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SpectroPipeR - step 1 - read Spectronaut data

a01_SpectroPipeR_read_Spectronaut_data.Rmd

If you want to have a closer look at the images and plots just do a “right-click” on the image and select “open in new tab”

The output folders contained figures that were always exported in two file formats: vector PDF and raster PNG.

Recommendations for MacOS Users

For MacOS users, it is recommended to use the PDF files, for example in Keynote, to achieve better quality.

Recommendations for Windows Users

For Windows users, it is recommended to use the PNG files, as applications like Microsoft PowerPoint can sometimes have issues when working with vector graphics.

Requirements

SpectroPipeR require the Quarto CLI. Please make sure you installed it on your system! https://quarto.org/docs/get-started/

SpectroPipeR also requires certain columns from the Spectronaut output report that are not included by default.

The following steps are advised:

  1. Download and install SpectroPipeR (https://github.com/stemicha/SpectroPipeR)
  2. Load SpectroPipeR and utilize the Spectronaut_export_scheme() function to create the necessary Spectronaut report scheme (*.rs).
  3. Import the generated Spectronaut report scheme.
  4. Conduct an analysis of your raw mass spectrometry data in Spectronaut and produce the output report using the imported report scheme.

Spectronaut output report file

Spectronaut output report should contain the following columns to work in SpectroPipeR:

mandatory Spectronaut columns
R.FileName
R.Condition
R.Replicate
R.Run Date
R.Instrument Name
R.Raw File Name
R.MS1 Mass Analyzer
R.MS2 Mass Analyzer
PG.ProteinGroups
PG.Organisms
PG.IBAQ
PEP.StrippedSequence
EG.ModifiedPeptide
PEP.NrOfMissedCleavages
EG.UserGroup
EG.Qvalue
EG.Cscore
EG.NormalizationFactor
EG.TotalQuantity (Settings)
EG.SignalToNoise
EG.Identified
EG.ApexRT
EG.IntCorrScore
EG.DatapointsPerPeak
EG.DatapointsPerPeak (MS1)
FG.Charge
FG.Id
FG.XICDBID
FG.LabeledSequence
FG.ShapeQualityScore
FG.MS1Quantity
FG.MS2Quantity
FG.MS1RawQuantity
FG.MS2RawQuantity

The Spectronaut_export_scheme() function can be utilized to generate a Spectronaut report template (*.rs). This template should then be imported into Spectronaut. Subsequently, the analysis of your mass spectrometry raw data should be conducted and documented using this template within Spectronaut.

Spectronaut_export_scheme(output_location = "../SpectroPipeR_test_folder")

Parameters

The global SpectroPipeR analysis paramaters is a list element containing the basic analysis project information SpectroPipeR needs to process the data.

parameter description
output_folder character - output folder path (abs.)
ion_q_value_cutoff numeric - Q-value used in Spectronaut analysis: Biognosys default is 0.01 = 1% error rate
id_drop_cutoff numeric - value between 0-1 (1 = 100%); xx percent lower than median of ion ID rate => outlier
normalization_method character - “median” or Spectronaut; auto-detection is per default ON, meaning if normalization was performed in Spectronaut this will be detected and preferred over parameter setting here; median normalization is the fallback option
normalization_factor_cutoff_outlier numeric - median off from global median (4 means abs. 4fold off)
filter_oxidized_peptides logical - if oxidized peptides should be removed from peptide quantification
protein_intensity_estimation character - Hi3 = Hi3 protein intensity estimation, MaxLFQ = MaxLFQ protein intensity estimation
stat_test character - choose statistical test: “rots” = reproducibility optimized test statistics, “modt” = moderate t-test (lmfit, eBayes), “t” = t-test
type_slr character - choose ratio aggregation method: “median” or “tukey” is used when calculating protein values
fold_change numeric - fold-change used as cutoff e.g. 1.5
p_value_cutoff numeric - p-value used as cutoff e.g. 0.05
paired logical - Should paired statistics be applied?

Attention:

If a Q-value of 0.01 is applied in Spectronaut and a Q-value of 0.001 is established within SpectroPipeR, it will impact the ID counting. However, this change will NOT affect the quantification. The Spectronaut report will be used as it is for quantification if no other filtering was selected.

filter_oxidized_peptides parameter

In SpectroPipeR, users can optionally set the filter_oxidized_peptides parameter to TRUE to remove peptides containing oxidized methionines while retaining their unmodified counterparts. This filtering is recommended because experiments using HYE species mixtures have demonstrated that methionine-oxidized peptides exhibit significantly lower intensity and suffer from markedly reduced relative quantitative precision compared to their unmodified forms. Additionally, the variation between replicate measurements is also substantially higher for oxidized peptides. Consequently, removing methionine-oxidized peptides before quantification and statistical analysis is the recommended approach in SpectroPipeR to ensure optimal results.

HYE species mix experiments

The HYE species mix experiments were carried out with 4 replicates as described in A multicenter study benchmarks software tools for label-free proteome quantification. The data can be accessed via MassIVE (MSV000092489) - https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=0f33717d84fd45b1a318ad40670022cc. The species mix is available at ProTec-Diagnostics (https://www.protec-diagnostics.com/en/shop/species-mix-32#attribute_values=3).

Spectronaut version 18.6 was used for data analysis. Following filtering, the ion data only included peptides containing methionine oxidation without prior carbamidomethylation, along with their unmodified counterparts. To determine quantitative ratios, mix A ion intensities were divided by the corresponding ion intensities from mix B.

HYE species mix composition
HYE species mix composition
HYE species mix experiements results regarding methionine oxidation

Methionine oxidation during sample preparation is a significant concern, particularly when using e.g. 2-chloroacetamide (CA) as an alkylating agent. The oxidation of methionine increases dramatically with CA, affecting up to 40% of all Met-containing peptides, compared to only 2-5% with iodoacetamide (IOA) (Hains, P. G. & Robinson, P. J. - 10.1021/acs.jproteome.7b00022). This extensive oxidation can cause qualitative and quantitative issues in large-scale proteomics studies, complicating data analysis and increasing the search space required for database matching.

Methionine oxidation has been also observed to accumulate spuriously during the initial stages of a typical bottom-up proteomics workflow. Notably, the extent of methionine oxidation increases with prolonged trypsin digestion and higher ionization energy during electrospray ionization (ESI) (Zang, L. et al., 2012 - 10.1016/j.jchromb.2012.03.016; Chen, M. & Cook, K. D. - 10.1021/ac061743r).

These observations complicate the differentiation between methionines oxidized in vivo and those artifactually oxidized in vitro during sample preparation and mass spectrometric analysis.

SpectroPipeR includes an option to remove oxidized methionine peptides.

# SpectroPipeR analysis with removing oxidized methionine peptides
SpectroPipeR_analysis <- SpectroPipeR(file = "Spectronaut_SpectroPipeR_report_file.tsv",
                                      parameter = list(output_folder = "output_folder",
                                                       # remove Met-ox. peptides
                                                       filter_oxidized_peptides = TRUE),
                                      condition_comparisons = cbind(c("HYE mix A",
                                                                      "HYE mix B"))
                                      )

This is recommended because species mix experiments have shown that the non-oxidized (methionine containing peptides) fraction of the peptide exhibits a lower coefficient of variation and better conservation of expected ratios compared to the oxidized fraction of the peptides.

HYE species mix results: coefficient of variation plot of ions containing oxidized methionines and their unmodified forms
HYE species mix results: coefficient of variation plot of ions containing oxidized methionines and their unmodified forms
HYE species mix results: histogram of mean ion intensity of ions containing oxidized methionines and their unmodified forms
HYE species mix results: histogram of mean ion intensity of ions containing oxidized methionines and their unmodified forms
HYE species mix results: ratios (A/B) boxplot of ions containing oxidized methionines and their unmodified forms
HYE species mix results: ratios (A/B) boxplot of ions containing oxidized methionines and their unmodified forms

SpectroPipeR - output folder structure

The specified output folder is essential. If it does not exist it will be created. Inside a SpectroPipeR specific folder structure will be generated.

SpectroPipeR output main folder structure
SpectroPipeR output main folder structure

SpectroPipeR - read Spectronaut data module

The read_spectronaut_module() function serves to ingest and process data from the Spectronaut software platform. This function performs the following key tasks:

data loading: - Reads in the raw data from the Spectronaut report - Prepares the data for use within the SpectroPipeR analysis environment

ID analysis: - Provides feedback on key qualitative characteristics of the analysis - Includes metrics such as: - Identification (ID) rates - ON/OFF analysis results - Other relevant qualitative insights

read_spectronaut_module() workflow

  1. check parameters
  2. generate output folders
  3. init log file
  4. load spectronaut report data
  5. check columns
  6. write input data to 01_input_data folder in specified output folder
  7. perform raw file name capping for better visibility inside plots
  8. reformat Q-value (EG.Qvalue = mixed column (character and numeric))
  9. (optional) ID condition filtering; keep only data for downstream analysis which passes condition-wise percentage filtering
  10. generate and export intermediate SDRF file
  11. export short summary of ID rates to log file
  12. performing counting of min. sample percentage where an ion was detected for user feedback in log
  13. count ID rates (ions, peptides, protein groups) by filtering for the specified Q-value in parameters list
  14. get ID rates for 1 or at least 2 peptide hits
  15. determine ion ID median to estimate ID outliers on that basis depending on the parameters list value
  16. generate ID tables and ID plots
  17. The ON/OFF analysis is conducted by filtering the data to include only proteins with a minimum of 2 peptide hits that are present in at least 50% of the sample replicates, with proteins meeting this criteria being classified as “ON” and those failing to do so being designated as “OFF”.
  18. generate ON/OFF analysis tables, plots and UpSet analysis
  19. extract missed cleavages rates globally and on run level

SpectroPipeR condition-wise filtering (optional)

The ID_condition_filtering option, when used in conjunction with ID_condition_filtering_percent, enables users to filter ions that are present in a specified proportion of replicates per condition. This functionality facilitates the exclusion of ions that are, for instance, detected only once within a given condition.

# SpectroPipeR analysis with ions only present in 50% of replicates per condition
SpectroPipeR_analysis <- SpectroPipeR(file = "Spectronaut_SpectroPipeR_report_file.tsv",
                                      parameter = list(output_folder = "output_folder"),
                                      ID_condition_filtering = T,
                                      ID_condition_filtering_percent = 0.5,
                                      condition_comparisons = cbind(c("HYE mix A",
                                                                      "HYE mix B"))
                                      )

The following schematic illustrates the condition-specific filtering process in SpectroPipeR.

SpectroPipeR condition-wise filtering
SpectroPipeR condition-wise filtering

The additional filtering step can potentially enhance the robustness of your data by ensuring that only ions detected across multiple replicates are utilized for quantitative and statistical analysis.

SDRF file

“Public proteomics data often lack essential metadata, limiting its potential. To address this, we present lesSDRF, a tool to simplify the process of metadata annotation, thereby ensuring that data leave a lasting, impactful legacy well beyond its initial publication.” Claeys, T. et al., 2023, Nat. Commun. 14, 6743

SpectroPipeR generates a intermediate SDRF tsv file, please use https://lessdrf.streamlit.app for finalizing file before submitting to a public repository. The “source name” is generated by using the R.Condition and R.Replicate column. Please carefully check column names and edit and refine them according to https://github.com/bigbio/proteomics-sample-metadata/tree/master/sdrf-proteomics.

For further information please read lesSDRF is more: maximizing the value of proteomics data through streamlined metadata annotation

ON/OFF analysis details

  1. determine number of replicates per condition
  2. filtering the ions for the used Q-value cutoff specified in the parameters
  3. filter for ProteinGroups with 2 or more peptides
  4. count ProteinGroups per condition
  5. calculate in how many replicates the ProteinGroup was identified with 2 or more peptides with a Q-value cutoff below the specified one in the parameters
  6. filter the list for ProteinGroups found only in at least 50% of replicates per condition
  7. generate the wide output

example code 

# load SpectroPipeR
library(SpectroPipeR)
# example input file, bundled with SpectroPipeR package
example_file_path <- system.file("extdata", "SN_test_HYE_mix_file.tsv", package="SpectroPipeR")
# use default parameters list
params <- list(output_folder = "../SpectroPipeR_test_folder")
# step 1: load Spectronaut data module
SpectroPipeR_data <- read_spectronaut_module(file = example_file_path,
                                      parameter = params,
                                      print.plot = FALSE)
# checking parameters ...
# #*****************************************
# # READ SPECTRONAUT MODULE
# #*****************************************
# 
# loading data ...
# write input data to output folder ...                                          
# R.FileName capping ...                                                                                    
# 02_ID_rate/8_sample_analysis ATTENTION !!! ---> folder already exists - files will be replaced !!!
# _________ data set loaded with ... _________
# number of raw files = 8
# number of conditions = 2
# number of ions without filtering = 16654
# number of peptides without filtering = 12761
# number of Protein groups without filtering = 1503
# count profiled values ...
# performing counting of min. sample percentage where an ion was detected...
# ion Q-value cutoff < 0.01
# 12.5 % (1/8) is the min. sample percentage where an ion was detected
# performing ID rate filtering ...
# performing protein count over replicates (more than or equal 2 peptides) ...
# performing protein count (<2 and more than or equal 2 peptides) ...
# _________ ON/OFF analysis: _________                                                                
# ... filter with Q-value 0.01 ...
# ... filter for 2 peptides and min. present in 50% of replicates ...
# ... ON/OFF analysis write outputs ...
# _________ ID rate per sample: _________                                                                 
# ions: median = 16205; min. = 15575; max. = 16541
# modified peptides: median = 12850; min. = 12409; max. = 13072
# stripped peptides: median = 12468.5; min. = 12042; max. = 12682
# protein groups: median = 1485.5; min. = 1470; max. = 1499

read_spectronaut_module() outputs

The output in your specified output folder of the read_spectronaut_module() function should look like in this example:

folder structure
folder structure

figures

ID_counts_plot

The bar chart labeled as ID_counts_plot illustrates the number of identifications (IDs) discovered at the ion, peptide, and protein group levels, corresponding to user specified Q-value. It is advisable to select the same Q-value as defined in Spectronaut.

ID counts plot
ID counts plot
ID_counts_plot_ion_filter

The bar chart labeled as ID_counts_plot_ion_filter depicts only the ions counts corresponding to user specified Q-value. The dotted line in the graph represents the median count of ions, while the solid line indicates the threshold criteria for defining an outlier run. If the ‘id_drop_cutoff’ parameter is set to 0.3, this implies that the cutoff criteria for identifying potential ID outliers is determined by the formula median of ion count * (1-0.3). A run with an ion count below this line would be highlighted in orange and labeled as an ID outlier.

ID_ion_counts_plot

The bar chart, titled ID_ion_counts_plot, presents the ion identifications (IDs) in a stacked barchart format. It differentiates between ions discovered below the specified Q-value threshold (depicted in blue), those found above the threshold (shown in grey), and the profiled ions (represented in red).

missed_cleavages_sample_wise

The bar chart, titled ID_ion_counts_plot, depicts the number of missed cleavages per run. The text above the bar indicates the missed cleavage percentage.

missed_cleavages_sample_wise_PERCENTAGE

The bar chart, titled missed_cleavages_sample_wise_PERCENTAGE, shows the number of peptides with x-missed cleavages (y-axis left side) and the percentage (y-axis right side) per run.

missed_cleavages_global

The bar chart, titled missed_cleavages_global, depicts the missed cleavage rate for the whole project.

Detected_ProteinGroups__UpSetR__plot

The UpSet plot (Detected_ProteinGroups__UpSetR__plot) illustrates the outcome from the ON/OFF analysis. The ON/OFF analysis is conducted by filtering the data to include only proteins with a minimum of 2 peptide hits that are present in at least 50% of the sample replicates, with proteins meeting this criteria being classified as “ON” and those failing to do so being designated as “OFF”.

If you want to know about the principle of an UpSet plot visit https://en.wikipedia.org/wiki/UpSet_plot

tables

file_list.csv

The file_list.csv table contains 4 columns and gives a brief overview of the files used in the project

  • R.FileName is the capped raw file name
  • R.FileName_raw is the un-capped version of the raw file name
  • R.Condition is the condition naming which was setup in your Spectronaut analysis
  • R.Replicate is the replicate number which was setup in your Spectronaut analysis
R.FileName R.FileName_raw R.Condition R.Replicate
20230222_Exp…anual_MixA_TR1 20230222_Exp_SM_MethMS_manual_MixA_TR1 A_manual 1
20230222_Exp…anual_MixA_TR2 20230222_Exp_SM_MethMS_manual_MixA_TR2 A_manual 2
20230222_Exp…anual_MixA_TR3 20230222_Exp_SM_MethMS_manual_MixA_TR3 A_manual 3
20230222_Exp…anual_MixA_TR4 20230222_Exp_SM_MethMS_manual_MixA_TR4 A_manual 4
20230222_Exp…anual_MixB_TR1 20230222_Exp_SM_MethMS_manual_MixB_TR1 B_manual 1
20230222_Exp…anual_MixB_TR2 20230222_Exp_SM_MethMS_manual_MixB_TR2 B_manual 2
20230222_Exp…anual_MixB_TR3 20230222_Exp_SM_MethMS_manual_MixB_TR3 B_manual 3
20230222_Exp…anual_MixB_TR4 20230222_Exp_SM_MethMS_manual_MixB_TR4 B_manual 4
ID_counts.csv

The ID_counts.csv table summarizes the ID counts.

  • R.FileName is the capped raw file name
  • R.Condition is the condition naming which was setup in your Spectronaut analysis
  • R.Replicate is the replicate number which was setup in your Spectronaut analysis
  • distinct_ions is the number of distinct ions identified with the user specified Q-value
  • distinct_modified_peptides is the number of distinct peptide sequences incl. modification identified with the user specified Q-value
  • distinct_peptides is the number of distinct stripped peptide sequences identified with the user specified Q-value
  • distinct_proteins is the number of distinct protein groups identified with the user specified Q-value
  • ion_ID_outlier yes / no indication if the run was marked as an ID outlier
R.FileName R.Condition R.Replicate distinct_ions distinct_modified_peptides distinct_peptides distinct_proteins ion_ID_outlier
20230222_Exp…anual_MixA_TR1 A_manual 1 15992 12709 12337 1479 no
20230222_Exp…anual_MixA_TR2 A_manual 2 15775 12531 12159 1473 no
20230222_Exp…anual_MixA_TR3 A_manual 3 15715 12500 12131 1474 no
20230222_Exp…anual_MixA_TR4 A_manual 4 15575 12409 12042 1470 no
20230222_Exp…anual_MixB_TR1 B_manual 1 16418 12991 12600 1494 no
20230222_Exp…anual_MixB_TR2 B_manual 2 16490 13041 12649 1499 no
20230222_Exp…anual_MixB_TR3 B_manual 3 16541 13072 12682 1498 no
20230222_Exp…anual_MixB_TR4 B_manual 4 16479 13050 12660 1492 no
ion_ID_counts_fractions.csv

The ion_ID_counts_fractions.csv table summarizes the ID counts below or above the user specified Q-value and the count of profiled ions.

  • R.FileName is the capped raw file name
  • R.Condition is the condition naming which was setup in your Spectronaut analysis
  • R.Replicate is the replicate number which was setup in your Spectronaut analysis
  • <0.01 count of ions below a Q-value of 0.01
  • >0.01 count of ions above a Q-value of 0.01
  • profiled count of ions that were profiled
R.FileName R.Condition R.Replicate <0.01 >0.01 profiled
20230222_Exp…anual_MixA_TR1 A_manual 1 15992 335 327
20230222_Exp…anual_MixA_TR2 A_manual 2 15775 438 441
20230222_Exp…anual_MixA_TR3 A_manual 3 15715 463 476
20230222_Exp…anual_MixA_TR4 A_manual 4 15575 543 536
20230222_Exp…anual_MixB_TR1 B_manual 1 16418 109 127
20230222_Exp…anual_MixB_TR2 B_manual 2 16490 91 73
20230222_Exp…anual_MixB_TR3 B_manual 3 16541 58 55
20230222_Exp…anual_MixB_TR4 B_manual 4 16479 83 92
missed_cleavages_sample_wise_PERCENTAGE.csv

The missed_cleavages_sample_wise_PERCENTAGE.csv table summarizes the missed cleavages counts per run.

  • R.FileName is the capped raw file name
  • MC_0 number of peptides with 0 missed cleavages
  • MC_1 number of peptides with 1 missed cleavages
  • MC_2 number of peptides with 2 missed cleavages
  • total_peptide_count total number of peptides per run
  • MC_peptide_count peptide count of missed cleavage peptides
  • MC_percentage the percentage of missed cleavages per run
R.FileName MC_0 MC_1 MC_2 total_peptide_count MC_peptide_count MC_percentage
20230222_Exp…anual_MixA_TR1 10485 1756 96 12337 1852 15.01175
20230222_Exp…anual_MixA_TR2 10341 1725 93 12159 1818 14.95189
20230222_Exp…anual_MixA_TR3 10316 1721 94 12131 1815 14.96167
20230222_Exp…anual_MixA_TR4 10243 1707 92 12042 1799 14.93938
20230222_Exp…anual_MixB_TR1 10727 1776 97 12600 1873 14.86508
20230222_Exp…anual_MixB_TR2 10760 1791 98 12649 1889 14.93399
20230222_Exp…anual_MixB_TR3 10785 1800 97 12682 1897 14.95821
20230222_Exp…anual_MixB_TR4 10770 1792 98 12660 1890 14.92891
protein_count__strippedPEP_without_Qvalue_cut.csv

The protein_count__strippedPEP_without_Qvalue_cut.csv table summarizes the protein group count without additional Q-value filtering with at least 2 peptides or below 2 peptides.

  • R.FileName is the capped raw file name
  • R.Condition is the condition naming which was setup in your Spectronaut analysis
  • R.Replicate is the replicate number which was setup in your Spectronaut analysis
  • protein_count protein group count
  • number_of_peptides indicates if it is the fraction below 2 peptides per protein group (<2) or the fraction of protein groups that have at least 2 peptides (>=2)
R.FileName R.Condition R.Replicate protein_count number_of_peptides
20230222_Exp…anual_MixA_TR1 A_manual 1 169 < 2
20230222_Exp…anual_MixA_TR2 A_manual 2 169 < 2
20230222_Exp…anual_MixA_TR3 A_manual 3 169 < 2
20230222_Exp…anual_MixA_TR4 A_manual 4 169 < 2
20230222_Exp…anual_MixB_TR1 B_manual 1 169 < 2
20230222_Exp…anual_MixB_TR2 B_manual 2 169 < 2
20230222_Exp…anual_MixB_TR3 B_manual 3 169 < 2
20230222_Exp…anual_MixB_TR4 B_manual 4 169 < 2
20230222_Exp…anual_MixA_TR1 A_manual 1 1334 >= 2
20230222_Exp…anual_MixA_TR2 A_manual 2 1334 >= 2
20230222_Exp…anual_MixA_TR3 A_manual 3 1334 >= 2
20230222_Exp…anual_MixA_TR4 A_manual 4 1334 >= 2
20230222_Exp…anual_MixB_TR1 B_manual 1 1334 >= 2
20230222_Exp…anual_MixB_TR2 B_manual 2 1334 >= 2
20230222_Exp…anual_MixB_TR3 B_manual 3 1334 >= 2
20230222_Exp…anual_MixB_TR4 B_manual 4 1334 >= 2
protein_count__strippedPEP.csv

The protein_count__strippedPEP_without_Qvalue_cut.csv table summarizes the protein group count with Q-value filtering (e.g. <0.01) with at least 2 peptides or below 2 peptides.

  • R.FileName is the capped raw file name
  • R.Condition is the condition naming which was setup in your Spectronaut analysis
  • R.Replicate is the replicate number which was setup in your Spectronaut analysis
  • protein_count protein group count
  • number_of_peptides indicates if it is the fraction below 2 peptides per protein group (<2) or the fraction of protein groups that have at least 2 peptides (>=2)
  • Qvalue_below user specififed Q-value threshold
R.FileName R.Condition R.Replicate protein_count number_of_peptides Qvalue_below
20230222_Exp…anual_MixA_TR1 A_manual 1 169 < 2 0.01
20230222_Exp…anual_MixA_TR2 A_manual 2 174 < 2 0.01
20230222_Exp…anual_MixA_TR3 A_manual 3 175 < 2 0.01
20230222_Exp…anual_MixA_TR4 A_manual 4 184 < 2 0.01
20230222_Exp…anual_MixB_TR1 B_manual 1 169 < 2 0.01
20230222_Exp…anual_MixB_TR2 B_manual 2 172 < 2 0.01
20230222_Exp…anual_MixB_TR3 B_manual 3 172 < 2 0.01
20230222_Exp…anual_MixB_TR4 B_manual 4 165 < 2 0.01
20230222_Exp…anual_MixA_TR1 A_manual 1 1310 >= 2 0.01
20230222_Exp…anual_MixA_TR2 A_manual 2 1299 >= 2 0.01
20230222_Exp…anual_MixA_TR3 A_manual 3 1299 >= 2 0.01
20230222_Exp…anual_MixA_TR4 A_manual 4 1286 >= 2 0.01
20230222_Exp…anual_MixB_TR1 B_manual 1 1325 >= 2 0.01
20230222_Exp…anual_MixB_TR2 B_manual 2 1327 >= 2 0.01
20230222_Exp…anual_MixB_TR3 B_manual 3 1326 >= 2 0.01
20230222_Exp…anual_MixB_TR4 B_manual 4 1327 >= 2 0.01
Detected_ProteinGroups__stripped_peptide_count_per_condition_raw_data.csv

The Detected_ProteinGroups__stripped_peptide_count_per_condition_raw_data.csv table summarizes the protein group count over replicates.

The table protein_count_over_replicates_min_2_pep_Qvalue_cutoff_0.01.csv is the same table without the peptide_count column.

The table Detected_ProteinGroups__UpSetR__plot__stripped_peptide_count_per_condition.csv summarize the same data in wide-tabular format containing the information about the peptide count per condition.

  • R.Condition is the condition naming which was setup in your Spectronaut analysis
  • R.FileName is the capped raw file name
  • peptide_count number of stripped peptide sequencing after filtering with the user specified Q-value
  • present_replicate_count count in how many replicate the protein group was found present
  • replicate_total_count total replicate count of the condition
  • present_replicate_percentage percentage of in how many replicate the protein group was found present
R.Condition PG.ProteinGroups peptide_count present_replicate_count replicate_total_count present_replicate_percentage
A_manual O75643 93 4 4 100
A_manual O95999 2 1 4 25
A_manual P05719 2 1 4 25
A_manual P08395 3 2 4 50
A_manual P0A6B4 2 1 4 25
A_manual P0A6K1 3 3 4 75
A_manual P0A742 2 1 4 25
A_manual P0A7B1 2 1 4 25
A_manual P0A9G8 3 3 4 75
A_manual P0ACN4 3 3 4 75
A_manual P0ACN7 2 1 4 25
A_manual P0ADR6 3 2 4 50
A_manual P0ADX7 3 3 4 75
A_manual P0AEW4 3 2 4 50
A_manual P0AFX9 3 3 4 75
A_manual P0C0L9 2 1 4 25
A_manual P16703 3 2 4 50
A_manual P19097 73 4 4 100
A_manual P23524 3 2 4 50
A_manual P27240 2 1 4 25
A_manual P27278 4 3 4 75
A_manual P30863 4 3 4 75
A_manual P31680 3 3 4 75
A_manual P33607 3 3 4 75
A_manual P37613 3 3 4 75
A_manual P39371 4 3 4 75
A_manual P52613 2 1 4 25
A_manual P60757 2 1 4 25
A_manual P66948 6 3 4 75
A_manual P75780 2 1 4 25
A_manual P75959 2 1 4 25
A_manual P76113 3 2 4 50
A_manual P76231 3 3 4 75
A_manual Q01082 111 4 4 100
A_manual Q08554 3 2 4 50
A_manual Q5D862 3 2 4 50
B_manual O75643 93 4 4 100
B_manual P19097 73 4 4 100
B_manual P22135 2 1 4 25
B_manual P32477 3 3 4 75
B_manual P33418 2 1 4 25
B_manual Q01082 111 4 4 100
B_manual Q12373 2 1 4 25
Detected_ProteinGroups__stripped_peptide_count_per_condition_raw_data.xlsx

The table Detected_ProteinGroups__stripped_peptide_count_per_condition_raw_data.xlsx holds the binary information about the ON/OFF analysis results.

  • PG.ProteinGroups is protein group ID column
  • conditions… the other columns highlighting in binary format (1 = present / 0 = not present) if a protein group was found present with the user specific criteria or not.
PG.ProteinGroups A_manual B_manual
A0PJW6 1 1
A1X283 1 1
L0R6Q1 1 1
O00330 1 1
O00487 1 1
O00571 1 1
O00622 1 1
O13516 1 1
O14561 1 1
O14579 1 1
O14657 1 1
O14818 1 1
O14893 1 1
O14929 1 1
O14949 1 1
O14981 1 1
O15014 1 1
O15061 1 1
O15091 1 1
O15160 1 1
O15212 1 1
O15355 1 1
O15400 1 1
O15460 1 1
O15504 1 1
O15511 1 1
O15533 1 1
O43172 1 1
O43324 1 1
O43665 1 1

The table protein_count_over_replicates_min_2_pep_percentage_WIDE_Qvalue_cutoff_0.01.csv summarize the data in the same way but here you can find the percentage over replicates in a wide-tabular format.

The table Detected_ProteinGroups__UpSetR__plot__binary_coded.csv contains the binary information of the ON/OFF analysis for the UpSet plot.