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wideP_consurf3

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Tanushree Tunstall 2022-08-10 14:08:40 +01:00
parent 0bcbb44ae5
commit 3af11ec3d3
1 changed files with 193 additions and 192 deletions
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43
scripts/functions/logoP_msa.R
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@ -1,29 +1,29 @@
##################################################################################### #####################################################################################
# LogoPlotMSA(): # LogoPlotMSA():
# Input: # Input:
# Data: # Data:
# msaSeq_mut: MSA chr vector for muts # msaSeq_mut: MSA chr vector for muts
# msaSeq_wt: MSA chr vector for wt # msaSeq_wt: MSA chr vector for wt
# Logo type params: # Logo type params:
# logo_type = c("EDLogo", "bits_pfm", "probability_pfm", "bits_raw", "probability_raw") # logo_type = c("EDLogo", "bits_pfm", "probability_pfm", "bits_raw", "probability_raw")
# EDLogo: calculated from the Logolas package based on PFM matrix (scaled). # EDLogo: calculated from the Logolas package based on PFM matrix (scaled).
#The required content from the package is sourced locally within 'my_logolas.R' #The required content from the package is sourced locally within 'my_logolas.R'
# bits_pfm: Information Content based on PFM scaled matrix (my_logolas.R) # bits_pfm: Information Content based on PFM scaled matrix (my_logolas.R)
# probability_pfm: Probability based on PFM scaled matrix (my_logolas.R) # probability_pfm: Probability based on PFM scaled matrix (my_logolas.R)
# bits_raw: Information Content based on Raw MSA (ggseqlogo) # bits_raw: Information Content based on Raw MSA (ggseqlogo)
# probability_raw: Probability based on Raw MSA (ggseqlogo) # probability_raw: Probability based on Raw MSA (ggseqlogo)
# EDScore_type = c("log", log-odds", "diff", "probKL", "ratio", "unscaled_log", "wKL") # EDScore_type = c("log", log-odds", "diff", "probKL", "ratio", "unscaled_log", "wKL")
# bg_prob: background probability, default is equal i.e NULL. # bg_prob: background probability, default is equal i.e NULL.
# This is used by the internal call to DataED_PFM(). This func takes thse args. I have used it here for # This is used by the internal call to DataED_PFM(). This func takes thse args. I have used it here for
# completeness and allow nuanced plot control # completeness and allow nuanced plot control
# my_logo_col = c("chemistry", "hydrophobicity", "clustalx", "taylor") # my_logo_col = c("chemistry", "hydrophobicity", "clustalx", "taylor")
# --> if clustalx and taylor, set variable to black bg + white font # --> if clustalx and taylor, set variable to black bg + white font
# --> if chemistry and hydrophobicity, then grey bg + black font # --> if chemistry and hydrophobicity, then grey bg + black font
# ...other params # ...other params
# Returns: Logo plots from MSA both mutant and wt (for comparability) # Returns: Logo plots from MSA both mutant and wt (for comparability)
# For my case, I always use it as it helps see what is at the wild-type already! # For my case, I always use it as it helps see what is at the wild-type already!
@ -62,7 +62,7 @@ LogoPlotMSA <- function(unified_msa
, leg_dir = "horizontal" #can be vertical or horizontal , leg_dir = "horizontal" #can be vertical or horizontal
, leg_ts = 16 # leg text size , leg_ts = 16 # leg text size
, leg_tts = 16 # leg title size , leg_tts = 16 # leg title size
) )
{ {
# FIXME: Hack! # FIXME: Hack!
@ -278,7 +278,7 @@ LogoPlotMSA <- function(unified_msa
, "\nQuitting! Resubmit with correct plot_positions") , "\nQuitting! Resubmit with correct plot_positions")
quit() quit()
} }
} }
###################################### ######################################
@ -454,6 +454,7 @@ LogoPlotMSA <- function(unified_msa
, PlotlogolasL[['ed_wt_logoP']] , PlotlogolasL[['ed_wt_logoP']]
, nrow = 2 , nrow = 2
, align = "v" , align = "v"
, axis='lr'
, rel_heights = c(3/4, 1/4)) , rel_heights = c(3/4, 1/4))
return(LogoED_comb) return(LogoED_comb)