Differential analysis GRCh38 analyse

analyse
GRCh38
Differential
Differential analysis of GRCh38-aligned data according to plan ‘analyse’
Authors
Affiliations

Gavin Kelly [Analyst]

Francis Crick Institute

BABS [Development]

Francis Crick Institute

Gavin Kelly [Developer,Statistical Metholodogy]

Francis Crick Institute

1 Preface

We load in all the necessary additional R packages, and set up some initial parameters.

Table 1: Context
Setting Value
section Differential analysis
res_dir results/v9.9.9
VERSION v9.9.9
TAG _v9.9.9-00eb166
staging_dir staging
file_col ID
name_col sample_name
metadata extdata/metadata_GRCh38.csv
counts extdata/genes.results/GRCh38/
alignment GRCh38
spec analyse
specname analyse
script staging/02_differential_analyse_GRCh38.qmd
NoteSetting analysis parameter
  • A random seed of 1 is used to ensure reproduciblity
  • Using analysis plan ‘analyse’.
  • Using alignment settings ‘GRCh38’
NoteSetting analysis parameter
  • The value of rowNoun is gene
NoteSetting analysis parameter
  • The value of RowNoun is Gene

2 Find differential genes

We use DESeq2 (Love, Anders, and Huber 2025) to find differential genes using the negative binomial distribution to model counts, with IHW (Ignatiadis et al. 2016) for multiple-testing correction with greater power than Benjamini-Hochberg, and ashr (Stephens et al. 2023) for effect-size shrinkage to ensure reported fold-changes are more robust.

NoteSetting analysis parameter
  • The threshold of statistical significance is 0.05
  • The threshold for absolute effect size is 0
  • Default independent filtering
  • Use the ‘“default”’ summary of an LRT ‘effect size’
  • bluster::HclustParam(cut.params = list(k = 12))
  • The value of pc_x is 1
  • The value of pc_y is 2
  • The value of sample_clust is bluster::HclustParam(metric = “pearson”)

Open Spreadsheet ‘D1’

2.1 Summary Tables

Here we summarise the results of the differential testing. Each sample-set gets its own table (flick between tabs to choose which), within which the different models, and their null hypotheses, are enumerated.

In the ‘Significant’ column we tally the number of significant genes (for pairwise comparisons, separated into up or down, where A-B being labelled up means expression is higher in A; for omnibus comparisons, a broad categorisation of the most extreme groups, as described above.) We also tally the total number of genes exhibiting that behaviour (ie not necessarily statistically signficant) - these might not always add up to the same value, as there is an independent filter of low-signal genes whose effect varies from comparison to comparison.

Our naming convention for a comparison often looks like A-B|M - again, because this is an semi-automated report, this mightn’t be optimal, but the rationale is A-B refers to the two experimental conditions being compared (a ‘upward’ fold change will be reported if the expression is higher in A than it is in B). The |M part refers to the fact that we may have looked at that A-B comparison conditional upon some other variable (which in this example takes the value M): anything before the | symbol identifies the experimental conditions that are being contrasted, and the part after the | identifies any stratification/conditioning of the data.

In Figure 1 we present some diagnostic plots of the observed p-values. Theoretically we want the pink part of the plot to have a solid peak on the left, and then make a reasonable plateau across higher p-values: gross deviations from this can indicate an absence of differential behaviour (lack of left-peak) or a model mis-specification that might need investigating. The blue tracks indicate p-values corresponding to genes with very low expression, which would typically struggle to provide evidence of differential behaviour, and so show an example of what a ‘bad’ pink trace might look like.

Table 2: D1
GeneList summary
D1
Comparison Group Significant Total mname cname
M1
C1 Down 1860 7386 Simple (Untreated-Dexamethasone)
C1 Up 1528 7608 Simple (Untreated-Dexamethasone)
C2.1 Up 273 8558 Simple (N061011-N052611)
C2.1 Down 238 8194 Simple (N061011-N052611)
C2.2 Down 741 8490 Simple (N080611-N052611)
C2.2 Up 583 8848 Simple (N080611-N052611)
C2.3 Down 927 8654 Simple (N080611-N061011)
C2.3 Up 685 8684 Simple (N080611-N061011)
C2.4 Down 717 7764 Simple (N61311-N052611)
C2.4 Up 567 7816 Simple (N61311-N052611)
C2.5 Down 649 8621 Simple (N61311-N061011)
C2.5 Up 476 8131 Simple (N61311-N061011)
C2.6 Up 902 8578 Simple (N61311-N080611)
C2.6 Down 916 8760 Simple (N61311-N080611)
M2
C3.1 Down 86 9731 Line-only (N061011-N052611)
C3.1 Up 86 9952 Line-only (N061011-N052611)
C3.2 Down 310 8869 Line-only (N080611-N052611)
C3.2 Up 207 9055 Line-only (N080611-N052611)
C3.3 Down 381 8706 Line-only (N080611-N061011)
C3.3 Up 220 8632 Line-only (N080611-N061011)
C3.4 Down 221 8406 Line-only (N61311-N052611)
C3.4 Up 192 8346 Line-only (N61311-N052611)
C3.5 Up 197 8340 Line-only (N61311-N061011)
C3.5 Down 195 8998 Line-only (N61311-N061011)
C3.6 Up 368 9798 Line-only (N61311-N080611)
C3.6 Down 292 9885 Line-only (N61311-N080611)
M3
C4 Down 1274 8501 Treatment-only (Untreated-Dexamethasone)
C4 Up 964 8775 Treatment-only (Untreated-Dexamethasone)
Table 3: D1 model-fit stats
Model-fit summary
D1
Model Low Count Outlier
M1 15240;13482;12896;14654 NA
M2 10551;12310;12896;13482 NA
M3 12895 63
Figure 1: p-Diagnostic for D1 M1 png pdf
Figure 2: p-Diagnostic for D1 M2 png pdf
Figure 3: p-Diagnostic for D1 M3 png pdf

3 Dataset D1: All

All samples included

  • Samples for inclusion in any analysis: TRUE (8)
  • Samples used, out of those already selected for inclusion, to actively inform analysis (differential, heatmap’s top genes, principal components): All included samples (8)

3.1 Model M1: Simple

Including treatment and line, so that we can look at either one of those effects while accounting for any systematic changes in the other. But no interaction, so when there is a modifying effect (of treatment type on the response to line, or vice versa) will be unaccounted for and genes exhibiting this behaviour will tend not to be selected. There is no replication (ie no line x treatment combination has more than one sample, so we have to restrict our model to at most this complexity.

Expression ~ treatment + cellLine

3.1.1 Comparison C1: (Untreated-Dexamethasone)

contrast = Untreated - Dexamethasone

Figure 4: MA plot of C1 -differential genes for model ‘M1’ in dataset ‘D1’ png pdf
Figure 5: Metadata colour-codings for All png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 6: Heatmap on C1 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Figure 7: Sorted heatmap on C1 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 8: Heatmap on C1 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Figure 9: Sorted heatmap on C1 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 10: Heatmap on C1 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Figure 11: Sorted heatmap on C1 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 12: Annotated by colour~treatment in dataset ‘D1’ png pdf
Figure 13: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 14: Annotated by colour~treatment in dataset ‘D1’ having removed effect of treatment png pdf
Figure 15: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 16: Annotated by colour~treatment in dataset ‘D1’ having removed effect of cellLine png pdf
Figure 17: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of cellLine png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 18: Cluster profile for C1 -differential genes of model ‘M1’ in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 19: Cluster profile for C1 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 20: Cluster profile for C1 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine png pdf

3.1.2 Comparison C2.1: (N061011-N052611)

contrast = N061011 - N052611

Figure 21: MA plot of C2.1 -differential genes for model ‘M1’ in dataset ‘D1’ png pdf
Figure 22: Metadata colour-codings for All png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 23: Heatmap on C2.1 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Figure 24: Sorted heatmap on C2.1 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 25: Heatmap on C2.1 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Figure 26: Sorted heatmap on C2.1 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 27: Heatmap on C2.1 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Figure 28: Sorted heatmap on C2.1 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 29: Annotated by colour~treatment in dataset ‘D1’ png pdf
Figure 30: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 31: Annotated by colour~treatment in dataset ‘D1’ having removed effect of treatment png pdf
Figure 32: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 33: Annotated by colour~treatment in dataset ‘D1’ having removed effect of cellLine png pdf
Figure 34: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of cellLine png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 35: Cluster profile for C2.1 -differential genes of model ‘M1’ in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 36: Cluster profile for C2.1 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 37: Cluster profile for C2.1 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine png pdf

3.1.3 Comparison C2.2: (N080611-N052611)

contrast = N080611 - N052611

Figure 38: MA plot of C2.2 -differential genes for model ‘M1’ in dataset ‘D1’ png pdf
Figure 39: Metadata colour-codings for All png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 40: Heatmap on C2.2 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Figure 41: Sorted heatmap on C2.2 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 42: Heatmap on C2.2 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Figure 43: Sorted heatmap on C2.2 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 44: Heatmap on C2.2 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Figure 45: Sorted heatmap on C2.2 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 46: Annotated by colour~treatment in dataset ‘D1’ png pdf
Figure 47: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 48: Annotated by colour~treatment in dataset ‘D1’ having removed effect of treatment png pdf
Figure 49: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 50: Annotated by colour~treatment in dataset ‘D1’ having removed effect of cellLine png pdf
Figure 51: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of cellLine png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 52: Cluster profile for C2.2 -differential genes of model ‘M1’ in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 53: Cluster profile for C2.2 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 54: Cluster profile for C2.2 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine png pdf

3.1.4 Comparison C2.3: (N080611-N061011)

contrast = N080611 - N061011

Figure 55: MA plot of C2.3 -differential genes for model ‘M1’ in dataset ‘D1’ png pdf
Figure 56: Metadata colour-codings for All png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 57: Heatmap on C2.3 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Figure 58: Sorted heatmap on C2.3 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 59: Heatmap on C2.3 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Figure 60: Sorted heatmap on C2.3 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 61: Heatmap on C2.3 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Figure 62: Sorted heatmap on C2.3 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 63: Annotated by colour~treatment in dataset ‘D1’ png pdf
Figure 64: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 65: Annotated by colour~treatment in dataset ‘D1’ having removed effect of treatment png pdf
Figure 66: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 67: Annotated by colour~treatment in dataset ‘D1’ having removed effect of cellLine png pdf
Figure 68: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of cellLine png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 69: Cluster profile for C2.3 -differential genes of model ‘M1’ in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 70: Cluster profile for C2.3 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 71: Cluster profile for C2.3 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine png pdf

3.1.5 Comparison C2.4: (N61311-N052611)

contrast = N61311 - N052611

Figure 72: MA plot of C2.4 -differential genes for model ‘M1’ in dataset ‘D1’ png pdf
Figure 73: Metadata colour-codings for All png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 74: Heatmap on C2.4 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Figure 75: Sorted heatmap on C2.4 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 76: Heatmap on C2.4 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Figure 77: Sorted heatmap on C2.4 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 78: Heatmap on C2.4 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Figure 79: Sorted heatmap on C2.4 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 80: Annotated by colour~treatment in dataset ‘D1’ png pdf
Figure 81: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 82: Annotated by colour~treatment in dataset ‘D1’ having removed effect of treatment png pdf
Figure 83: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 84: Annotated by colour~treatment in dataset ‘D1’ having removed effect of cellLine png pdf
Figure 85: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of cellLine png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 86: Cluster profile for C2.4 -differential genes of model ‘M1’ in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 87: Cluster profile for C2.4 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 88: Cluster profile for C2.4 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine png pdf

3.1.6 Comparison C2.5: (N61311-N061011)

contrast = N61311 - N061011

Figure 89: MA plot of C2.5 -differential genes for model ‘M1’ in dataset ‘D1’ png pdf
Figure 90: Metadata colour-codings for All png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 91: Heatmap on C2.5 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Figure 92: Sorted heatmap on C2.5 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 93: Heatmap on C2.5 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Figure 94: Sorted heatmap on C2.5 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 95: Heatmap on C2.5 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Figure 96: Sorted heatmap on C2.5 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 97: Annotated by colour~treatment in dataset ‘D1’ png pdf
Figure 98: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 99: Annotated by colour~treatment in dataset ‘D1’ having removed effect of treatment png pdf
Figure 100: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 101: Annotated by colour~treatment in dataset ‘D1’ having removed effect of cellLine png pdf
Figure 102: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of cellLine png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 103: Cluster profile for C2.5 -differential genes of model ‘M1’ in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 104: Cluster profile for C2.5 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 105: Cluster profile for C2.5 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine png pdf

3.1.7 Comparison C2.6: (N61311-N080611)

contrast = N61311 - N080611

Figure 106: MA plot of C2.6 -differential genes for model ‘M1’ in dataset ‘D1’ png pdf
Figure 107: Metadata colour-codings for All png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 108: Heatmap on C2.6 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Figure 109: Sorted heatmap on C2.6 -differential genes of model ‘M1’ in dataset ‘D1’. White for baseline expression png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 110: Heatmap on C2.6 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Figure 111: Sorted heatmap on C2.6 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment. White for baseline expression png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 112: Heatmap on C2.6 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Figure 113: Sorted heatmap on C2.6 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine. White for baseline expression png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 114: Annotated by colour~treatment in dataset ‘D1’ png pdf
Figure 115: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 116: Annotated by colour~treatment in dataset ‘D1’ having removed effect of treatment png pdf
Figure 117: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 118: Annotated by colour~treatment in dataset ‘D1’ having removed effect of cellLine png pdf
Figure 119: Annotated by colour~cellLine in dataset ‘D1’ having removed effect of cellLine png pdf
Plot config P1

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment
Figure 120: Cluster profile for C2.6 -differential genes of model ‘M1’ in dataset ‘D1’ png pdf
Plot config P2

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - treatment

Figure 121: Cluster profile for C2.6 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of treatment png pdf
Plot config P3

Aesthetics:

  • X = cellLine
  • Colour = treatment
  • Grouping variables = treatment

Reconstruction: . - cellLine

Figure 122: Cluster profile for C2.6 -differential genes of model ‘M1’ in dataset ‘D1’ having removed effect of cellLine png pdf

3.2 Model M2: Line-only

Just including a line effect, and totally ignoring treatment. So any systematic treatment effect will not be accounted for, and genes exhibiting a change due to treatment will tend not to be selected

Expression ~ cellLine

3.2.1 Comparison C3.1: (N061011-N052611)

contrast = N061011 - N052611

Figure 123: MA plot of C3.1 -differential genes for model ‘M2’ in dataset ‘D1’ png pdf
Figure 124: Metadata colour-codings for All png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 125: Heatmap on C3.1 -differential genes of model ‘M2’ in dataset ‘D1’. White for baseline expression png pdf
Figure 126: Sorted heatmap on C3.1 -differential genes of model ‘M2’ in dataset ‘D1’. White for baseline expression png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 127: Annotated by nothing in dataset ‘D1’ png pdf
Figure 128: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 129: Cluster profile for C3.1 -differential genes of model ‘M2’ in dataset ‘D1’ png pdf

3.2.2 Comparison C3.2: (N080611-N052611)

contrast = N080611 - N052611

Figure 130: MA plot of C3.2 -differential genes for model ‘M2’ in dataset ‘D1’ png pdf
Figure 131: Metadata colour-codings for All png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 132: Heatmap on C3.2 -differential genes of model ‘M2’ in dataset ‘D1’. White for baseline expression png pdf
Figure 133: Sorted heatmap on C3.2 -differential genes of model ‘M2’ in dataset ‘D1’. White for baseline expression png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 134: Annotated by nothing in dataset ‘D1’ png pdf
Figure 135: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 136: Cluster profile for C3.2 -differential genes of model ‘M2’ in dataset ‘D1’ png pdf

3.2.3 Comparison C3.3: (N080611-N061011)

contrast = N080611 - N061011

Figure 137: MA plot of C3.3 -differential genes for model ‘M2’ in dataset ‘D1’ png pdf
Figure 138: Metadata colour-codings for All png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 139: Heatmap on C3.3 -differential genes of model ‘M2’ in dataset ‘D1’. White for baseline expression png pdf
Figure 140: Sorted heatmap on C3.3 -differential genes of model ‘M2’ in dataset ‘D1’. White for baseline expression png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 141: Annotated by nothing in dataset ‘D1’ png pdf
Figure 142: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 143: Cluster profile for C3.3 -differential genes of model ‘M2’ in dataset ‘D1’ png pdf

3.2.4 Comparison C3.4: (N61311-N052611)

contrast = N61311 - N052611

Figure 144: MA plot of C3.4 -differential genes for model ‘M2’ in dataset ‘D1’ png pdf
Figure 145: Metadata colour-codings for All png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 146: Heatmap on C3.4 -differential genes of model ‘M2’ in dataset ‘D1’. White for baseline expression png pdf
Figure 147: Sorted heatmap on C3.4 -differential genes of model ‘M2’ in dataset ‘D1’. White for baseline expression png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 148: Annotated by nothing in dataset ‘D1’ png pdf
Figure 149: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 150: Cluster profile for C3.4 -differential genes of model ‘M2’ in dataset ‘D1’ png pdf

3.2.5 Comparison C3.5: (N61311-N061011)

contrast = N61311 - N061011

Figure 151: MA plot of C3.5 -differential genes for model ‘M2’ in dataset ‘D1’ png pdf
Figure 152: Metadata colour-codings for All png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 153: Heatmap on C3.5 -differential genes of model ‘M2’ in dataset ‘D1’. White for baseline expression png pdf
Figure 154: Sorted heatmap on C3.5 -differential genes of model ‘M2’ in dataset ‘D1’. White for baseline expression png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 155: Annotated by nothing in dataset ‘D1’ png pdf
Figure 156: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 157: Cluster profile for C3.5 -differential genes of model ‘M2’ in dataset ‘D1’ png pdf

3.2.6 Comparison C3.6: (N61311-N080611)

contrast = N61311 - N080611

Figure 158: MA plot of C3.6 -differential genes for model ‘M2’ in dataset ‘D1’ png pdf
Figure 159: Metadata colour-codings for All png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 160: Heatmap on C3.6 -differential genes of model ‘M2’ in dataset ‘D1’. White for baseline expression png pdf
Figure 161: Sorted heatmap on C3.6 -differential genes of model ‘M2’ in dataset ‘D1’. White for baseline expression png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 162: Annotated by nothing in dataset ‘D1’ png pdf
Figure 163: Annotated by colour~cellLine in dataset ‘D1’ png pdf
Plot config design

Aesthetics:

  • X = cellLine

Reconstruction: cellLine

Figure 164: Cluster profile for C3.6 -differential genes of model ‘M2’ in dataset ‘D1’ png pdf

3.3 Model M3: Treatment-only

Just including a treatment effect, and totally ignoring treatment. So any systematic differences between lines will not be accounted for, and genes exhibiting a dependencey on line will tend not to be selected

Expression ~ treatment

3.3.1 Comparison C4: (Untreated-Dexamethasone)

contrast = Untreated - Dexamethasone

Figure 165: MA plot of C4 -differential genes for model ‘M3’ in dataset ‘D1’ png pdf
Figure 166: Metadata colour-codings for All png pdf
Plot config design

Aesthetics:

  • X = treatment

Reconstruction: treatment

Figure 167: Heatmap on C4 -differential genes of model ‘M3’ in dataset ‘D1’. White for baseline expression png pdf
Figure 168: Sorted heatmap on C4 -differential genes of model ‘M3’ in dataset ‘D1’. White for baseline expression png pdf
Plot config design

Aesthetics:

  • X = treatment

Reconstruction: treatment

Figure 169: Annotated by nothing in dataset ‘D1’ png pdf
Figure 170: Annotated by colour~treatment in dataset ‘D1’ png pdf
Plot config design

Aesthetics:

  • X = treatment

Reconstruction: treatment

Figure 171: Cluster profile for C4 -differential genes of model ‘M3’ in dataset ‘D1’ png pdf

4 Downloads

Differential gene for ‘D1’

Gene-Cluster mappings

5 Terms Of Use

The Crick has a publication policy and we expect to be included on publications, regardless of funding arrangements. Any use of these results in publication must be discussed with BABS regarding authorship. If not authorship then the BABS analyst must receive a named acknowledgement. Please also cite the following sources which have enabled the analysis to be carried out.

6 References

Ignatiadis, Nikolaos, Bernd Klaus, Judith Zaugg, and Wolfgang Huber. 2016. “Data-Driven Hypothesis Weighting Increases Detection Power in Genome-Scale Multiple Testing.” Nature Methods. https://doi.org/10.1038/nmeth.3885.
Love, Michael, Simon Anders, and Wolfgang Huber. 2025. DESeq2: Differential Gene Expression Analysis Based on the Negative Binomial Distribution. https://doi.org/10.18129/B9.bioc.DESeq2.
Stephens, Matthew, Peter Carbonetto, David Gerard, Mengyin Lu, Lei Sun, Jason Willwerscheid, and Nan Xiao. 2023. Ashr: Methods for Adaptive Shrinkage, Using Empirical Bayes. https://github.com/stephens999/ashr.