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`summarise` replaced with more suitable tidy functions

non-essential columns removed from requirements
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ec363 committed Nov 21, 2024
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137 changes: 93 additions & 44 deletions R/generate_cfs.R
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Original file line number Diff line number Diff line change
Expand Up @@ -504,42 +504,91 @@ generate_cfs <- function(calibration_csv,

# 4. Summarise -------------------------------------------------------

# calculate mean of n replicates, for raw_values and normalised_values columns, for non_sat_values and all_values dfs
# a. summ_values_nonsat uses non_sat_values and therefore does not include saturated values.
# v1 ###
# # calculate mean of n replicates, for raw_values and normalised_values columns, for non_sat_values and all_values dfs
# # a. summ_values_nonsat uses non_sat_values and therefore does not include saturated values.
# summ_values_nonsat <- non_sat_values %>%
# dplyr::group_by(.data$instrument, .data$plate, .data$seal,
# .data$channel_name, .data$channel_ex, .data$channel_em,
# .data$media, .data$calibrant, .data$protein,
#
# .data$mw_gmol1, .data$concentration_ngul, .data$dilution, .data$rev_dilution, .data$volume,
# .data$mass_ng, .data$moles, .data$molecules,
# .data$dilution_ratio, .data$max_concentration,
#
# # Putting these in order for skimming by eye. Group by Gain first, then conc second.
# .data$measure, .data$dilution_idx, # dilution_idx will vary
# # replicate, well, row, column will vary
# .data$raw_blanks, .data$norm_blanks, .data$norm_blanks_sd,
# .drop = FALSE) %>%
# dplyr::summarise(dplyr::across(dplyr::ends_with("_value"), ~mean(.x, na.rm = TRUE)))
# summ_values_nonsat
#
# # b. summ_values_all uses all_values and therefore includes both sat/non_sat points
# summ_values_all <- all_values %>%
# dplyr::group_by(.data$instrument, .data$plate, .data$seal,
# .data$channel_name, .data$channel_ex, .data$channel_em,
# .data$media, .data$calibrant, .data$protein,
#
# .data$mw_gmol1, .data$concentration_ngul, .data$dilution, .data$rev_dilution, .data$volume,
# .data$mass_ng, .data$moles, .data$molecules,
# .data$dilution_ratio, .data$max_concentration,
#
# # Putting these in order for skimming by eye. Group by Gain first, then conc second.
# .data$measure, .data$dilution_idx, # dilution_idx will vary
# # .data$molecules, # molecules specified by molecules_var
# # replicate, well, row, column will vary
# .data$raw_blanks, .data$norm_blanks, .data$norm_blanks_sd,
# .drop = FALSE) %>%
# dplyr::summarise(dplyr::across(dplyr::ends_with("_value"), ~mean(.x, na.rm = TRUE)))
# summ_values_all

# v2
# # calculate mean of n replicates, for raw_values and normalised_values columns, for non_sat_values and all_values dfs
# # a. summ_values_nonsat uses non_sat_values and therefore does not include saturated values.
summ_values_nonsat <- non_sat_values %>%
dplyr::group_by(.data$instrument, .data$plate, .data$seal,
.data$channel_name, .data$channel_ex, .data$channel_em,
.data$media, .data$calibrant, .data$protein,

.data$mw_gmol1, .data$concentration_ngul, .data$dilution, .data$rev_dilution, .data$volume,
.data$mass_ng, .data$moles, .data$molecules,
.data$dilution_ratio, .data$max_concentration,

# Putting these in order for skimming by eye. Group by Gain first, then conc second.
.data$measure, .data$dilution_idx, # dilution_idx will vary
# replicate, well, row, column will vary
.data$raw_blanks, .data$norm_blanks, .data$norm_blanks_sd,
.drop = FALSE) %>%
dplyr::summarise(dplyr::across(dplyr::ends_with("_value"), ~mean(.x, na.rm = TRUE)))

# For each dilution and wavelength
dplyr::group_by(.data$concentration_ngul, .data$molecules, .data$dilution_idx,
.data$measure,
.drop = FALSE) %>% # don't remove groups w no values

# Take mean of raw and normalised values
dplyr::mutate(raw_value = mean(.data$raw_value, na.rm = TRUE)) %>%
dplyr::mutate(normalised_value = mean(.data$normalised_value, na.rm = TRUE)) %>%
dplyr::ungroup() %>%

# Tidy
dplyr::select(-c(.data$replicate, .data$well, .data$row, .data$column
# .data$kfactor_1cm, .data$kfactor_well,
# .data$pathlength_each, .data$pathlength_blanks, .data$pathlength_volume, .data$pathlength,
# .data$raw_cm1_blanks
)) %>%
dplyr::distinct() # remove duplicate rows
# dplyr::arrange(dplyr::desc(.data$dilution_idx)) # arrange by dilution, starting from 1
summ_values_nonsat

# b. summ_values_all uses all_values and therefore includes both sat/non_sat points
summ_values_all <- all_values %>%
dplyr::group_by(.data$instrument, .data$plate, .data$seal,
.data$channel_name, .data$channel_ex, .data$channel_em,
.data$media, .data$calibrant, .data$protein,

.data$mw_gmol1, .data$concentration_ngul, .data$dilution, .data$rev_dilution, .data$volume,
.data$mass_ng, .data$moles, .data$molecules,
.data$dilution_ratio, .data$max_concentration,

# Putting these in order for skimming by eye. Group by Gain first, then conc second.
.data$measure, .data$dilution_idx, # dilution_idx will vary
# .data$molecules, # molecules specified by molecules_var
# replicate, well, row, column will vary
.data$raw_blanks, .data$norm_blanks, .data$norm_blanks_sd,
.drop = FALSE) %>%
dplyr::summarise(dplyr::across(dplyr::ends_with("_value"), ~mean(.x, na.rm = TRUE)))

# For each dilution and wavelength
dplyr::group_by(.data$concentration_ngul, .data$molecules, .data$dilution_idx,
.data$measure,
.drop = FALSE) %>% # don't remove groups w no values

# Take mean of raw and normalised values
dplyr::mutate(raw_value = mean(.data$raw_value, na.rm = TRUE)) %>%
dplyr::mutate(normalised_value = mean(.data$normalised_value, na.rm = TRUE)) %>%
dplyr::ungroup() %>%

# Tidy
dplyr::select(-c(.data$replicate, .data$well, .data$row, .data$column
# .data$kfactor_1cm, .data$kfactor_well,
# .data$pathlength_each, .data$pathlength_blanks, .data$pathlength_volume, .data$pathlength,
# .data$raw_cm1_blanks
)) %>%
dplyr::distinct() # remove duplicate rows
# dplyr::arrange(dplyr::desc(.data$dilution_idx)) # arrange by dilution, starting from 1
summ_values_all

# 5. Find CFS fits -------------------------------------------------------
Expand Down Expand Up @@ -685,26 +734,25 @@ generate_cfs <- function(calibration_csv,

media = temp_meas_calib_values$media[1],
calibrant = calib, # calib is the current element in calibrant for loop
protein = temp_meas_calib_values$protein[1],
# protein = temp_meas_calib_values$protein[1], # redundant ###

mw_gmol1 = temp_meas_calib_values$mw_gmol1[1],
# mw_gmol1 = temp_meas_calib_values$mw_gmol1[1], # not essential
# conc, diln, rev_dilution will vary
volume = temp_meas_calib_values$volume[1],
# volume = temp_meas_calib_values$volume[1], # not essential
# mass, moles, molecules will vary

measure = meas, # meas is the current element in measure (eg GFP040) for loop

# diln ratio, maxconc, not relevant
# dilnidx will vary

raw_blanks = temp_meas_calib_values$raw_blanks[1],
norm_blanks = temp_meas_calib_values$norm_blanks[1],
norm_blanks_sd = temp_meas_calib_values$norm_blanks_sd[1],
# raw_blanks = temp_meas_calib_values$raw_blanks[1], # not needed
# norm_blanks = temp_meas_calib_values$norm_blanks[1], # not needed
# norm_blanks_sd = temp_meas_calib_values$norm_blanks_sd[1], # not needed
# raw_value, normalised_value will vary
cf = res$par[1], beta = res$par[2],
residuals = residual
)

fit_values <- rbind(fit_values, new_fit)
} # res$convergence

Expand Down Expand Up @@ -793,21 +841,22 @@ generate_cfs <- function(calibration_csv,

media = temp_trimmed_values$media[1],
calibrant = temp_trimmed_values$calibrant[2], # [2] might help if a BSA/none gets in there
protein = temp_trimmed_values$protein[3], # 1 gets you "none"
# protein = temp_trimmed_values$protein[3], # 1 gets you "none" # redundant ###

mw_gmol1 = temp_trimmed_values$mw_gmol1[3], # row1 is blank, row2+ is protein, row3+ needed if a BSA left in
# mw_gmol1 = temp_trimmed_values$mw_gmol1[3], # not essential
# row1 is blank, row2+ is protein, row3+ needed if a BSA left in
# conc, diln, revdiln will vary
volume = temp_trimmed_values$volume[1],
# volume = temp_trimmed_values$volume[1], # not essential
# mass, moles, molecules will vary
# diln ratio, max conc not relevant
# dilnidx will vary

measure = meas, # meas is the current element in measure (eg GFP040) for loop
gain = temp_trimmed_values$gain[1],

raw_blanks = temp_trimmed_values$raw_blanks[2], # needed if BSA gets left in
norm_blanks = temp_trimmed_values$norm_blanks[1],
norm_blanks_sd = temp_trimmed_values$norm_blanks_sd[2], # needed if BSA gets left in
# raw_blanks = temp_trimmed_values$raw_blanks[2], # not essential # needed if BSA gets left in
# norm_blanks = temp_trimmed_values$norm_blanks[1], # not essential
# norm_blanks_sd = temp_trimmed_values$norm_blanks_sd[2], # needed if BSA gets left in # not essential
# raw_value, norm_value will vary

# new columns:
Expand Down
74 changes: 50 additions & 24 deletions R/get_conc_ECmax.R
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Original file line number Diff line number Diff line change
Expand Up @@ -139,32 +139,58 @@ get_conc_ecmax <- function(protein_slug, protein_seq,
# 2. Averaged data -------------------------------------------------

# Take mean of duplicate readings for: raw_value, normalised_value, normalised_cm1_value

# v1 ###
# names(spectrum_data_subset)
# summ_data <- spectrum_data_subset %>%
# dplyr::group_by(.data$instrument, .data$plate, .data$seal,
# # .data$channel_name, .data$channel_ex, .data$channel_em, # removed
# .data$media, .data$calibrant, .data$protein,
# # replicate will vary
#
# # .data$mw_gmol1, .data$concentration_ngul,
# .data$dilution,
# # .data$rev_dilution,
#
# # well, row, column will vary
# .data$measure,
#
# ## raw_value # TAKING MEAN
# .data$kfactor_1cm,
# # .data$kfactor_well, .data$pathlength_each, # will vary
# # .data$pathlength_blanks, # remove as removing pathlength_each
# .data$volume,
# # .data$pathlength_volume, # remove as removing pathlength_each
# .data$pathlength_method,
# # .data$pathlength, # might vary
# ## raw_cm1_value, # TAKING MEAN
# .data$raw_cm1_blanks,
# ## normalised_cm1_value # TAKING MEAN
#
# .drop = FALSE) %>%
# dplyr::summarise(dplyr::across(dplyr::ends_with("_value"), ~mean(.x, na.rm = TRUE)))
# names(summ_data)
# summ_data # half the rows bc going from duplicates to averaged rows

# v2
names(spectrum_data_subset)
summ_data <- spectrum_data_subset %>%
dplyr::group_by(.data$instrument, .data$plate, .data$seal,
# .data$channel_name, .data$channel_ex, .data$channel_em, # removed
.data$media, .data$calibrant, .data$protein,
# replicate will vary

.data$mw_gmol1, .data$concentration_ngul, .data$dilution, .data$rev_dilution,

# well, row, column will vary
.data$measure,

## raw_value # TAKING MEAN
.data$kfactor_1cm,
# .data$kfactor_well, .data$pathlength_each, # will vary
# .data$pathlength_blanks, # remove as removing pathlength_each
.data$volume,
# .data$pathlength_volume, # remove as removing pathlength_each
.data$pathlength_method,
# .data$pathlength, # might vary
## raw_cm1_value, # TAKING MEAN
.data$raw_cm1_blanks,
## normalised_cm1_value # TAKING MEAN

.drop = FALSE) %>%
dplyr::summarise(dplyr::across(dplyr::ends_with("_value"), ~mean(.x, na.rm = TRUE)))

# For each dilution and wavelength
dplyr::group_by(.data$dilution, .data$measure,
.drop = FALSE) %>% # don't remove groups w no values

# Take mean of raw and normalised values
dplyr::mutate(raw_value = mean(.data$raw_value, na.rm = TRUE)) %>%
dplyr::mutate(raw_cm1_value = mean(.data$raw_cm1_value, na.rm = TRUE)) %>%
dplyr::mutate(normalised_cm1_value = mean(.data$normalised_cm1_value, na.rm = TRUE)) %>%
dplyr::ungroup() %>%

# Tidy
dplyr::select(-c(.data$replicate, .data$well, .data$row, .data$column,
.data$kfactor_well, .data$pathlength_each)) %>%
dplyr::distinct() # remove duplicate rows

names(summ_data)
summ_data # half the rows bc going from duplicates to averaged rows

Expand Down
63 changes: 43 additions & 20 deletions R/process_absorbance_spectrum.R
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Original file line number Diff line number Diff line change
Expand Up @@ -496,27 +496,50 @@ process_absorbance_spectrum <- function(spectrum_csv,
# 4. Summarise (take means) -------------------------------------------------------

# Take mean of duplicate readings for: raw_value, normalised_value, normalised_cm1_value
names(norm_values)

# # v1
# names(norm_values)
# summ_values <- norm_values %>%
# dplyr::group_by(.data$instrument, .data$plate, .data$seal,
# # .data$channel_name, .data$channel_ex, .data$channel_em, # removed
# .data$media, .data$calibrant, .data$protein,
# # replicate will vary
# .data$mw_gmol1, .data$concentration_ngul, .data$dilution, .data$rev_dilution, .data$volume,
# # well, row, column will vary
# .data$measure,
# ## raw_value # TAKING MEAN
# .data$kfactor_1cm,
# # .data$kfactor_well, .data$pathlength_each, # will vary
# # .data$pathlength_blanks, # remove as removing pathlength_each
# # .data$pathlength_volume, # remove as removing pathlength_each
# .data$pathlength_method,
# # .data$pathlength, # might vary
# ## raw_cm1_value, # TAKING MEAN
# .data$raw_cm1_blanks,
# ## normalised_cm1_value # TAKING MEAN
# .drop = FALSE) %>%
# dplyr::summarise(dplyr::across(dplyr::ends_with("_value"), ~mean(.x, na.rm = TRUE)))
# summ_values

# v2
summ_values <- norm_values %>%
dplyr::group_by(.data$instrument, .data$plate, .data$seal,
# .data$channel_name, .data$channel_ex, .data$channel_em, # removed
.data$media, .data$calibrant, .data$protein,
# replicate will vary
.data$mw_gmol1, .data$concentration_ngul, .data$dilution, .data$rev_dilution, .data$volume,
# well, row, column will vary
.data$measure,
## raw_value # TAKING MEAN
.data$kfactor_1cm,
# .data$kfactor_well, .data$pathlength_each, # will vary
# .data$pathlength_blanks, # remove as removing pathlength_each
# .data$pathlength_volume, # remove as removing pathlength_each
.data$pathlength_method,
# .data$pathlength, # might vary
## raw_cm1_value, # TAKING MEAN
.data$raw_cm1_blanks,
## normalised_cm1_value # TAKING MEAN
.drop = FALSE) %>%
dplyr::summarise(dplyr::across(dplyr::ends_with("_value"), ~mean(.x, na.rm = TRUE)))

# For each dilution and wavelength
dplyr::group_by(.data$dilution, .data$measure,
.drop = FALSE) %>% # don't remove groups w no values
# Take mean of raw and normalised (cm-1) values
dplyr::mutate(raw_value = mean(.data$raw_value, na.rm = TRUE)) %>%
dplyr::mutate(raw_cm1_value = mean(.data$raw_cm1_value, na.rm = TRUE)) %>%
dplyr::mutate(normalised_cm1_value = mean(.data$normalised_cm1_value, na.rm = TRUE)) %>%
dplyr::ungroup() %>%

# Tidy
dplyr::select(-c(.data$replicate, .data$well, .data$row, .data$column,
.data$kfactor_1cm, .data$kfactor_well,
.data$pathlength_each, .data$pathlength_blanks, .data$pathlength_volume, .data$pathlength,
.data$raw_cm1_blanks)) %>%
dplyr::distinct() %>% # remove duplicate rows
dplyr::arrange(dplyr::desc(.data$dilution)) # arrange by dilution, starting from 1
summ_values

# Normalised cm_1 250nm+
Expand Down

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