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File Version Author Date Message
Rmd 8f254aa Gunjan Dixit 2024-10-09 Modified age analysis with interaction model
html 8f254aa Gunjan Dixit 2024-10-09 Modified age analysis with interaction model
html 0f07f72 Gunjan Dixit 2024-10-07 Added BAL subclustering without DecontX

Introduction

This RMarkdown performs age modelling analysis for the earlyAIR tissue- Adenoids

suppressPackageStartupMessages({
  library(here)
  library(glue)
  library(patchwork)
  library(Seurat)
  library(dplyr)
  library(tidyverse)
  library(gridExtra)
  library(paletteer)
  library(viridis)
  library(ggforce)
  library(RColorBrewer)
  library(scran)
  library(ggridges)
  library(speckle)
  library(edgeR)
  library(kableExtra)
  library(dplyr)
  library(limma)
  library(knitr)
  library(openxlsx)
})

Load data

tissue <- "Adenoids"
out <- here("output/RDS/AllBatches_Annotation_SEUs_v2/G000231_Neeland_Adenoids.annotated_clusters.SEU.rds")
merged_obj <- readRDS(out)
merged_obj
An object of class Seurat 
17456 features across 184005 samples within 1 assay 
Active assay: RNA (17456 features, 2000 variable features)
 10 layers present: data.1, data.2, data.3, counts.1, scale.data.1, counts.2, scale.data.2, counts.3, scale.data.3, scale.data
 2 dimensional reductions calculated: pca, umap.merged

Plot sample wise cell-type proportions

metadata_df <- data.frame(
  sample = merged_obj$sample_id, 
  donor = merged_obj$donor_id,
  age_years = as.character(merged_obj$age_years),  
  cell_type = Idents(merged_obj)
)

color_palette = readRDS(here("output/RDS/color_palette_unique.rds"))

metadata_df$age_years <- as.numeric(metadata_df$age_years)

barplot_data <- metadata_df %>%
  group_by(donor, age_years, cell_type) %>%
  summarise(n_cells = n()) %>%
  ungroup() %>%
  group_by(donor, age_years) %>%
  mutate(n_cells_total = sum(n_cells)) %>%
  ungroup() %>%
  mutate(percentage_cells = n_cells / n_cells_total)
`summarise()` has grouped output by 'donor', 'age_years'. You can override
using the `.groups` argument.
barplot_data <- barplot_data %>%
  arrange(age_years)

a <- ggplot(barplot_data, aes(x = reorder(paste(donor, age_years, sep = ":"), age_years),
                         y = percentage_cells, fill = cell_type)) +
  geom_bar(stat = "identity") +
  ggtitle(paste0("Age vs Cell Type Proportions: ", tissue)) +
  labs(x = "Sample:Age (Years)", y = "Proportion", fill = "Cell Type") +
  scale_fill_manual(values = color_palette) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 13, hjust = 0.5, face = "bold"),
    legend.position = "top",
    axis.text.x = element_text(angle = 45, hjust = 1)
  )
a

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Calculate proportions using Propeller

props <- getTransformedProps(clusters = merged_obj$cell_labels_v2,
                               sample = merged_obj$sample_id, transform = "asin")
Performing arcsin square root transformation of proportions
cat('### ', tissue, '\n')
###  Adenoids 
# Plot Cell Type Mean Variance
p1 <- plotCellTypeMeanVar(props$Counts)
Using classic mode.

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07 
# Plot Cell Type Proportions Mean Variance
p2 <- plotCellTypePropsMeanVar(props$Counts)

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07 
p1 / p2
numeric(0)
print(knitr::kable(props$Proportions, caption = "Cell-type proportions in samples"))


Table: Cell-type proportions in samples

|                                 |      s042|      s043|      s044|      s045|      s046|      s047|      s048|      s049|      s050|      s051|      s052|      s053|      s054|      s055|      s056|      s057|      s122|      s123|      s124|      s125|      s126|      s127|      s128|      s129|      s130|      s131|      s132|      s133|      s134|      s135|      s136|      s137|
|:--------------------------------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|B cells                          | 0.0012565| 0.0011970| 0.0036579| 0.0011142| 0.0003168| 0.0016077| 0.0007910| 0.0013493| 0.0011455| 0.0021042| 0.0006300| 0.0006954| 0.0030349| 0.0008116| 0.0006959| 0.0001979| 0.0057615| 0.0023669| 0.0005106| 0.0012739| 0.0027930| 0.0234757| 0.0253067| 0.0010433| 0.0013317| 0.0010542| 0.0021198| 0.0022664| 0.0010437| 0.0016762| 0.0010556| 0.0004396|
|CD4 T proliferating              | 0.0041885| 0.0083792| 0.0104108| 0.0037140| 0.0060184| 0.0036748| 0.0158197| 0.0026985| 0.0057274| 0.0097317| 0.0066152| 0.0052156| 0.0037353| 0.0045447| 0.0039434| 0.0045527| 0.0059535| 0.0029586| 0.0097005| 0.0117834| 0.0081796| 0.0088034| 0.0076687| 0.0073031| 0.0042373| 0.0124394| 0.0044628| 0.0039033| 0.0037274| 0.0041905| 0.0042226| 0.0017582|
|CD4 TCM                          | 0.0069110| 0.0229830| 0.0261677| 0.0155989| 0.0136205| 0.0128617| 0.0096895| 0.0057826| 0.0118366| 0.0136770| 0.0094503| 0.0206885| 0.0094549| 0.0120110| 0.0206449| 0.0178147| 0.0092184| 0.0236686| 0.0154867| 0.0187898| 0.0101746| 0.0208673| 0.0734663| 0.0425143| 0.0077482| 0.0409024| 0.0194131| 0.0177537| 0.0216192| 0.0268194| 0.0269944| 0.0114286|
|CD4 TFH                          | 0.0462827| 0.0811587| 0.0658413| 0.0560817| 0.0650934| 0.0661461| 0.0711885| 0.0574402| 0.0446735| 0.0699632| 0.0571744| 0.0827538| 0.0582468| 0.0581075| 0.0347947| 0.0506730| 0.0359132| 0.0295858| 0.0689244| 0.1019108| 0.0817955| 0.0352136| 0.0029141| 0.0555556| 0.0364407| 0.0453300| 0.0440701| 0.0600604| 0.0247503| 0.0635564| 0.0438848| 0.0665201|
|CD4 TN                           | 0.0452356| 0.0237012| 0.0697805| 0.0250696| 0.0489389| 0.0445567| 0.0464702| 0.0246723| 0.0347461| 0.0352446| 0.0480391| 0.0335535| 0.0189098| 0.0495050| 0.0331710| 0.0496833| 0.0453236| 0.0463511| 0.0382914| 0.0560510| 0.0539651| 0.0143463| 0.0426380| 0.1051122| 0.0244552| 0.0923466| 0.0539998| 0.0256862| 0.0262412| 0.0486101| 0.0396622| 0.0058608|
|CD4 Treg-eff                     | 0.0150785| 0.0304046| 0.0450197| 0.0256267| 0.0199557| 0.0337621| 0.0322325| 0.0264071| 0.0271096| 0.0210416| 0.0133879| 0.0429416| 0.0217112| 0.0271060| 0.0350267| 0.0364212| 0.0326484| 0.0422091| 0.0163376| 0.0350318| 0.0167581| 0.0286925| 0.0165644| 0.0219092| 0.0208232| 0.0274088| 0.0452973| 0.0248048| 0.0237066| 0.0324068| 0.0278993| 0.0313553|
|CD8 TF                           | 0.0119372| 0.0114915| 0.0140687| 0.0144847| 0.0155211| 0.0176849| 0.0102828| 0.0100231| 0.0370370| 0.0107838| 0.0239408| 0.0368567| 0.0330337| 0.0193150| 0.0213408| 0.0401821| 0.0334166| 0.0435897| 0.0161675| 0.0178344| 0.0114713| 0.0169547| 0.0742331| 0.0255608| 0.0368039| 0.0455408| 0.0263305| 0.0345001| 0.0380200| 0.0192764| 0.0203589| 0.0225641|
|CD8 TN                           | 0.0165445| 0.0313622| 0.0264491| 0.0094708| 0.0318340| 0.0436380| 0.0217520| 0.0306476| 0.0215731| 0.0144661| 0.0096078| 0.0175591| 0.0096883| 0.0124980| 0.0132220| 0.0237530| 0.0280392| 0.0185404| 0.0457794| 0.0347134| 0.0172569| 0.0114118| 0.0595092| 0.0341680| 0.0319613| 0.0638836| 0.0103760| 0.0188869| 0.0184881| 0.0131303| 0.0149299| 0.0033700|
|csMBC FCRL4/5+                   | 0.0069110| 0.0110127| 0.0053461| 0.0042711| 0.0114032| 0.0043638| 0.0085031| 0.0042406| 0.0064910| 0.0055234| 0.0058277| 0.0057371| 0.0067702| 0.0040578| 0.0034795| 0.0029691| 0.0057615| 0.0017751| 0.0062968| 0.0095541| 0.0077805| 0.0068471| 0.0026074| 0.0046948| 0.0032688| 0.0075901| 0.0035702| 0.0050365| 0.0028329| 0.0034921| 0.0025637| 0.0013187|
|Cycling GCB                      | 0.0171728| 0.0126885| 0.0070343| 0.0168988| 0.0288248| 0.0082683| 0.0251137| 0.0227448| 0.0160367| 0.0160442| 0.0143330| 0.0182545| 0.0204272| 0.0245090| 0.0150777| 0.0039588| 0.0071058| 0.0067061| 0.0153165| 0.0343949| 0.0179551| 0.1565047| 0.0173313| 0.0039124| 0.0188862| 0.0080118| 0.0022314| 0.0081843| 0.0047711| 0.0124319| 0.0081436| 0.0099634|
|Double negative T                | 0.0027225| 0.0055063| 0.0045020| 0.0027855| 0.0041178| 0.0103353| 0.0031639| 0.0050116| 0.0055365| 0.0052604| 0.0015751| 0.0020862| 0.0038520| 0.0030839| 0.0025516| 0.0049485| 0.0026887| 0.0098619| 0.0032335| 0.0044586| 0.0066833| 0.0074992| 0.0027607| 0.0057381| 0.0021792| 0.0029517| 0.0045744| 0.0052883| 0.0025347| 0.0061461| 0.0015081| 0.0008791|
|DZ early  Sphase                 | 0.0307853| 0.0217860| 0.0239167| 0.0209842| 0.0476718| 0.0199816| 0.0353965| 0.0210100| 0.0269187| 0.0294582| 0.0300835| 0.0194715| 0.0317497| 0.0415517| 0.0141498| 0.0027712| 0.0082581| 0.0142012| 0.0294418| 0.0324841| 0.0296259| 0.0769482| 0.0073620| 0.0130412| 0.0426150| 0.0050601| 0.0153966| 0.0211534| 0.0165499| 0.0308702| 0.0158347| 0.0124542|
|DZ G2Mphase                      | 0.0272251| 0.0189131| 0.0230726| 0.0172702| 0.0465632| 0.0231971| 0.0276844| 0.0269854| 0.0236732| 0.0265650| 0.0250433| 0.0309458| 0.0284814| 0.0383055| 0.0157736| 0.0023753| 0.0105627| 0.0138067| 0.0214432| 0.0280255| 0.0251372| 0.0205412| 0.0041411| 0.0106938| 0.0376513| 0.0046384| 0.0146156| 0.0176278| 0.0150589| 0.0284956| 0.0131202| 0.0161172|
|DZ GCB                           | 0.0387435| 0.0179555| 0.0222285| 0.0276695| 0.0422870| 0.0293983| 0.0294641| 0.0304549| 0.0343643| 0.0355076| 0.0291384| 0.0279903| 0.0424886| 0.0592436| 0.0336349| 0.0065321| 0.0190129| 0.0179487| 0.0234854| 0.0292994| 0.0334165| 0.0313009| 0.0085890| 0.0135629| 0.0386199| 0.0061143| 0.0160660| 0.0239234| 0.0229611| 0.0287750| 0.0147791| 0.0279853|
|DZ GCB Noproli-memory like       | 0.0039791| 0.0028729| 0.0056275| 0.0070566| 0.0014254| 0.0018374| 0.0120625| 0.0034695| 0.0024819| 0.0044713| 0.0020476| 0.0060848| 0.0024513| 0.0284045| 0.0044073| 0.0015835| 0.0007682| 0.0003945| 0.0018720| 0.0015924| 0.0029925| 0.0078252| 0.0009202| 0.0023474| 0.0018160| 0.0010542| 0.0016735| 0.0017628| 0.0014910| 0.0037715| 0.0015081| 0.0030769|
|DZ late Sphase                   | 0.0360209| 0.0241800| 0.0306697| 0.0219127| 0.0503643| 0.0213597| 0.0419221| 0.0319969| 0.0274914| 0.0420831| 0.0315010| 0.0265994| 0.0330337| 0.0409025| 0.0178613| 0.0033650| 0.0149798| 0.0147929| 0.0386317| 0.0401274| 0.0324190| 0.0104336| 0.0029141| 0.0179969| 0.0422518| 0.0046384| 0.0159545| 0.0175019| 0.0187863| 0.0270988| 0.0158347| 0.0153846|
|DZtoLZ GCB transition            | 0.0420942| 0.0217860| 0.0453011| 0.0521820| 0.0521064| 0.0627010| 0.0383627| 0.0695837| 0.0412371| 0.0570752| 0.0548118| 0.0584145| 0.0595308| 0.0545366| 0.0510322| 0.0077197| 0.0366814| 0.0487179| 0.0490129| 0.0579618| 0.0526683| 0.0661885| 0.0377301| 0.0469484| 0.0986683| 0.0143369| 0.0355908| 0.0756736| 0.0366781| 0.0628579| 0.0357412| 0.0742857|
|Early GC-committed NBC           | 0.0188482| 0.0081398| 0.0112549| 0.0183844| 0.0193221| 0.0117134| 0.0166106| 0.0194680| 0.0129821| 0.0163072| 0.0166955| 0.0241655| 0.0261468| 0.0228859| 0.0118302| 0.0043547| 0.0080661| 0.0104536| 0.0134445| 0.0181529| 0.0193516| 0.0159765| 0.0036810| 0.0146062| 0.0175545| 0.0042167| 0.0120495| 0.0176278| 0.0125242| 0.0181590| 0.0165887| 0.0147985|
|Early MBC                        | 0.0456545| 0.0162796| 0.0295442| 0.0228412| 0.0364270| 0.0229674| 0.0458770| 0.0138782| 0.0316915| 0.0249868| 0.0203182| 0.0292072| 0.1040037| 0.0506411| 0.0308513| 0.0057403| 0.0078740| 0.0102564| 0.0194010| 0.0318471| 0.0282294| 0.0120639| 0.0004601| 0.0143453| 0.0253027| 0.0046384| 0.0080330| 0.0227902| 0.0126733| 0.0222098| 0.0054290| 0.0441026|
|Early PC precursor               | 0.0039791| 0.0023941| 0.0033765| 0.0020427| 0.0047513| 0.0036748| 0.0031639| 0.0048188| 0.0015273| 0.0036823| 0.0045676| 0.0052156| 0.0107389| 0.0030839| 0.0023196| 0.0001979| 0.0007682| 0.0011834| 0.0023826| 0.0028662| 0.0061845| 0.0042387| 0.0015337| 0.0036515| 0.0042373| 0.0006325| 0.0021198| 0.0036515| 0.0029820| 0.0032127| 0.0067863| 0.0026374|
|Epithelial cells                 | 0.0018848| 0.0026335| 0.0014069| 0.0020427| 0.0006335| 0.0006890| 0.0017797| 0.0021203| 0.0003818| 0.0007891| 0.0011025| 0.0029555| 0.0072371| 0.0025970| 0.0062630| 0.0015835| 0.0009602| 0.0021696| 0.0015317| 0.0060510| 0.0007980| 0.0045647| 0.0029141| 0.0005216| 0.0009685| 0.0010542| 0.0011157| 0.0012591| 0.0025347| 0.0022349| 0.0031669| 0.0011722|
|Follicular dendritic cells       | 0.0052356| 0.0038305| 0.0123804| 0.0053853| 0.0120367| 0.0022967| 0.0073166| 0.0057826| 0.0061092| 0.0065755| 0.0039376| 0.0073018| 0.0100385| 0.0094141| 0.0041754| 0.0023753| 0.0003841| 0.0027613| 0.0054459| 0.0047771| 0.0032918| 0.0146723| 0.0016871| 0.0052165| 0.0081114| 0.0025300| 0.0027892| 0.0076807| 0.0087968| 0.0132700| 0.0043734| 0.0055678|
|GC-commited metabolic activation | 0.0157068| 0.0148432| 0.0067530| 0.0176416| 0.0361102| 0.0057418| 0.0142377| 0.0121434| 0.0116457| 0.0160442| 0.0141755| 0.0053894| 0.0173923| 0.0154196| 0.0062630| 0.0013856| 0.0036489| 0.0027613| 0.0073179| 0.0066879| 0.0092768| 0.0293446| 0.0027607| 0.0070423| 0.0153753| 0.0042167| 0.0062479| 0.0094435| 0.0026838| 0.0107557| 0.0069371| 0.0016117|
|Mast cells                       | 0.0004188| 0.0016758| 0.0000000| 0.0001857| 0.0001584| 0.0004593| 0.0009887| 0.0003855| 0.0005727| 0.0005260| 0.0007875| 0.0010431| 0.0011673| 0.0004869| 0.0009279| 0.0005938| 0.0001920| 0.0005917| 0.0008509| 0.0019108| 0.0009975| 0.0035866| 0.0015337| 0.0010433| 0.0008475| 0.0000000| 0.0004463| 0.0007555| 0.0020874| 0.0004191| 0.0001508| 0.0016117|
|Memory B cells                   | 0.0718325| 0.1288006| 0.1623523| 0.2102136| 0.0657270| 0.1740928| 0.0943247| 0.1303007| 0.2185949| 0.1743819| 0.1263191| 0.1470793| 0.1028365| 0.1212466| 0.2261656| 0.1983373| 0.1319378| 0.2333333| 0.0713070| 0.0891720| 0.0792020| 0.0808608| 0.0673313| 0.1851852| 0.0880145| 0.1884883| 0.2571684| 0.1884916| 0.1756374| 0.2120408| 0.1034535| 0.2411722|
|Monocytes/macrophages            | 0.0115183| 0.0189131| 0.0101294| 0.0120706| 0.0175800| 0.0098760| 0.0174016| 0.0080956| 0.0158457| 0.0115729| 0.0077178| 0.0116481| 0.0148243| 0.0089271| 0.0192531| 0.0124703| 0.0136355| 0.0136095| 0.0124234| 0.0133758| 0.0097756| 0.0283665| 0.0352761| 0.0125196| 0.0142857| 0.0141261| 0.0066942| 0.0107026| 0.0156553| 0.0106160| 0.0119137| 0.0115751|
|Naïve B cell-IFN                 | 0.0730890| 0.0299258| 0.0205402| 0.0189415| 0.0476718| 0.0473128| 0.0377694| 0.0516577| 0.0173730| 0.0136770| 0.0444164| 0.0027816| 0.0295319| 0.0063301| 0.0584551| 0.0071259| 0.1697715| 0.0025641| 0.0435671| 0.0321656| 0.0403990| 0.0061950| 0.1851227| 0.0028691| 0.1301453| 0.0016867| 0.0080330| 0.0045329| 0.0708215| 0.0026540| 0.0039210| 0.0030769|
|Naïve B cells                    | 0.2998953| 0.3155375| 0.1226787| 0.2909935| 0.2035160| 0.1926964| 0.2467866| 0.2812259| 0.2002673| 0.2293530| 0.3213104| 0.1898470| 0.1799930| 0.2134394| 0.2032011| 0.3452098| 0.1430766| 0.1964497| 0.3286249| 0.1703822| 0.2726185| 0.0854255| 0.1452454| 0.2670840| 0.1127119| 0.3116171| 0.1750530| 0.2618988| 0.3293574| 0.1395446| 0.4676519| 0.2150916|
|Neutrophils                      | 0.0004188| 0.0000000| 0.0000000| 0.0001857| 0.0001584| 0.0000000| 0.0007910| 0.0011565| 0.0000000| 0.0015781| 0.0006300| 0.0081711| 0.0008171| 0.0006492| 0.0009279| 0.0243468| 0.0005761| 0.0000000| 0.0003404| 0.0006369| 0.0001995| 0.0009782| 0.0004601| 0.0028691| 0.0001211| 0.0000000| 0.0000000| 0.0001259| 0.0000000| 0.0002794| 0.0085960| 0.0002930|
|NK-T cells                       | 0.0058639| 0.0035911| 0.0160383| 0.0022284| 0.0006335| 0.0039045| 0.0017797| 0.0015420| 0.0009546| 0.0023672| 0.0001575| 0.0008693| 0.0030349| 0.0017854| 0.0048713| 0.0013856| 0.0028807| 0.0017751| 0.0020422| 0.0015924| 0.0001995| 0.0026084| 0.0021472| 0.0010433| 0.0009685| 0.0023192| 0.0092603| 0.0028960| 0.0022365| 0.0051683| 0.0015081| 0.0021978|
|NK/gamma-delta T                 | 0.0071204| 0.0129279| 0.0143500| 0.0055710| 0.0033259| 0.0096463| 0.0059324| 0.0046261| 0.0053456| 0.0052604| 0.0044101| 0.0109527| 0.0073538| 0.0045447| 0.0139179| 0.0116785| 0.0076820| 0.0092702| 0.0146358| 0.0060510| 0.0025935| 0.0107597| 0.0173313| 0.0086072| 0.0047215| 0.0109635| 0.0152851| 0.0089398| 0.0093932| 0.0097779| 0.0054290| 0.0067399|
|Plasma B cells                   | 0.0332984| 0.0225042| 0.0182893| 0.0323120| 0.0259740| 0.0280202| 0.0203678| 0.0368157| 0.0211913| 0.0265650| 0.0274059| 0.0265994| 0.0283647| 0.0207758| 0.0215727| 0.0389945| 0.0243902| 0.0234714| 0.0211028| 0.0238854| 0.0243392| 0.0639061| 0.0134969| 0.0232134| 0.0277240| 0.0109635| 0.0309048| 0.0221607| 0.0310124| 0.0155050| 0.0475041| 0.0347253|
|Plasmacytoid DCs                 | 0.0027225| 0.0203495| 0.0157569| 0.0051996| 0.0063351| 0.0089573| 0.0041527| 0.0042406| 0.0061092| 0.0031562| 0.0066152| 0.0067803| 0.0107389| 0.0050317| 0.0099745| 0.0241489| 0.0078740| 0.0122288| 0.0115725| 0.0076433| 0.0025935| 0.0143463| 0.0113497| 0.0088680| 0.0049637| 0.0090660| 0.0120495| 0.0114581| 0.0043238| 0.0092192| 0.0031669| 0.0013187|
|Pre-BCRi II                      | 0.0048168| 0.0043093| 0.0011255| 0.0035283| 0.0077605| 0.0025264| 0.0045482| 0.0044333| 0.0042001| 0.0028932| 0.0070877| 0.0053894| 0.0057196| 0.0048693| 0.0074229| 0.0047506| 0.0036489| 0.0017751| 0.0020422| 0.0057325| 0.0060848| 0.0039126| 0.0009202| 0.0028691| 0.0012107| 0.0054818| 0.0013388| 0.0018887| 0.0020874| 0.0016762| 0.0003016| 0.0082051|
|Pre-T cells                      | 0.0140314| 0.0004788| 0.0008441| 0.0024141| 0.0006335| 0.0039045| 0.0126557| 0.0019275| 0.0309278| 0.0034193| 0.0000000| 0.0000000| 0.0058363| 0.0021100| 0.0000000| 0.0003959| 0.0000000| 0.0000000| 0.0015317| 0.0047771| 0.0364090| 0.0000000| 0.0001534| 0.0000000| 0.0039952| 0.0000000| 0.0000000| 0.0001259| 0.0000000| 0.0000000| 0.0000000| 0.0000000|
|T-IFN                            | 0.0178010| 0.0057458| 0.0087226| 0.0027855| 0.0079189| 0.0066605| 0.0057346| 0.0121434| 0.0013364| 0.0044713| 0.0070877| 0.0027816| 0.0126065| 0.0022724| 0.0074229| 0.0023753| 0.1229115| 0.0019724| 0.0124234| 0.0117834| 0.0133666| 0.0013042| 0.1042945| 0.0028691| 0.0602906| 0.0021084| 0.0040165| 0.0012591| 0.0113314| 0.0015365| 0.0009048| 0.0005861|
|TFH-LZ-GC                        | 0.0127749| 0.0397414| 0.1153630| 0.0228412| 0.0172632| 0.0594855| 0.0278821| 0.0287201| 0.0448645| 0.0533930| 0.0220507| 0.0759736| 0.0350181| 0.0249959| 0.0433774| 0.0530483| 0.0476282| 0.1358974| 0.0280803| 0.0452229| 0.0330175| 0.0730355| 0.0156442| 0.0234742| 0.0286925| 0.0425891| 0.1316523| 0.0582977| 0.0246012| 0.1081157| 0.0141758| 0.0987546|

Set model and design matrix

merged_obj@meta.data <- merged_obj@meta.data %>%
  mutate(age_group = case_when(
    age_years >= 1 & age_years < 6  ~ "Preschool_1to5_years",
    age_years >= 6 & age_years < 12 ~ "Kids_6to11_years",
    age_years >= 12 ~ "Adolescent_12to17_years",
    TRUE ~ "Other"))
samples_metadata <- merged_obj@meta.data %>%
  dplyr::filter(sample_id %in% unique(merged_obj@meta.data$sample_id)) %>%
  dplyr::group_by(sample_id) %>%
  dplyr::summarise(
    age = dplyr::first(age_years),
    sex = dplyr::first(sex),
    batch = dplyr::first(batch_name),
    age_group = dplyr::first(age_group),
    .groups = 'drop'  
  )
  
age <- samples_metadata$age
sex <- as.factor(samples_metadata$sex)
batch <- as.factor(samples_metadata$batch)
design <- model.matrix(~age + sex + batch)
design
   (Intercept)   age sexM batchG000231_batch8
1            1  3.62    1                   0
2            1  3.29    0                   0
3            1  6.79    0                   0
4            1  5.82    0                   0
5            1  1.64    1                   0
6            1  3.73    1                   0
7            1  2.31    1                   0
8            1  3.82    1                   0
9            1  6.67    0                   0
10           1  2.73    1                   0
11           1  5.02    0                   0
12           1  3.93    1                   0
13           1  3.76    1                   0
14           1  4.45    1                   0
15           1  5.28    0                   0
16           1 11.27    0                   0
17           1  8.90    1                   1
18           1 15.19    1                   1
19           1  2.41    1                   1
20           1  1.58    0                   1
21           1  2.86    0                   1
22           1  8.01    1                   1
23           1 11.16    1                   1
24           1  7.34    0                   1
25           1  6.42    1                   1
26           1 11.57    1                   1
27           1 10.54    0                   1
28           1 11.23    1                   1
29           1 12.75    1                   1
30           1  7.77    0                   1
31           1 13.22    0                   1
32           1 12.21    0                   1
attr(,"assign")
[1] 0 1 2 3
attr(,"contrasts")
attr(,"contrasts")$sex
[1] "contr.treatment"

attr(,"contrasts")$batch
[1] "contr.treatment"

Linear model fitting with Limma

fit <- lmFit(props$TransformedProps, design)
fit <- eBayes(fit, robust=TRUE)
coef = "age"
toptable.transformedProps <- topTable(fit, coef = coef) 
  
fit.prop <- lmFit(props$Proportions, design)
fit.prop <- eBayes(fit.prop, robust=TRUE)
toptable.props <- topTable(fit.prop, sort.by = "p", coef = 2)
  
cat(paste('### ', tissue, '\n', sep = ""))
### Adenoids
print(knitr::kable(toptable.transformedProps, caption = paste0("Transformed proportions Toptable results: ", tissue)))


Table: Transformed proportions Toptable results: Adenoids

|                                 |      logFC|   AveExpr|         t|   P.Value| adj.P.Val|         B|
|:--------------------------------|----------:|---------:|---------:|---------:|---------:|---------:|
|DZ late Sphase                   | -0.0091283| 0.1540219| -5.604706| 0.0000039| 0.0001445|  2.372546|
|DZ G2Mphase                      | -0.0067936| 0.1420384| -4.379118| 0.0001280| 0.0019915| -1.104915|
|csMBC FCRL4/5+                   | -0.0033821| 0.0714178| -4.202144| 0.0002105| 0.0019915| -1.595011|
|Memory B cells                   |  0.0145601| 0.3883601|  4.161395| 0.0002387| 0.0019915| -1.716354|
|CD4 TFH                          | -0.0085537| 0.2320508| -4.114150| 0.0002691| 0.0019915| -1.836690|
|CD8 TF                           |  0.0070714| 0.1547135|  4.046547| 0.0003248| 0.0020031| -2.021358|
|DZ early  Sphase                 | -0.0079620| 0.1512945| -3.846497| 0.0005645| 0.0029839| -2.562110|
|DZ GCB                           | -0.0053032| 0.1609978| -3.219026| 0.0030307| 0.0140171| -4.187079|
|GC-commited metabolic activation | -0.0049868| 0.0966337| -3.032760| 0.0048917| 0.0201103| -4.643030|
|Pre-T cells                      | -0.0068313| 0.0383735| -2.758206| 0.0097032| 0.0340046| -5.287862|
print(knitr::kable(toptable.props, caption = paste0("Proportions Toptable results: ", tissue)))


Table: Proportions Toptable results: Adenoids

|                       |      logFC|   AveExpr|         t|   P.Value| adj.P.Val|         B|
|:----------------------|----------:|---------:|---------:|---------:|---------:|---------:|
|DZ late Sphase         | -0.0026081| 0.0253441| -5.957600| 0.0000018| 0.0000663|  1.451431|
|CD4 TFH                | -0.0037602| 0.0550639| -4.779690| 0.0000468| 0.0008658| -1.822607|
|DZ G2Mphase            | -0.0017237| 0.0213672| -4.293384| 0.0001795| 0.0022138| -3.156687|
|csMBC FCRL4/5+         | -0.0004774| 0.0053718| -4.096114| 0.0003067| 0.0024490| -3.685954|
|Memory B cells         |  0.0100725| 0.1482938|  4.069820| 0.0003309| 0.0024490| -3.759882|
|CD8 TF                 |  0.0022942| 0.0253315|  3.824678| 0.0006427| 0.0039636| -4.410879|
|DZ early  Sphase       | -0.0022006| 0.0246557| -3.156666| 0.0037064| 0.0195908| -6.106548|
|DZ GCB                 | -0.0014881| 0.0269669| -2.967503| 0.0059633| 0.0275802| -6.559260|
|Early GC-committed NBC | -0.0007410| 0.0148863| -2.705660| 0.0112844| 0.0463915| -7.159900|
|CD4 T proliferating    | -0.0004359| 0.0062582| -2.484113| 0.0189912| 0.0702673| -7.642512|
get_age_group_color <- function(age) {
  if (age >= 1 && age <= 5) {
    return("orange")  # Preschool (1-5 years)
  } else if (age > 5 && age <= 12) {
    return("purple")  # Kids (6-11 years)
  } else if (age > 12 && age <= 17) {
    return("darkgreen")  # Adolescent (12-17 years)
  } else {
    return("black")  # Default color for other cases
  }
}
age_group_colors <- sapply(age, get_age_group_color)
  
par(mfrow=c(1,1))
for (i in rownames(toptable.transformedProps)) {
  plot(age, props$TransformedProps[i,], 
       pch=16, cex=3, ylab="Transformed Proportions", cex.lab=1.5, cex.axis=1.5,cex.main=2, col=age_group_colors)
  abline(a=fit$coefficients[i, 1], b=fit$coefficients[i, 2], col=4, lwd=2)
  title(paste0(tissue, "-", i, " : Age as Continuous"), cex.main = 1.2, adj = 0)
}

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Plot results for Sex

coef = "sexM"
toptable.transformedProps.sex <- topTable(fit, coef = coef) 


# Plot Sex
cat(paste0("#### Sex"," {.tabset}\n\n"))
#### Sex {.tabset}
  par(mfrow=c(1,2))
  for (i in rownames(toptable.transformedProps.sex)) {
    plot(sex, props$TransformedProps[i,], 
         pch=16, cex=3, ylab="Transformed Proportions", xlab="Sex", cex.lab=1.5, cex.axis=1.5,
         cex.main=2, col=c("hotpink", "darkblue"))
    abline(a=fit$coefficients[i, 1], b=fit$coefficients[i, 3], col=4, 
           lwd=2)
    title(paste0(tissue, "-", i), cex.main = 1.2, adj = 0)
}

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Interaction model for Age and Sex

design <- model.matrix(~age*sex)
design
   (Intercept)   age sexM age:sexM
1            1  3.62    1     3.62
2            1  3.29    0     0.00
3            1  6.79    0     0.00
4            1  5.82    0     0.00
5            1  1.64    1     1.64
6            1  3.73    1     3.73
7            1  2.31    1     2.31
8            1  3.82    1     3.82
9            1  6.67    0     0.00
10           1  2.73    1     2.73
11           1  5.02    0     0.00
12           1  3.93    1     3.93
13           1  3.76    1     3.76
14           1  4.45    1     4.45
15           1  5.28    0     0.00
16           1 11.27    0     0.00
17           1  8.90    1     8.90
18           1 15.19    1    15.19
19           1  2.41    1     2.41
20           1  1.58    0     0.00
21           1  2.86    0     0.00
22           1  8.01    1     8.01
23           1 11.16    1    11.16
24           1  7.34    0     0.00
25           1  6.42    1     6.42
26           1 11.57    1    11.57
27           1 10.54    0     0.00
28           1 11.23    1    11.23
29           1 12.75    1    12.75
30           1  7.77    0     0.00
31           1 13.22    0     0.00
32           1 12.21    0     0.00
attr(,"assign")
[1] 0 1 2 3
attr(,"contrasts")
attr(,"contrasts")$sex
[1] "contr.treatment"
fit <- lmFit(props$TransformedProps, design) 

cont.matrix <- cbind(
  AgeInFemales = c(0, 1, 0, 0),
  AgeInMales = c(0, 1, 0, 1),
  Diff = c(0, 0, 0, 1)
)
cont.matrix
     AgeInFemales AgeInMales Diff
[1,]            0          0    0
[2,]            1          1    0
[3,]            0          0    0
[4,]            0          1    1
fit2 <- contrasts.fit(fit, cont.matrix) 
fit2 <- eBayes(fit2)

coef = "Diff"
toptable.transformedProps <- topTable(fit2, coef = coef) 
toptable.transformedProps
                             logFC    AveExpr         t     P.Value adj.P.Val
Neutrophils           -0.008238283 0.02632626 -2.882840 0.007139463 0.2641601
Plasma B cells        -0.006036302 0.16304236 -2.267821 0.030538199 0.5649567
csMBC FCRL4/5+         0.002310903 0.07141775  1.742044 0.091523758 0.5853223
B cells                0.004615980 0.04415960  1.587945 0.122557403 0.5853223
CD8 TN                 0.006390036 0.14835309  1.584799 0.123271556 0.5853223
CD8 TF                 0.004598912 0.15471351  1.581915 0.123929182 0.5853223
CD4 TCM                0.005191868 0.13350483  1.508555 0.141647298 0.5853223
Double negative T      0.002530382 0.06317316  1.481409 0.148700545 0.5853223
CD4 T proliferating    0.002691442 0.07685101  1.456340 0.155461715 0.5853223
Monocytes/macrophages  0.003007789 0.11595110  1.439594 0.160113095 0.5853223
                              B
Neutrophils           -4.480896
Plasma B cells        -5.821961
csMBC FCRL4/5+        -6.782023
B cells               -7.024793
CD8 TN                -7.029552
CD8 TF                -7.033907
CD4 TCM               -7.142429
Double negative T     -7.181460
CD4 T proliferating   -7.216961
Monocytes/macrophages -7.240381

Save results

out <- here("output",
            "CSV_v2",
             paste0("G000231_Neeland_",tissue,".propeller.xlsx"))
if (!file.exists(out)) {
  write.xlsx(toptable.transformedProps, file = out, rowNames= T)
}

Age modelling across GC cell subsets (population)

idx <- which(merged_obj$cell_labels %in% "Germinal centre B cells")
merged_obj <- merged_obj[,idx]
merged_obj
An object of class Seurat 
17456 features across 42615 samples within 1 assay 
Active assay: RNA (17456 features, 2000 variable features)
 4 layers present: data.2, counts.2, scale.data.2, scale.data
 2 dimensional reductions calculated: pca, umap.merged

Repeat modeling steps

metadata_df <- data.frame(
  sample = merged_obj$sample_id, 
  #donor = merged_obj$donor_id,
  age_years = as.character(merged_obj$age_years),  
  cell_type = merged_obj$cell_labels_v2
)
metadata_df$age_years <- as.numeric(metadata_df$age_years)

# Calculate cell type proportions within each sample, age group, and cell type
barplot_data <- metadata_df %>%
  group_by(sample, age_years, cell_type) %>%
  summarise(n_cells = n()) %>%
  ungroup() %>%
  group_by(sample, age_years) %>%
  mutate(n_cells_total = sum(n_cells)) %>%
  ungroup() %>%
  mutate(percentage_cells = n_cells / n_cells_total)
`summarise()` has grouped output by 'sample', 'age_years'. You can override
using the `.groups` argument.
barplot_data <- barplot_data %>%
  arrange(age_years)

b <- ggplot(barplot_data, aes(x = reorder(paste(sample, age_years, sep = ":"), age_years),
                         y = percentage_cells, fill = cell_type)) +
  geom_bar(stat = "identity") +
  ggtitle(paste0("Age vs Cell Type Proportions (T cell population): ", tissue)) +
  labs(x = "Sample:Age (Years)", y = "Proportion", fill = "Cell Type") +
  scale_fill_manual(values = color_palette) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 13, hjust = 0.5, face = "bold"),
    legend.position = "top",
    axis.text.x = element_text(angle = 45, hjust = 1)
  )
b

props <- getTransformedProps(clusters = merged_obj$cell_labels_v2,
                               sample = merged_obj$sample_id, transform = "asin")
Performing arcsin square root transformation of proportions
# Plot Cell Type Mean Variance
p1 <- plotCellTypeMeanVar(props$Counts)
Using classic mode.

# Plot Cell Type Proportions Mean Variance
p2 <- plotCellTypePropsMeanVar(props$Counts)

p1 / p2
numeric(0)
print(knitr::kable(props$Proportions, caption = "Cell-type proportions (GC cell population) in samples"))


Table: Cell-type proportions (GC cell population) in samples

|                                 |      s042|      s043|      s044|      s045|      s046|      s047|      s048|      s049|      s050|      s051|      s052|      s053|      s054|      s055|      s056|      s057|      s122|      s123|      s124|      s125|      s126|      s127|      s128|      s129|      s130|      s131|      s132|      s133|      s134|      s135|      s136|      s137|
|:--------------------------------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|csMBC FCRL4/5+                   | 0.0240700| 0.0637119| 0.0249671| 0.0186386| 0.0302267| 0.0202775| 0.0284957| 0.0162602| 0.0274415| 0.0198864| 0.0234474| 0.0222822| 0.0176668| 0.0105798| 0.0168350| 0.0691244| 0.0466563| 0.0124138| 0.0276119| 0.0326087| 0.0293564| 0.0156250| 0.0289608| 0.0307167| 0.0094406| 0.1165049| 0.0267335| 0.0224467| 0.0188867| 0.0138504| 0.0178947| 0.0058939|
|Cycling GCB                      | 0.0598104| 0.0734072| 0.0328515| 0.0737439| 0.0764064| 0.0384205| 0.0841617| 0.0872136| 0.0677966| 0.0577652| 0.0576679| 0.0708980| 0.0533049| 0.0639018| 0.0729517| 0.0921659| 0.0575428| 0.0468966| 0.0671642| 0.1173913| 0.0677456| 0.3571429| 0.1925043| 0.0255973| 0.0545455| 0.1229773| 0.0167084| 0.0364759| 0.0318091| 0.0493075| 0.0568421| 0.0445318|
|DZ early  Sphase                 | 0.1072210| 0.1260388| 0.1116951| 0.0915721| 0.1263644| 0.0928495| 0.1186216| 0.0805617| 0.1138015| 0.1060606| 0.1210393| 0.0756246| 0.0828511| 0.1083369| 0.0684624| 0.0645161| 0.0668740| 0.0993103| 0.1291045| 0.1108696| 0.1117802| 0.1755952| 0.0817717| 0.0853242| 0.1230769| 0.0776699| 0.1152882| 0.0942761| 0.1103380| 0.1224377| 0.1105263| 0.0556647|
|DZ G2Mphase                      | 0.0948213| 0.1094183| 0.1077530| 0.0753647| 0.1234257| 0.1077908| 0.0927767| 0.1034738| 0.1000807| 0.0956439| 0.1007605| 0.1201891| 0.0743223| 0.0998730| 0.0763187| 0.0552995| 0.0855365| 0.0965517| 0.0940299| 0.0956522| 0.0948438| 0.0468750| 0.0459966| 0.0699659| 0.1087413| 0.0711974| 0.1094403| 0.0785634| 0.1003976| 0.1130194| 0.0915789| 0.0720367|
|DZ GCB                           | 0.1349380| 0.1038781| 0.1038108| 0.1207455| 0.1120907| 0.1366062| 0.0987409| 0.1167775| 0.1452785| 0.1278409| 0.1172370| 0.1087103| 0.1108742| 0.1544647| 0.1627385| 0.1520737| 0.1539658| 0.1255172| 0.1029851| 0.1000000| 0.1260820| 0.0714286| 0.0954003| 0.0887372| 0.1115385| 0.0938511| 0.1203008| 0.1066218| 0.1530815| 0.1141274| 0.1031579| 0.1250819|
|DZ GCB Noproli-memory like       | 0.0138585| 0.0166205| 0.0262812| 0.0307942| 0.0037783| 0.0085379| 0.0404241| 0.0133038| 0.0104923| 0.0160985| 0.0082383| 0.0236327| 0.0063966| 0.0740584| 0.0213244| 0.0368664| 0.0062208| 0.0027586| 0.0082090| 0.0054348| 0.0112909| 0.0178571| 0.0102215| 0.0153584| 0.0052448| 0.0161812| 0.0125313| 0.0078563| 0.0099404| 0.0149584| 0.0105263| 0.0137525|
|DZ late Sphase                   | 0.1254559| 0.1398892| 0.1432326| 0.0956240| 0.1335013| 0.0992529| 0.1404904| 0.1226903| 0.1162228| 0.1515152| 0.1267427| 0.1033086| 0.0862016| 0.1066441| 0.0864198| 0.0783410| 0.1213064| 0.1034483| 0.1694030| 0.1369565| 0.1223184| 0.0238095| 0.0323680| 0.1177474| 0.1220280| 0.0711974| 0.1194653| 0.0780022| 0.1252485| 0.1074792| 0.1105263| 0.0687623|
|DZtoLZ GCB transition            | 0.1466083| 0.1260388| 0.2115637| 0.2277147| 0.1381192| 0.2913554| 0.1285620| 0.2668145| 0.1743341| 0.2054924| 0.2205323| 0.2268737| 0.1553457| 0.1421921| 0.2469136| 0.1797235| 0.2970451| 0.3406897| 0.2149254| 0.1978261| 0.1987204| 0.1510417| 0.4190801| 0.3071672| 0.2849650| 0.2200647| 0.2664996| 0.3372615| 0.2445328| 0.2493075| 0.2494737| 0.3320236|
|Early GC-committed NBC           | 0.0656455| 0.0470914| 0.0525624| 0.0802269| 0.0512175| 0.0544290| 0.0556660| 0.0746489| 0.0548830| 0.0587121| 0.0671736| 0.0938555| 0.0682303| 0.0596699| 0.0572391| 0.1013825| 0.0653188| 0.0731034| 0.0589552| 0.0619565| 0.0730147| 0.0364583| 0.0408859| 0.0955631| 0.0506993| 0.0647249| 0.0902256| 0.0785634| 0.0834990| 0.0720222| 0.1157895| 0.0661428|
|Early MBC                        | 0.1590080| 0.0941828| 0.1379763| 0.0996759| 0.0965575| 0.1067236| 0.1537442| 0.0532151| 0.1339790| 0.0899621| 0.0817490| 0.1134369| 0.2713981| 0.1320355| 0.1492705| 0.1336406| 0.0637636| 0.0717241| 0.0850746| 0.1086957| 0.1065111| 0.0275298| 0.0051107| 0.0938567| 0.0730769| 0.0711974| 0.0601504| 0.1015713| 0.0844930| 0.0880886| 0.0378947| 0.1971185|
|Early PC precursor               | 0.0138585| 0.0138504| 0.0157687| 0.0089141| 0.0125945| 0.0170758| 0.0106030| 0.0184775| 0.0064568| 0.0132576| 0.0183777| 0.0202566| 0.0280231| 0.0080406| 0.0112233| 0.0046083| 0.0062208| 0.0082759| 0.0104478| 0.0097826| 0.0233346| 0.0096726| 0.0170358| 0.0238908| 0.0122378| 0.0097087| 0.0158730| 0.0162738| 0.0198807| 0.0127424| 0.0473684| 0.0117878|
|GC-commited metabolic activation | 0.0547046| 0.0858726| 0.0315375| 0.0769854| 0.0957179| 0.0266809| 0.0477137| 0.0465632| 0.0492333| 0.0577652| 0.0570342| 0.0209318| 0.0453853| 0.0402031| 0.0303030| 0.0322581| 0.0295490| 0.0193103| 0.0320896| 0.0228261| 0.0350019| 0.0669643| 0.0306644| 0.0460751| 0.0444056| 0.0647249| 0.0467836| 0.0420875| 0.0178926| 0.0426593| 0.0484211| 0.0072037|
fit <- lmFit(props$TransformedProps, design)
fit <- eBayes(fit, robust=TRUE)
toptable.transformedProps <- topTable(fit) 
Removing intercept from test coefficients
fit.prop <- lmFit(props$Proportions, design)
fit.prop <- eBayes(fit.prop, robust=TRUE)
toptable.props <- topTable(fit.prop, sort.by = "F")
Removing intercept from test coefficients
print(knitr::kable(toptable.transformedProps, caption = paste0("Transformed proportions Toptable results: ", tissue)))


Table: Transformed proportions Toptable results: Adenoids

|                                 |        age|       sexM|   age.sexM|   AveExpr|         F|   P.Value| adj.P.Val|
|:--------------------------------|----------:|----------:|----------:|---------:|---------:|---------:|---------:|
|DZtoLZ GCB transition            |  0.0094888| -0.0239776|  0.0051743| 0.4973090| 5.9046469| 0.0024982| 0.0299789|
|Early GC-committed NBC           |  0.0068340|  0.0204215| -0.0057300| 0.2614005| 3.5566912| 0.0246152| 0.1229153|
|DZ late Sphase                   | -0.0059952| -0.0053302| -0.0018078| 0.3321544| 3.3460571| 0.0307288| 0.1229153|
|Early MBC                        | -0.0018975|  0.0322420| -0.0091148| 0.3162679| 2.3354358| 0.0922760| 0.2768279|
|DZ G2Mphase                      | -0.0029690|  0.0048775| -0.0010462| 0.3046049| 1.6142842| 0.2047316| 0.4913557|
|GC-commited metabolic activation | -0.0040201|  0.0015062| -0.0004909| 0.2051218| 1.3039716| 0.2895196| 0.5530865|
|Cycling GCB                      | -0.0072293| -0.0260940|  0.0095590| 0.2656987| 1.0147792| 0.3989096| 0.5530865|
|DZ GCB Noproli-memory like       |  0.0022498|  0.0300034| -0.0060151| 0.1198400| 0.9791156| 0.4143870| 0.5530865|
|DZ early  Sphase                 | -0.0047578| -0.0173860|  0.0028707| 0.3230403| 0.9781693| 0.4148149| 0.5530865|
|csMBC FCRL4/5+                   | -0.0035594| -0.0487736|  0.0061295| 0.1598554| 0.5436824| 0.6558179| 0.7869815|
print(knitr::kable(toptable.props, caption = paste0("Proportions Toptable results: ", tissue)))


Table: Proportions Toptable results: Adenoids

|                                 |        age|       sexM|   age.sexM|   AveExpr|         F|   P.Value| adj.P.Val|
|:--------------------------------|----------:|----------:|----------:|---------:|---------:|---------:|---------:|
|DZtoLZ GCB transition            |  0.0079384| -0.0187380|  0.0045311| 0.2312128| 5.8959280| 0.0027560| 0.0330723|
|Early GC-committed NBC           |  0.0037044|  0.0115831| -0.0031146| 0.0677986| 4.5020169| 0.0100096| 0.0600578|
|DZ late Sphase                   | -0.0036712| -0.0024719| -0.0008197| 0.1089250| 3.7246549| 0.0216838| 0.0867354|
|Early MBC                        | -0.0001415|  0.0276792| -0.0059335| 0.1025754| 1.8019465| 0.1682052| 0.4182741|
|DZ G2Mphase                      | -0.0015913|  0.0034211| -0.0006199| 0.0909918| 1.7682361| 0.1742809| 0.4182741|
|GC-commited metabolic activation | -0.0014956|  0.0006809| -0.0002697| 0.0436109| 1.1459122| 0.3464400| 0.6928801|
|DZ early  Sphase                 | -0.0026563| -0.0091485|  0.0015446| 0.1020476| 0.8983355| 0.4533959| 0.7206013|
|Cycling GCB                      | -0.0033816| -0.0136728|  0.0057667| 0.0752391| 0.8316456| 0.4870624| 0.7206013|
|Early PC precursor               |  0.0009084|  0.0057230| -0.0011009| 0.0148725| 0.6578154| 0.5843738| 0.7206013|
|DZ GCB Noproli-memory like       |  0.0005529|  0.0099786| -0.0016211| 0.0162203| 0.6313186| 0.6005011| 0.7206013|
get_age_group_color <- function(age) {
  if (age >= 1 && age <= 5) {
    return("orange")  # Preschool (1-5 years)
  } else if (age > 5 && age <= 12) {
    return("purple")  # Kids (6-11 years)
  } else if (age > 12 && age <= 17) {
    return("darkgreen")  # Adolescent (12-17 years)
  } else {
    return("black")  # Default color for other cases
  }
}
age_group_colors <- sapply(age, get_age_group_color)
  
sorted_indices <- match(rownames(toptable.transformedProps), rownames(props$Proportions))

par(mfrow=c(1,1))
for (i in sorted_indices) {
  plot(age, props$Proportions[i,], 
       pch=16, cex=3, ylab="Proportions", cex.lab=1.5, cex.axis=1.5,cex.main=2, col=age_group_colors)
  abline(a=fit.prop$coefficients[i, 1], b=fit.prop$coefficients[i, 2], col=4, lwd=2)
  title(paste0(tissue, "-", rownames(props$Proportions)[i], " : Age as Continuous"), cex.main = 1.2, adj = 0)
}

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07

Version Author Date
0f07f72 Gunjan Dixit 2024-10-07 

# Plot Sex
cat(paste0("#### Sex"," {.tabset}\n\n"))
#### Sex {.tabset}
  par(mfrow=c(1,2))
  for (i in sorted_indices) {
    plot(sex, props$Proportions[i,], 
         pch=16, cex=3, ylab="Proportions", xlab="Sex", cex.lab=1.5, cex.axis=1.5,
         cex.main=2, col=c("hotpink", "darkblue"))
    abline(a=fit.prop$coefficients[i, 1], b=fit.prop$coefficients[i, 3], col=4, 
           lwd=2)
    title(paste0(tissue, "-", rownames(props$Proportions)[i]), cex.main = 1.2, adj = 0)
}

Save results for GCcell population

if (!file.exists(out)) {
  write.xlsx(toptable.transformedProps, file = out, rowNames = TRUE, sheetName = "GCcell_subclustering")
} else {
  write.xlsx(toptable.transformedProps, file = out, rowNames = TRUE, sheetName = "GCcell_subclustering", append = TRUE)
}

Session Info

sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Australia/Melbourne
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] openxlsx_4.2.5.2            knitr_1.45                 
 [3] kableExtra_1.4.0            edgeR_4.0.16               
 [5] limma_3.58.1                speckle_1.2.0              
 [7] ggridges_0.5.6              scran_1.30.2               
 [9] scuttle_1.12.0              SingleCellExperiment_1.24.0
[11] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[13] GenomicRanges_1.54.1        GenomeInfoDb_1.38.6        
[15] IRanges_2.36.0              S4Vectors_0.40.2           
[17] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[19] matrixStats_1.2.0           RColorBrewer_1.1-3         
[21] ggforce_0.4.2               viridis_0.6.5              
[23] viridisLite_0.4.2           paletteer_1.6.0            
[25] gridExtra_2.3               lubridate_1.9.3            
[27] forcats_1.0.0               stringr_1.5.1              
[29] purrr_1.0.2                 readr_2.1.5                
[31] tidyr_1.3.1                 tibble_3.2.1               
[33] ggplot2_3.5.0               tidyverse_2.0.0            
[35] dplyr_1.1.4                 Seurat_5.0.1.9009          
[37] SeuratObject_5.0.1          sp_2.1-3                   
[39] patchwork_1.2.0             glue_1.7.0                 
[41] here_1.0.1                  workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22          splines_4.3.2            
  [3] later_1.3.2               bitops_1.0-7             
  [5] polyclip_1.10-6           fastDummies_1.7.3        
  [7] lifecycle_1.0.4           rprojroot_2.0.4          
  [9] globals_0.16.2            processx_3.8.3           
 [11] lattice_0.22-5            MASS_7.3-60.0.1          
 [13] magrittr_2.0.3            plotly_4.10.4            
 [15] sass_0.4.8                rmarkdown_2.25           
 [17] jquerylib_0.1.4           yaml_2.3.8               
 [19] metapod_1.10.1            httpuv_1.6.14            
 [21] sctransform_0.4.1         zip_2.3.1                
 [23] spam_2.10-0               spatstat.sparse_3.0-3    
 [25] reticulate_1.35.0         cowplot_1.1.3            
 [27] pbapply_1.7-2             abind_1.4-5              
 [29] zlibbioc_1.48.0           Rtsne_0.17               
 [31] RCurl_1.98-1.14           tweenr_2.0.3             
 [33] git2r_0.33.0              GenomeInfoDbData_1.2.11  
 [35] ggrepel_0.9.5             irlba_2.3.5.1            
 [37] listenv_0.9.1             spatstat.utils_3.0-4     
 [39] goftest_1.2-3             RSpectra_0.16-1          
 [41] dqrng_0.3.2               spatstat.random_3.2-2    
 [43] fitdistrplus_1.1-11       parallelly_1.37.0        
 [45] svglite_2.1.3             DelayedMatrixStats_1.24.0
 [47] DelayedArray_0.28.0       leiden_0.4.3.1           
 [49] codetools_0.2-19          xml2_1.3.6               
 [51] tidyselect_1.2.0          farver_2.1.1             
 [53] ScaledMatrix_1.10.0       spatstat.explore_3.2-6   
 [55] jsonlite_1.8.8            BiocNeighbors_1.20.2     
 [57] ellipsis_0.3.2            progressr_0.14.0         
 [59] survival_3.5-8            systemfonts_1.0.5        
 [61] tools_4.3.2               ica_1.0-3                
 [63] Rcpp_1.0.12               SparseArray_1.2.4        
 [65] xfun_0.42                 withr_3.0.0              
 [67] fastmap_1.1.1             bluster_1.12.0           
 [69] fansi_1.0.6               rsvd_1.0.5               
 [71] callr_3.7.5               digest_0.6.34            
 [73] timechange_0.3.0          R6_2.5.1                 
 [75] mime_0.12                 colorspace_2.1-0         
 [77] scattermore_1.2           tensor_1.5               
 [79] spatstat.data_3.0-4       utf8_1.2.4               
 [81] generics_0.1.3            data.table_1.15.0        
 [83] httr_1.4.7                htmlwidgets_1.6.4        
 [85] S4Arrays_1.2.0            whisker_0.4.1            
 [87] uwot_0.1.16               pkgconfig_2.0.3          
 [89] gtable_0.3.4              lmtest_0.9-40            
 [91] XVector_0.42.0            htmltools_0.5.7          
 [93] dotCall64_1.1-1           scales_1.3.0             
 [95] png_0.1-8                 rstudioapi_0.15.0        
 [97] tzdb_0.4.0                reshape2_1.4.4           
 [99] nlme_3.1-164              cachem_1.0.8             
[101] zoo_1.8-12                KernSmooth_2.23-22       
[103] parallel_4.3.2            miniUI_0.1.1.1           
[105] pillar_1.9.0              grid_4.3.2               
[107] vctrs_0.6.5               RANN_2.6.1               
[109] promises_1.2.1            BiocSingular_1.18.0      
[111] beachmat_2.18.1           xtable_1.8-4             
[113] cluster_2.1.6             evaluate_0.23            
[115] locfit_1.5-9.8            cli_3.6.2                
[117] compiler_4.3.2            rlang_1.1.3              
[119] crayon_1.5.2              future.apply_1.11.1      
[121] labeling_0.4.3            rematch2_2.1.2           
[123] ps_1.7.6                  getPass_0.2-4            
[125] plyr_1.8.9                fs_1.6.3                 
[127] stringi_1.8.3             BiocParallel_1.36.0      
[129] deldir_2.0-2              munsell_0.5.0            
[131] lazyeval_0.2.2            spatstat.geom_3.2-8      
[133] Matrix_1.6-5              RcppHNSW_0.6.0           
[135] hms_1.1.3                 sparseMatrixStats_1.14.0 
[137] future_1.33.1             statmod_1.5.0            
[139] shiny_1.8.0               highr_0.10               
[141] ROCR_1.0-11               igraph_2.0.2             
[143] bslib_0.6.1              

sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Australia/Melbourne
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] openxlsx_4.2.5.2            knitr_1.45                 
 [3] kableExtra_1.4.0            edgeR_4.0.16               
 [5] limma_3.58.1                speckle_1.2.0              
 [7] ggridges_0.5.6              scran_1.30.2               
 [9] scuttle_1.12.0              SingleCellExperiment_1.24.0
[11] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[13] GenomicRanges_1.54.1        GenomeInfoDb_1.38.6        
[15] IRanges_2.36.0              S4Vectors_0.40.2           
[17] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[19] matrixStats_1.2.0           RColorBrewer_1.1-3         
[21] ggforce_0.4.2               viridis_0.6.5              
[23] viridisLite_0.4.2           paletteer_1.6.0            
[25] gridExtra_2.3               lubridate_1.9.3            
[27] forcats_1.0.0               stringr_1.5.1              
[29] purrr_1.0.2                 readr_2.1.5                
[31] tidyr_1.3.1                 tibble_3.2.1               
[33] ggplot2_3.5.0               tidyverse_2.0.0            
[35] dplyr_1.1.4                 Seurat_5.0.1.9009          
[37] SeuratObject_5.0.1          sp_2.1-3                   
[39] patchwork_1.2.0             glue_1.7.0                 
[41] here_1.0.1                  workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22          splines_4.3.2            
  [3] later_1.3.2               bitops_1.0-7             
  [5] polyclip_1.10-6           fastDummies_1.7.3        
  [7] lifecycle_1.0.4           rprojroot_2.0.4          
  [9] globals_0.16.2            processx_3.8.3           
 [11] lattice_0.22-5            MASS_7.3-60.0.1          
 [13] magrittr_2.0.3            plotly_4.10.4            
 [15] sass_0.4.8                rmarkdown_2.25           
 [17] jquerylib_0.1.4           yaml_2.3.8               
 [19] metapod_1.10.1            httpuv_1.6.14            
 [21] sctransform_0.4.1         zip_2.3.1                
 [23] spam_2.10-0               spatstat.sparse_3.0-3    
 [25] reticulate_1.35.0         cowplot_1.1.3            
 [27] pbapply_1.7-2             abind_1.4-5              
 [29] zlibbioc_1.48.0           Rtsne_0.17               
 [31] RCurl_1.98-1.14           tweenr_2.0.3             
 [33] git2r_0.33.0              GenomeInfoDbData_1.2.11  
 [35] ggrepel_0.9.5             irlba_2.3.5.1            
 [37] listenv_0.9.1             spatstat.utils_3.0-4     
 [39] goftest_1.2-3             RSpectra_0.16-1          
 [41] dqrng_0.3.2               spatstat.random_3.2-2    
 [43] fitdistrplus_1.1-11       parallelly_1.37.0        
 [45] svglite_2.1.3             DelayedMatrixStats_1.24.0
 [47] DelayedArray_0.28.0       leiden_0.4.3.1           
 [49] codetools_0.2-19          xml2_1.3.6               
 [51] tidyselect_1.2.0          farver_2.1.1             
 [53] ScaledMatrix_1.10.0       spatstat.explore_3.2-6   
 [55] jsonlite_1.8.8            BiocNeighbors_1.20.2     
 [57] ellipsis_0.3.2            progressr_0.14.0         
 [59] survival_3.5-8            systemfonts_1.0.5        
 [61] tools_4.3.2               ica_1.0-3                
 [63] Rcpp_1.0.12               SparseArray_1.2.4        
 [65] xfun_0.42                 withr_3.0.0              
 [67] fastmap_1.1.1             bluster_1.12.0           
 [69] fansi_1.0.6               rsvd_1.0.5               
 [71] callr_3.7.5               digest_0.6.34            
 [73] timechange_0.3.0          R6_2.5.1                 
 [75] mime_0.12                 colorspace_2.1-0         
 [77] scattermore_1.2           tensor_1.5               
 [79] spatstat.data_3.0-4       utf8_1.2.4               
 [81] generics_0.1.3            data.table_1.15.0        
 [83] httr_1.4.7                htmlwidgets_1.6.4        
 [85] S4Arrays_1.2.0            whisker_0.4.1            
 [87] uwot_0.1.16               pkgconfig_2.0.3          
 [89] gtable_0.3.4              lmtest_0.9-40            
 [91] XVector_0.42.0            htmltools_0.5.7          
 [93] dotCall64_1.1-1           scales_1.3.0             
 [95] png_0.1-8                 rstudioapi_0.15.0        
 [97] tzdb_0.4.0                reshape2_1.4.4           
 [99] nlme_3.1-164              cachem_1.0.8             
[101] zoo_1.8-12                KernSmooth_2.23-22       
[103] parallel_4.3.2            miniUI_0.1.1.1           
[105] pillar_1.9.0              grid_4.3.2               
[107] vctrs_0.6.5               RANN_2.6.1               
[109] promises_1.2.1            BiocSingular_1.18.0      
[111] beachmat_2.18.1           xtable_1.8-4             
[113] cluster_2.1.6             evaluate_0.23            
[115] locfit_1.5-9.8            cli_3.6.2                
[117] compiler_4.3.2            rlang_1.1.3              
[119] crayon_1.5.2              future.apply_1.11.1      
[121] labeling_0.4.3            rematch2_2.1.2           
[123] ps_1.7.6                  getPass_0.2-4            
[125] plyr_1.8.9                fs_1.6.3                 
[127] stringi_1.8.3             BiocParallel_1.36.0      
[129] deldir_2.0-2              munsell_0.5.0            
[131] lazyeval_0.2.2            spatstat.geom_3.2-8      
[133] Matrix_1.6-5              RcppHNSW_0.6.0           
[135] hms_1.1.3                 sparseMatrixStats_1.14.0 
[137] future_1.33.1             statmod_1.5.0            
[139] shiny_1.8.0               highr_0.10               
[141] ROCR_1.0-11               igraph_2.0.2             
[143] bslib_0.6.1