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Merge branch 'embb_dev'

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This commit is contained in:
Tanushree Tunstall 2021-11-12 14:37:10 +00:00
commit c32de1bf0f
7 changed files with 238 additions and 116 deletions
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146
scripts/combining_dfs.py
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@ -116,9 +116,10 @@ if not outdir:
#=======
gene_list_normal = ["pnca", "katg", "rpob", "alr"]
#FIXME: for gid, this should be SRY as this is the drug...please check!!!!
if gene.lower() == "gid":
print("\nReading mCSM file for gene:", gene)
in_filename_mcsm = gene.lower() + '_complex_mcsm_norm_SAM.csv'
in_filename_mcsm = gene.lower() + '_complex_mcsm_norm_SRY.csv' # was incorrectly SAM previously
if gene.lower() == "embb":
print("\nReading mCSM file for gene:", gene)
in_filename_mcsm = gene.lower() + '_complex_mcsm_norm1.csv'
@ -139,7 +140,7 @@ deepddg_df = pd.read_csv(infile_deepddg, sep = ',')
in_filename_dssp = gene.lower() + '_dssp.csv'
infile_dssp = outdir + in_filename_dssp
dssp_df = pd.read_csv(infile_dssp, sep = ',')
dssp_df_raw = pd.read_csv(infile_dssp, sep = ',')
in_filename_kd = gene.lower() + '_kd.csv'
infile_kd = outdir + in_filename_kd
@ -163,10 +164,13 @@ infilename_dynamut2 = gene.lower() + '_dynamut2_norm.csv'
infile_dynamut2 = outdir + 'dynamut_results/dynamut2/' + infilename_dynamut2
dynamut2_df = pd.read_csv(infile_dynamut2, sep = ',')
#------------------------------------------------------------
infilename_mcsm_f_snps = gene.lower() + '_mcsm_formatted_snps.csv'
infile_mcsm_f_snps = outdir + infilename_mcsm_f_snps
mcsm_f_snps = pd.read_csv(infile_mcsm_f_snps, sep = ',', names = ['mutationinformation'], header = None)
#------------------------------------------------------------------------------
# ONLY:for gene pnca and gid: End logic should pick this up!
geneL_dy_na = ["pnca", "gid"]
#if gene.lower() == "pnca" or "gid" :
geneL_dy_na = ['gid']
if gene.lower() in geneL_dy_na :
print("\nGene:", gene.lower()
, "\nReading Dynamut and mCSM_na files")
@ -179,24 +183,27 @@ if gene.lower() in geneL_dy_na :
mcsm_na_df = pd.read_csv(infile_mcsm_na, sep = ',')
# ONLY:for gene embb and alr: End logic should pick this up!
geneL_ppi2 = ["embb", "alr"]
geneL_ppi2 = ['embb', 'alr']
#if gene.lower() == "embb" or "alr":
if gene.lower() in "embb" or "alr":
if gene.lower() in geneL_ppi2:
infilename_mcsm_ppi2 = gene.lower() + '_complex_mcsm_ppi2_norm.csv'
infile_mcsm_ppi2 = outdir + 'mcsm_ppi2/' + infilename_mcsm_ppi2
mcsm_ppi2_df = pd.read_csv(infile_mcsm_ppi2, sep = ',')
#--------------------------------------------------------------
infilename_mcsm_f_snps = gene.lower() + '_mcsm_formatted_snps.csv'
infile_mcsm_f_snps = outdir + infilename_mcsm_f_snps
mcsm_f_snps = pd.read_csv(infile_mcsm_f_snps, sep = ',', names = ['mutationinformation'], header = None)
if gene.lower() == "embb":
sel_chain = "B"
else:
sel_chain = "A"
#------------------------------------------------------------------------------
#=======
# output
#=======
out_filename_comb = gene.lower() + '_all_params.csv'
outfile_comb = outdir + out_filename_comb
print('Output filename:', outfile_comb
print('\nOutput filename:', outfile_comb
, '\n===================================================================')
# end of variable assignment for input and output files
#%%############################################################################
#=====================
@ -230,7 +237,7 @@ len(foldx_df.loc[foldx_df['ddg_foldx'] < 0])
foldx_scale = lambda x : x/abs(foldx_min) if x < 0 else (x/foldx_max if x >= 0 else 'failed')
foldx_df['foldx_scaled'] = foldx_df['ddg_foldx'].apply(foldx_scale)
print('Raw foldx scores:\n', foldx_df['ddg_foldx']
print('\nRaw foldx scores:\n', foldx_df['ddg_foldx']
, '\n---------------------------------------------------------------'
, '\nScaled foldx scores:\n', foldx_df['foldx_scaled'])
@ -273,9 +280,42 @@ else:
#=======================
# Deepddg
# TODO: RERUN 'gid'
#=======================
deepddg_df.shape
#--------------------------
# check if >1 chain
#--------------------------
deepddg_df.loc[:,'chain_id'].value_counts()
if len(deepddg_df.loc[:,'chain_id'].value_counts()) > 1:
print("\nChains detected: >1"
, "\nGene:", gene
, "\nChains:", deepddg_df.loc[:,'chain_id'].value_counts().index)
print('\nSelecting chain:', sel_chain, 'for gene:', gene)
deepddg_df = deepddg_df[deepddg_df['chain_id'] == sel_chain]
#--------------------------
# Check for duplicates
#--------------------------
if len(deepddg_df['mutationinformation'].duplicated().value_counts())> 1:
print("\nFAIL: Duplicates detected in DeepDDG infile"
, "\nNo. of duplicates:"
, deepddg_df['mutationinformation'].duplicated().value_counts()[1]
, "\nformat deepDDG infile before proceeding")
sys.exit()
else:
print("\nPASS: No duplicates detected in DeepDDG infile")
#--------------------------
# Drop chain id col as other targets don't have it.Check for duplicates
#--------------------------
col_to_drop = ['chain_id']
deepddg_df = deepddg_df.drop(col_to_drop, axis = 1)
#-------------------------
# scale Deepddg values
#-------------------------
@ -287,7 +327,7 @@ deepddg_max = deepddg_df['deepddg'].max()
deepddg_scale = lambda x : x/abs(deepddg_min) if x < 0 else (x/deepddg_max if x >= 0 else 'failed')
deepddg_df['deepddg_scaled'] = deepddg_df['deepddg'].apply(deepddg_scale)
print('Raw deepddg scores:\n', deepddg_df['deepddg']
print('\nRaw deepddg scores:\n', deepddg_df['deepddg']
, '\n---------------------------------------------------------------'
, '\nScaled deepddg scores:\n', deepddg_df['deepddg_scaled'])
@ -307,8 +347,8 @@ else:
print('\nFAIL: deepddg values scaled numbers MISmatch'
, '\nExpected number:', deepddg_pos
, '\nGot:', deepddg_pos2
, '\n======================================================')
, '\n======================================================')
#--------------------------
# Deepddg outcome category
#--------------------------
@ -324,48 +364,15 @@ else:
, '\nGot:', doc[0]
, '\n======================================================')
sys.exit()
#--------------------------
# check if >1 chain
#--------------------------
deepddg_df.loc[:,'chain_id'].value_counts()
if len(deepddg_df.loc[:,'chain_id'].value_counts()) > 1:
print("\nChains detected: >1"
, "\nGene:", gene
, "\nChains:", deepddg_df.loc[:,'chain_id'].value_counts().index)
#--------------------------
# subset chain
#--------------------------
if gene.lower() == "embb":
sel_chain = "B"
else:
sel_chain = "A"
deepddg_df = deepddg_df[deepddg_df['chain_id'] == sel_chain]
if deepddg_df['deepddg_scaled'].min() == -1 and deepddg_df['deepddg_scaled'].max() == 1:
print('\nPASS: Deepddg data is scaled between -1 and 1',
'\nproceeding with merge')
#--------------------------
# Check for duplicates
#--------------------------
if len(deepddg_df['mutationinformation'].duplicated().value_counts())> 1:
print("\nFAIL: Duplicates detected in DeepDDG infile"
, "\nNo. of duplicates:"
, deepddg_df['mutationinformation'].duplicated().value_counts()[1]
, "\nformat deepDDG infile before proceeding")
sys.exit()
else:
print("\nPASS: No duplicates detected in DeepDDG infile")
#--------------------------
# Drop chain id col as other targets don't have itCheck for duplicates
#--------------------------
col_to_drop = ['chain_id']
deepddg_df = deepddg_df.drop(col_to_drop, axis = 1)
#%%=============================================================================
#%%============================================================================
# Now merges begin
#%%=============================================================================
print('==================================='
, '\nFirst merge: mcsm + foldx'
, '\n===================================')
@ -395,10 +402,11 @@ print('\n\nResult of first merge:', mcsm_foldx_dfs.shape
mcsm_foldx_dfs[merging_cols_m1].apply(len)
mcsm_foldx_dfs[merging_cols_m1].apply(len) == len(mcsm_foldx_dfs)
#%% for embB and any other targets where mCSM-lig hasn't run for
# get the empty cells to be full of meaningful info
#%% for embB and any other targets where mCSM-lig hasn't run for ALL nsSNPs.
# Get the empty cells to be full of meaningful info
if mcsm_foldx_dfs.loc[:,'wild_type': 'mut_aa_3lower'].isnull().values.any():
print ("NAs detected in mcsm cols after merge")
print ('\nNAs detected in mcsm cols after merge.'
, '\nCleaning data before merging deepddg_df')
##############################
# Extract relevant col values
@ -442,6 +450,8 @@ if mcsm_foldx_dfs.loc[:,'wild_type': 'mut_aa_3lower'].isnull().values.any():
mcsm_foldx_dfs['wt_aa_3lower'] = wt.map(lookup_dict)
mut = mcsm_foldx_dfs['mutant_type'].squeeze()
mcsm_foldx_dfs['mut_aa_3lower'] = mut.map(lookup_dict)
else:
print('\nNo NAs detected in mcsm_fold_dfs. Proceeding to merge deepddg_df')
#%%
print('==================================='
@ -460,14 +470,18 @@ ncols_deepddg_merge = len(mcsm_foldx_deepddg_dfs.columns)
mcsm_foldx_deepddg_dfs['position'] = mcsm_foldx_deepddg_dfs['position'].astype('int64')
#%%============================================================================
#FIXME: select df with 'chain' to allow corret dim merging!
print('==================================='
, '\Third merge: dssp + kd'
, '\nThird merge: dssp + kd'
, '\n===================================')
dssp_df.shape
dssp_df_raw.shape
kd_df.shape
rd_df.shape
dssp_df = dssp_df_raw[dssp_df_raw['chain_id'] == sel_chain]
dssp_df['chain_id'].value_counts()
#dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = "outer")
merging_cols_m2 = detect_common_cols(dssp_df, kd_df)
dssp_kd_dfs = pd.merge(dssp_df
@ -553,17 +567,19 @@ combined_df['wild_type'].equals(combined_df['wild_type_dssp'])
foo = combined_df[['wild_type', 'wild_type_kd', 'wt_3letter_caps', 'wt_aa_3lower', 'mut_aa_3lower']]
# Drop cols
cols_to_drop = ['chain_id', 'wild_type_kd', 'wild_type_dssp', 'wt_3letter_caps' ]
cols_to_drop = ['chain_id', 'wild_type_kd', 'wild_type_dssp', 'wt_3letter_caps']
combined_df_clean = combined_df.drop(cols_to_drop, axis = 1)
del(foo)
#%%============================================================================
# Output columns
out_filename_stab_struc = gene.lower() + '_comb_stab_struc_params.csv'
outfile_stab_struc = outdir + '/' + out_filename_stab_struc
outfile_stab_struc = outdir + out_filename_stab_struc
print('Output filename:', outfile_stab_struc
, '\n===================================================================')
combined_df_clean
# write csv
print('\nWriting file: combined stability and structural parameters')
combined_df_clean.to_csv(outfile_stab_struc, index = False)
@ -642,7 +658,7 @@ else:
#%%============================================================================
# Output columns: when dynamut, dynamut2 and others weren't being combined
out_filename_comb_afor = gene.lower() + '_comb_afor.csv'
outfile_comb_afor = outdir + '/' + out_filename_comb_afor
outfile_comb_afor = outdir + out_filename_comb_afor
print('Output filename:', outfile_comb_afor
, '\n===================================================================')
@ -657,7 +673,7 @@ print('\nFinished writing file:'
#dfs_list = [dynamut_df, dynamut2_df, mcsm_na_df] # gid
if gene.lower() == "pnca":
dfs_list = [dynamut_df, dynamut2_df]
dfs_list = [dynamut2_df]
if gene.lower() == "gid":
dfs_list = [dynamut_df, dynamut2_df, mcsm_na_df]
if gene.lower() == "embb":
@ -709,4 +725,4 @@ combined_all_params.to_csv(outfile_comb, index = False)
print('\nFinished writing file:'
, '\nNo. of rows:', combined_all_params.shape[0]
, '\nNo. of cols:', combined_all_params.shape[1])
#%% end of script
#%% end of script

112
scripts/plotting/TESTING_PLOTS.R Executable file
View file

@ -0,0 +1,112 @@
#!/usr/bin/env Rscript
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
getwd()
source("Header_TT.R")
drug = 'streptomycin'
gene = 'gid'
spec = matrix(c(
"drug" , "d", 1, "character",
"gene" , "g", 1, "character",
"data_file1" , "fa", 2, "character",
"data_file2" , "fb", 2, "character"
), byrow = TRUE, ncol = 4)
opt = getopt(spec)
drug = opt$drug
gene = opt$gene
infile_params = opt$data_file1
infile_metadata = opt$data_file2
if(is.null(drug)|is.null(gene)) {
stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
}
#===========
# Input
#===========
source("get_plotting_dfs.R")
#===========
# output
#===========
# PS
bp_subcols_duet = "barplot_coloured_PS.svg"
plot_bp_subcols_duet = paste0(plotdir, "/", bp_subcols_duet)
##############################################################################
# add frequency of positions (from lib data.table)
setDT(subcols_df_ps)[, pos_count := .N, by = .(position)]
foo = data.frame(subcols_df_ps$mutationinformation
, subcols_df_ps$position
, subcols_df_ps$pos_count)
#snpsBYpos_df <- subcols_df_ps %>%
# group_by(position) %>%
# summarize(snpsBYpos = mean(pos_count))
#********************
# generate plot: PS
# NO axis colours
#********************
g = ggplot(subcols_df_ps
, aes(x = factor(position, ordered = T)))
g2 = g + geom_bar()
g2
foo = g2 + geom_text(stat='count', aes(label = ..count..))
foo
######
bp_subcols_duet = "TEST_PS.svg"
plot_bp_subcols_duet = paste0(plotdir, "/", bp_subcols_duet)
print(paste0("plot name:", plot_bp_subcols_duet))
svg(plot_bp_subcols_duet, width = 26, height = 4)
g1 = ggplot(subcols_df_ps, aes(x = factor(position, ordered = T)
, y = pos_count)) +
geom_bar(stat = "summary"
, aes(fill = group), colour = "grey") +
###################################
g = ggplot(subcols_df_ps
, aes(x = factor(position, ordered = T)))
outPlot_bp_ps = g +
geom_bar(aes(fill = group), colour = "grey") +
scale_fill_manual( values = subcols_ps
, guide = "none") +
theme( axis.text.x = element_text(size = my_xaxls
, angle = 90
, hjust = 1
, vjust = 0.4)
, axis.text.y = element_text(size = my_yaxls
, angle = 0
, hjust = 1
, vjust = 0)
, axis.title.x = element_text(size = my_xaxts)
, axis.title.y = element_text(size = my_yaxts ) ) +
labs(title = ""
#title = my_title
, x = "Position"
, y = "Frequency")
print(outPlot_bp_ps)
#dev.off()
######################################################################=
# End of script
######################################################################=