In cekehe/rappp: QC, data transformation and visualization for PAPP

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library(rappp)

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Affinity proteomics

Protein profiling and Autoimmunity profiling

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Affinity proteomics

Protein profiling and Autoimmunity profiling

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Method introduction & reason for package

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Project workflow

• New Package template was created locally on Cecilia's computer.
• New repo was created on Cecilia's GitHub.

• The local and GitHub repos were connected.

• Forking and cloning by August and Sofia.

• August and Sofia made collaborators, to allow them to accept pull requests themselves.

• Incorprate separate funcitons for different parts of our data analysis workflow.

• Sofia: Bead count visualization
• Cecilia: Data transformation
• August: Data visualization

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Bead count plot

bead_count_base(df_bead_count)

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bead_count_ggplot(df_bead_count)

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#> system.time(bead_count_base(df_bead_count))
#   user  system elapsed 
# 16.030   0.140  16.255 
#
#
#> system.time(bead_count_ggplot(df_bead_count))
#   user  system elapsed 
#  7.012   0.171   7.233

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Bead count plot with ggplot

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Data transformation

Basic data transformation workflow:

Currently done differently by different people and often with copy-paste.

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Data transformation

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Data transformation

normalized <- ap_norm(MFI_data)

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# > system.time(ap_norm(MFI_data))
#   user  system elapsed 
#   0.11    0.00    0.11 

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Data visualization

beeswarm_ggplot(
  data = SBA_data,
  filename = "filename",
  sample_info = sampleinfo,
  bead_info = beadinfo,
  grouping_var = "diagnosis",
  sample_name_var = "sample_name",
  xlab = c("BIP/MAN", "DEL", "DEP", "SCA", "SCZ", "NON-PSY")
)

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Data visualization

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Conclusions and future work

• Established our first package!

• Collaborated using github

• Started to use tidyverse functions
  

This will enable us to: - More standardized workflow within group

- Continue building with our new knowledge!

devtools::check()

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sessionInfo()

cekehe/rappp documentation built on May 17, 2022, 8:54 a.m.