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Molecular correlates of sleep deprivation in the mouse brain identified by meta-analysis of microarray data.

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The authors' code

Jupyter notebook · 180 lines · 4.9 KB · no license

  1. # %% [markdown]
  2. # # Calculations of the Effect Size (ES) for each microarray study
  3. #
  4. # ### Using Hedges' g value, an adjusted Cohen's d value
  6. # %% [markdown]
  7. # $$ {Enrichment} = \bar{X_2}-\bar{X_1}$$
  8. #
  9. # Let Group 1 be Sleeping Forebrain Oligodendrocyte Expression values and Group 2 be 4-8hSD Forebrain Oligodendrocyte Expression values
  10. #
  11. # (S mean - SD mean) **(Logged values, so minus gives ratio)**
  13. # %% [markdown]
  14. # $$ {Pooled\ Standard\ Deviation} = \sqrt\frac{(n_1-1)S_1^2 +(n_2-1)S_2^2}{(n_2 +n_2) -2} $$
  16. # %% [markdown]
  17. # $$ {Cohen's\ d\ value} = \frac{Enrichment}{Pooled\ Standard\ Deviation} $$
  19. # %% [markdown]
  20. # $$ {Correction\ Factor (J\ Factor)} = 1- \frac{3}{4df-1} $$
  22. # %% [markdown]
  23. # $$ {Hedges'\ g\ value} = Cohen's\ d\ \text{x}\ J\ $$
  25. # %% [markdown]
  26. # $$ {Variance\ in\ d (V_d)} = \frac{n_1- +n_2}{n_1 n_2} + \frac{d^2}{2(n_1 +n_2)} $$
  28. # %% [markdown]
  29. # $$ {Variance\ in\ g (V_g)} = J^2\ \text{x}\ V_d $$
  31. # %% [markdown]
  32. # $$ {Standard\ Error\ in\ g (SE_g)} = \sqrt{V_g} $$
  34. # %% [markdown]
  35. # ## Setup working environment and import data
  37. # %%
  38. import pandas as pd # Dataframes and file IO
  39. import numpy as np # numerical calculations
  40. %cd /Users/Ella1/Desktop/data sets 430AV2
  43. # %%
  44. prefix = '430AV2_ForeOlig_' # define a prefix to add to column names (making indexing easier later)
  46. # %%
  47. # import the data file to a data frame 'df'
  48. df=pd.read_table('DATASET-GSE48369.txt', delimiter='\t', index_col=0) #,nrows=500)
  49. df.shape
  51. # %%
  52. # remove probes that are know to cross-hybridise to more than one target
  53. df =df[~df.index.str.contains('_x_|_s_')] # important reverse selector ~
  54. df.shape
  56. # %% [markdown]
  57. # ## Look at column names and then setup filters for grouping columns into Oligodendrocyte samples of S and SD groups
  58. # It is important that we pick up only one of the two types of tissue investivagtes in this assession.
  60. # %%
  61. df.columns
  63. # %%
  64. # define regular expressions for sleep (S) and sleep dep (SD) filters
  65. s_filt ='ForeOlig_S.CEL'
  66. sd_filt ='ForeOlig_SD.CEL'
  68. # %%
  69. df_s=df.filter(regex= s_filt)
  70. df_s.head()
  72. # %%
  73. df_sd=df.filter(regex= sd_filt)
  74. df_sd.head()
  76. # %% [markdown]
  77. # ## Calculations
  79. # %%
  80. # Enrichment
  82. df[prefix+'Enrich'] = df.filter(regex=sd_filt).mean(axis=1) - df.filter(regex=s_filt).mean(axis=1)
  84. # %%
  85. df[prefix+'Enrich'].head()
  87. # %%
  88. # Calculating Pooled StDev
  89. Scount = df.filter(regex=s_filt).count(axis=1)
  90. SDcount = df.filter(regex=sd_filt).count(axis=1)
  92. StdevS = (Scount-1) * df.filter(regex=s_filt).var(axis=1)
  93. StdevSD = (SDcount-1) * df.filter(regex=sd_filt).var(axis=1)
  95. df[prefix+'poolStDev'] = np.sqrt((StdevS+StdevSD)/(Scount+ SDcount-2))
  97. # %%
  98. # Calculating Cohen's d
  99. df[prefix+'Cohens_d'] = df[prefix+'Enrich'] / df[prefix+'poolStDev']
  101. # %%
  102. #df[prefix+'poolStDev'].head()
  103. df[prefix+'Cohens_d'] .head()
  105. # %%
  106. # Calculating J value (Correction factor)
  108. df[prefix+'J'] = 1-(3/(4*(Scount+SDcount-1)))
  111. # %%
  112. # Calculating Hedge's g
  114. df[prefix+'Hedges_g'] = df[prefix+'Cohens_d'] * df[prefix+'J']
  116. # %%
  117. #df[prefix+'J'].head()
  118. df[prefix+'Hedges_g'] .head()
  120. # %%
  121. # Calculating Var_d
  122. Scount = df.filter(regex=s_filt).count(axis=1)
  123. SDcount = df.filter(regex=sd_filt).count(axis=1)
  125. Ftop1 = Scount + SDcount
  126. Ftop2 = Scount * SDcount
  127. Fbottom1 = np.square(df[prefix+'Cohens_d'])
  128. Fbottom2 = 2*(Scount + SDcount)
  131. df[prefix+'Var_d'] = (Ftop1/Ftop2) + (Fbottom1 /Fbottom2)
  133. # %%
  134. #check output
  135. df[prefix+'Var_d'].head()
  137. # %%
  138. df[prefix+'Var_g'] = df[prefix+'Var_d'] * np.square(df[prefix+'J'])
  140. # %%
  141. # Calculating SEg
  142. df[prefix+'SEg'] = np.sqrt(df[prefix+'Var_g'])
  144. # %%
  145. df.sort_values(by= '430AV2_ForeOlig_Hedges_g', ascending=False, inplace=True)
  146. df
  148. # %%
  149. df.columns
  151. # %% [markdown]
  152. # ### Import key file from BioMart and index probesets to MGI gene symbols
  154. # %%
  155. dfX=pd.read_table('../FHS project/Sleep notebook Copy/BioMart_Ensmbl_index/mart_export72_430v2430Av2.txt',index_col=[3])
  157. dfX.pop('Affy mouse430 2 probeset') # remove 430V2 probeset info (not needed for 430AV2 indexing)
  158. dfX.head(5)
  160. # %%
  161. df_Join = df.join(dfX, how='left', sort=True)
  162. df_FINAL1 = df_Join.groupby('MGI symbol').mean()
  163. df_FINAL1[df_FINAL1.index.duplicated()==True] # checking that no duplicate entries exist in the dataframe
  165. # %% [markdown]
  166. # ### Columns from the list above can then easily be picked to produce files for use later. Examples below given:
  167. # #### df3 = average S and SD expression for the platform and the log-fold changes
  168. # #### df4 = Hedges g values and associated variance for Meta-analysis (after indexing)
  170. # %%
  171. # df3 = df_FINAL1.loc[:,[u'avg-SD', u'avg-S', u'log_fold-S_vs_SD']]
  172. # df3.columns =[prefix+'avg-SD', prefix+'avg-S', prefix+'log_fold-S_vs_SD']
  173. # df3.to_csv('input_files/430AV2_SymbolExpression_forIndex.csv')
  175. # %%
  176. df4 = df_FINAL1.loc[:,[u'430AV2_ForeOlig_Enrich',u'430AV2_ForeOlig_Hedges_g', u'430AV2_ForeOlig_Var_g', u'430AV2_ForeOlig_SEg']]
  177. df4.to_csv('../FHS project/Sleep notebook Copy/IPython_notebooks/input_files/430AV2_ForeOlig_SymbolforIndexHedges.csv')
  179. # %%
  180. df4.head(10) # check final ouput

A10_430AV2_ForeOlig.ipynb at commit fae3c7a, no license · at the source

Overview

Authors: Osama H.M.H. Abdalla1,2, Ella Dunlop3, Tatiana S. Wilson4,5, Paul K. Reardon6, Mudassar Iqbal7, Shu K.E. Tam8, Vladyslav V. Vyazovskiy9, David W. Ray4,5, Laurence A. Brown10, Hai-Ying Mary Cheng1,2, Stuart N. Peirson3
ORCID iDs: Osama H.M.H. Abdalla, Stuart N. Peirson
  1. Department of Biology, University of Toronto Mississauga, Mississauga, ON, L5L 1C6, Canada
  2. Department of Cell & Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
  3. Sleep and Circadian Neuroscience Institute (SCNi), Kavli Institute of Nanoscience Discovery, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
  4. Sleep and Circadian Neuroscience Institute (SCNi), NIHR Oxford Health Biomedical Research Centre, John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom
  5. Oxford Centre for Diabetes, Endocrinology and Metabolism, and Oxford Kavli Centre for Nanoscience Discovery, University of Oxford, Oxford, OX37LE, United Kingdom
  6. Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, 20892, USA
  7. Division of Informatics, Imaging & Data Sciences, University of Manchester, United Kingdom
  8. Duke Kunshan University (DKU), Kunshan, Jiangsu, China
  9. Sleep and Circadian Neuroscience Institute (SCNi), Kavli Institute of Nanoscience Discovery, Department of Physiology, Anatomy and Genetics, University of Oxford, United Kingdom
  10. Research IT, IT Services, University of Oxford, United Kingdom
Institutions: University of Toronto (Canada); University of Oxford (United Kingdom); John Radcliffe Hospital (United Kingdom); Oxford Centre for Diabetes, Endocrinology and Metabolism (United Kingdom); National Institutes of Health (United States); National Institute of Mental Health (United States); University of Manchester (United Kingdom); Duke Kunshan University (China)
Journal: Neurobiology of sleep and circadian rhythms, volume 21, article 100149
Dates: received 12 May 2026; accepted 20 June 2026; published online 23 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.nbscr.2026.100149 · PMID 42518733 · PMCID PMC13382439 · OpenAlex W7165624396
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics
Keywords: Meta-analysis, Sleep deprivation, Rasd1
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Biotechnology and Biological Sciences Research Council (BB/S015817/1, BB/X002357/1); National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC/V000977/1)
Citations: not cited yet (Europe PMC); 75 references in the paper
Research resources: RRID:SCR_016611

Abstract

Studying the transcriptional changes in the brain following sleep deprivation has provided insight into the molecular mechanisms that differ between sleep and wake. Individual studies are limited in their ability to detect differentially expressed genes due to small sample size. Here we performed a meta-analysis of published brain expression data, totalling 173 microarrays across 245 mice. 498 genes were identified as significantly changing with sleep-deprivation at q < 0.01, 96 of which were previously identified by the original studies. Of the remaining 402 novel candidate sleep genes, 14 were associated with human sleep traits and 3 with sleep phenotypes in knockout mice. Candidate gene validation showed significant upregulation of Rasd1 (Dexras1) following sleep deprivation, and phenotyping of Rasd1 KO mice revealed changes in the amount and distribution of behavioural sleep duration and sleep bout structure. These results provide a greater understanding of the molecular correlates of sleep and provide a resource for the sleep research community.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

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FHSProject139/FSH-Project-Notebooks-and-Files

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: fae3c7ae2f8cd9cf432071d42113d8a8eed0fdf5, 5 March 2019
Languages: Jupyter (27)
Size: 60 files, 27 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 27 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (27 files), pandas (27 files), Matplotlib (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
28 files
At the source: github.com/FHSProject139/FSH-Project-Notebooks-and-Files

ohabdalla1/Dexras1_Behavioural_Sleep

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ed0e4416bbf2f6deb231558ab2a8e9e741286371, 18 March 2025
Languages: Jupyter (2)
Size: 66 files, 2 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: rstatix (2 files), tidyverse (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/ohabdalla1/Dexras1_Behavioural_Sleep

The paper's code and data availability statement is in the Data section.

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Data

No dataset and no data link were found in the paper.

Data availability

All code, including Jupyter Notebooks and data files and their corresponding Conda Environments are available for download and re-production at https://github.com/FHSProject139/FSH-Project-Notebooks-and-Files, and a version of the code at the point of manuscript submission is available at https://figshare.com/s/7bed11236e44b6995564.

Jupyter Notebooks and data files pertaining to the Rasd1 KO mice can be found at https://github.com/ohabdalla1/Dexras1_Behavioural_Sleep and a version of the code at the point of manuscript submission is available at https://figshare.com/s/d5cadb1b22ff74f90265.

Data used for the meta-analysis are publicly available and accession codes are listed in Data Set S1 and Data Set S2. All other data are available in the main text or the supplementary materials.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 2, 28 September 2026

  • Authors: added Osama H.M.H. Abdalla (0009-0000-4854-0995); Stuart N. Peirson (0000-0003-3653-834X); removed Osama H.M.H. Abdalla; Stuart N. Peirson

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 11 authors, 3 keywords, 2 funders, 74 references, 1 RRID.

Cite

This paper

Abdalla, O. H., Dunlop, E., Wilson, T. S., Reardon, P. K., Iqbal, M., Tam, S. K., Vyazovskiy, V. V., Ray, D. W., Brown, L. A., Cheng, H.-Y. M., & Peirson, S. N. (2026). Molecular correlates of sleep deprivation in the mouse brain identified by meta-analysis of microarray data. Neurobiology of sleep and circadian rhythms, 21, 100149. https://doi.org/10.1016/j.nbscr.2026.100149

BibTeX

@article{abdalla2026molecular,
author = {Abdalla, Osama H.M.H. and Dunlop, Ella and Wilson, Tatiana S. and Reardon, Paul K. and Iqbal, Mudassar and Tam, Shu K.E. and Vyazovskiy, Vladyslav V. and Ray, David W. and Brown, Laurence A. and Cheng, Hai-Ying Mary and Peirson, Stuart N.},
title = {{Molecular correlates of sleep deprivation in the mouse brain identified by meta-analysis of microarray data}},
journal = {Neurobiology of sleep and circadian rhythms},
year = {2026},
month = jun,
volume = {21},
pages = {100149},
publisher = {Elsevier},
issn = {2451-9944},
doi = {10.1016/j.nbscr.2026.100149},
url = {https://doi.org/10.1016/j.nbscr.2026.100149},
pmid = {42518733},
pmcid = {PMC13382439}
}

RIS

TY - JOUR
AU - Abdalla, Osama H.M.H.
AU - Dunlop, Ella
AU - Wilson, Tatiana S.
AU - Reardon, Paul K.
AU - Iqbal, Mudassar
AU - Tam, Shu K.E.
AU - Vyazovskiy, Vladyslav V.
AU - Ray, David W.
AU - Brown, Laurence A.
AU - Cheng, Hai-Ying Mary
AU - Peirson, Stuart N.
TI - Molecular correlates of sleep deprivation in the mouse brain identified by meta-analysis of microarray data
T2 - Neurobiology of sleep and circadian rhythms
J2 - Neurobiol Sleep Circadian Rhythms
PY - 2026
DA - 2026/06/23
VL - 21
SP - 100149
SN - 2451-9944
PB - Elsevier
DO - 10.1016/j.nbscr.2026.100149
UR - https://doi.org/10.1016/j.nbscr.2026.100149
LA - en
ER -

CSL-JSON

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