:
#growth_value_string =
# rho:Rep1:7.13
growth_value_string =
#############################################################
## Gene Analysis ##
#############################################################
[gene_analysis]
#True to combine sgRNAs by transcripts, False to combine by genes
#If this is set to True and calculate_mw is set to True,
#the script will also generate a table of gene scores based on
#the transcript with the best Mann-Whitney p-value
collapse_to_transcripts = True
###Generates a distribution of negative control genes by randomly sampling
#perform same metric calculations on this set
#options are:
#auto (match the pseudogenes with the targeting library table)
#manual (Specify pseudogenes with the below settings)
#off
generate_pseudogene_dist = auto
#sgRNAs/gene for your library
pseudogene_size = 10
#approx number of genes in your library
num_pseudogenes = 16000
###calculate_ave set to True to perform this analysis
#Average of best n sgRNAs; -1 to take average of all
#best is defined as largest phenotype by absolute value
calculate_ave = True
best_n = 3
###Calculate Mann-Whitney p-values
calculate_mw = True
###Calculate Kolmorogov-Smirnov p-values
#calculate_ks = False #NOT IMPLEMENTED
###Score based on nth best sgRNA
calculate_nth = False
nth = 2