flashier_pbmc_purified
Matthew Stephens
2024-04-04
Last updated: 2024-04-04
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Knit directory: misc/analysis/
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|---|---|---|---|---|
| Rmd | cb16e70 | Matthew Stephens | 2024-04-04 | workflowr::wflow_publish("flashier_pbmc_purified.Rmd") |
Introduction
I run flashier (sparse EBNMF) on the purified pbmcs. I use some code from Eric Weine to plot the results.
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(tidyr)
library(purrr)
library(stringr)
library(Matrix)
Attaching package: 'Matrix'The following objects are masked from 'package:tidyr':
expand, pack, unpacklibrary(flashier)Loading required package: ebnmLoading required package: magrittr
Attaching package: 'magrittr'The following object is masked from 'package:purrr':
set_namesThe following object is masked from 'package:tidyr':
extractlibrary(ggplot2)Load the data (in a sparse matrix called counts)
load("../data/pbmc_purified.RData")lcounts = counts
lcounts@x = log(counts@x + 1)Run flashier
fit1 = flash(lcounts,S = 0.01, ebnm_fn = ebnm_point_exponential, var_type = 2)Adding factor 1 to flash object...
Adding factor 2 to flash object...
Adding factor 3 to flash object...
Adding factor 4 to flash object...
Adding factor 5 to flash object...
Adding factor 6 to flash object...
Adding factor 7 to flash object...
Adding factor 8 to flash object...
Adding factor 9 to flash object...
Adding factor 10 to flash object...
Adding factor 11 to flash object...
Adding factor 12 to flash object...
Adding factor 13 to flash object...
Adding factor 14 to flash object...
Adding factor 15 to flash object...
Adding factor 16 to flash object...
Adding factor 17 to flash object...
Adding factor 18 to flash object...
Adding factor 19 to flash object...
Factor doesn't significantly increase objective and won't be added.
Wrapping up...
Done.
Nullchecking 18 factors...
Done.saveRDS(fit1,file='../output/fit.nn.pbmc.purified.rds')Note that the first factor is “intercept-like”, capturing library size and some kind of background expression,
plot(fit1$L_pm[,1],rowSums(lcounts),main="loadings for factor 1 vs rowSums of lcounts")
Here I am using Eric’s code to plot a heatmap of the results, ordered by cell type
FF_log1p = fit1$L_pm
FF_log1p <- scale(FF_log1p, center = FALSE, scale = apply(FF_log1p, 2, max))
colnames(FF_log1p) <- paste0("k", 1:ncol(FF_log1p))
cell.type <- samples$celltype
# Downsample the number of cells and sort them using tSNE.
set.seed(8675309)
cell.idx <- numeric(0)
cell.types <- levels(cell.type)
for (i in 1:length(cell.types)) {
which.idx <- which(cell.type == cell.types[i])
# Downsample common cell types.
if (length(which.idx) > 1250) {
which.idx <- sample(which.idx, 1250)
}
# Don't include rare cell types.
if (length(which.idx) > 20) {
# Sort using tsne.
tsne.res <- Rtsne::Rtsne(
FF_log1p[which.idx, ],
dims = 1,
pca = FALSE,
normalize = FALSE,
perplexity = min(100, floor((length(which.idx) - 1) / 3) - 1),
theta = 0.1,
max_iter = 1000,
eta = 200,
check_duplicates = FALSE
)$Y[, 1]
which.idx <- which.idx[order(tsne.res)]
cell.idx <- c(cell.idx, which.idx)
}
}
cell.type <- cell.type[cell.idx]
cell.type <- droplevels(cell.type)
FF_log1p <- FF_log1p[cell.idx, ]
make.heatmap.tib <- function(FF) {
tib <- as_tibble(scale(FF, center = FALSE, scale = apply(FF, 2, max))) %>%
mutate(Cell.type = cell.type) %>%
arrange(Cell.type) %>%
mutate(Cell.idx = row_number())
tib <- tib %>%
pivot_longer(
-c(Cell.idx, Cell.type),
names_to = "Factor",
values_to = "Loading",
values_drop_na = TRUE
) %>%
mutate(Factor = as.numeric(str_extract(Factor, "[0-9]+")))
return(tib)
}
log1p_tib <- make.heatmap.tib(FF_log1p)
heatmap.tib = log1p_tib
tib <- heatmap.tib %>%
group_by(Cell.type, Cell.idx) %>%
summarize()`summarise()` has grouped output by 'Cell.type'. You can override using the
`.groups` argument.cell_type_breaks <- c(1, which(tib$Cell.type[-1] != tib$Cell.type[-nrow(tib)]))
label_pos <- cell_type_breaks / 2 + c(cell_type_breaks[-1], nrow(tib)) / 2
plt <- ggplot(heatmap.tib, aes(x = Factor, y = -Cell.idx, fill = Loading)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "firebrick") +
labs(y = "") +
scale_y_continuous(breaks = -label_pos,
minor_breaks = NULL,
labels = levels(cell.type)) +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_minimal() +
geom_hline(yintercept = -cell_type_breaks, size = 0.1) +
theme(legend.position = "none",
strip.text = element_text(size = 16)) Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.plt
List driving genes:
for(k in 1:18){
print(paste0("factor ",k))
print(genes$symbol[order(fit1$F_pm[,k],decreasing =TRUE)[1:50]])
}[1] "factor 1"
[1] "MALAT1" "RPL13" "RPS2" "RPL10" "RPL13A" "RPS6" "RPS18" "B2M"
[9] "RPS4X" "RPL3" "RPS27" "RPS3" "RPS14" "TMSB4X" "RPL32" "RPS19"
[17] "RPL11" "RPS12" "RPL18A" "RPLP1" "RPL19" "RPLP2" "RPL21" "RPS3A"
[25] "RPS15" "RPS15A" "RPS27A" "RPL15" "RPL28" "RPL12" "RPL23A" "RPL18"
[33] "RPL31" "RPS8" "TMSB10" "RPL34" "RPS23" "RPL27A" "RPL26" "RPS9"
[41] "RPL6" "RPL8" "RPL29" "RPL7" "RPL10A" "RPS7" "RPL9" "RPL35A"
[49] "RPS5" "RPS16"
[1] "factor 2"
[1] "AIF1" "ZFAS1" "RP11-620J15.3" "PRSS57"
[5] "SERPINB1" "SNHG7" "HSP90AB1" "H2AFY"
[9] "TALDO1" "SPINK2" "CYTL1" "SOX4"
[13] "EIF3E" "TXN" "UQCRB" "GPX1"
[17] "TKT" "SNHG8" "EBPL" "STMN1"
[21] "GSTP1" "UQCRH" "LYL1" "HLA-DRA"
[25] "KIAA0125" "MT-ND3" "SNRPE" "HMGA1"
[29] "LSM7" "LST1" "HINT1" "CAT"
[33] "FCER1A" "IMPDH2" "EGFL7" "LINC00493"
[37] "SNRPD1" "NUCB2" "LSMD1" "RNF130"
[41] "C19orf77" "HIST1H4C" "APEX1" "NGFRAP1"
[45] "ANP32B" "HMGB1" "COX6B1" "RPL39"
[49] "SNRPF" "C6orf48"
[1] "factor 3"
[1] "RPL13" "RPL10" "RPS2" "RPL13A" "RPS6" "RPL3" "MALAT1" "B2M"
[9] "RPS3" "TMSB4X" "RPL32" "RPS4X" "RPL18A" "RPS18" "RPL11" "RPS27"
[17] "RPS14" "RPL21" "EEF1A1" "RPL19" "RPS19" "RPS12" "LTB" "RPLP2"
[25] "JUNB" "RPS3A" "RPL8" "RPL18" "RPL15" "RPS5" "RPL12" "RPL6"
[33] "TMSB10" "RPS15" "RPS9" "RPL10A" "RPL23A" "HLA-B" "RPS8" "RPS25"
[41] "RPL9" "RPL14" "RPS27A" "RPL31" "RPS15A" "RPL29" "RPS16" "RPL28"
[49] "RPL4" "RPLP1"
[1] "factor 4"
[1] "GNLY" "NKG7" "GZMB" "GZMA" "TYROBP" "CCL5" "CST7"
[8] "FCER1G" "CLIC3" "HCST" "CTSW" "KLRB1" "FGFBP2" "IFITM2"
[15] "MALAT1" "PRF1" "CD7" "HOPX" "CYBA" "B2M" "PFN1"
[22] "PLAC8" "CD247" "HLA-C" "GZMH" "CALM1" "GZMM" "FCGR3A"
[29] "SPON2" "HLA-B" "KLRF1" "HLA-A" "MYL12A" "SRGN" "ACTB"
[36] "TMSB4X" "EFHD2" "ITGB2" "CCL4" "PTPRCAP" "IFITM1" "KLRD1"
[43] "RARRES3" "HLA-E" "CD63" "ID2" "SERF2" "RAC2" "UBB"
[50] "ATP5E"
[1] "factor 5"
[1] "S100A9" "S100A8" "LYZ" "CST3" "TYROBP" "HLA-DRB1"
[7] "S100A4" "S100A6" "CD74" "FTL" "LGALS1" "HLA-DRA"
[13] "HLA-DRB5" "FTH1" "LST1" "AIF1" "FCER1G" "FCN1"
[19] "HLA-DPB1" "SAT1" "PSAP" "CYBA" "GPX1" "TYMP"
[25] "S100A11" "TSPO" "GSTP1" "CTSS" "HLA-DPA1" "FOS"
[31] "COTL1" "PYCARD" "HLA-DQB1" "CFD" "S100A10" "ATP5E"
[37] "MYL6" "HLA-DQA1" "LGALS2" "CEBPD" "OAZ1" "CD14"
[43] "FCGRT" "NEAT1" "TMEM176B" "KLF6" "LINC00936" "GRN"
[49] "NPC2" "HLA-DMA"
[1] "factor 6"
[1] "TUBA1B" "HMGB2" "H2AFZ" "STMN1" "TUBB" "TYMS"
[7] "KIAA0101" "PCNA" "TK1" "H2AFV" "RRM2" "HN1"
[13] "RAN" "TUBB4B" "DUT" "SNRPB" "CARHSP1" "CENPM"
[19] "LDHA" "PTTG1" "UBE2C" "ANXA2" "RANBP1" "PPIA"
[25] "CKS1B" "HIST1H4C" "DDX39A" "CALM3" "COX8A" "PPP1CA"
[31] "MCM7" "NUSAP1" "DEK" "HNRNPF" "BIRC5" "H3F3A"
[37] "GAPDH" "PSMA7" "SIVA1" "PSME2" "LGALS1" "ZWINT"
[43] "TPI1" "COTL1" "LSM4" "HMGB1" "CCDC167" "CACYBP"
[49] "CRIP1" "DTYMK"
[1] "factor 7"
[1] "IL32" "S100A4" "ACTB" "SH3BGRL3" "PFN1" "LTB"
[7] "HLA-A" "CD52" "S100A10" "CD3D" "B2M" "VIM"
[13] "HLA-C" "CORO1A" "JUNB" "ACTG1" "HLA-B" "UBC"
[19] "S100A6" "CD3E" "CFL1" "GAPDH" "COTL1" "AES"
[25] "GSTK1" "ARPC1B" "ARHGDIB" "EMP3" "PTPRCAP" "TMSB4X"
[31] "CD99" "IER2" "CALM1" "FXYD5" "ANXA2" "CD2"
[37] "AQP3" "EVL" "IL2RG" "LCK" "CRIP1" "LGALS1"
[43] "HLA-E" "MYL12A" "HSPA8" "UCP2" "FTH1" "ISG20"
[49] "LAT" "CD27"
[1] "factor 8"
[1] "CD74" "HLA-DRA" "HLA-DRB1" "HLA-DPB1" "CD79A" "HLA-DPA1"
[7] "CD79B" "HLA-DRB5" "CD37" "HLA-DQA1" "MS4A1" "HLA-DQA2"
[13] "HLA-DQB1" "LTB" "CYBA" "TCL1A" "IGLL5" "HLA-DMA"
[19] "CD52" "RPS11" "HLA-DMB" "MALAT1" "NCF1" "LY86"
[25] "LINC00926" "VPREB3" "ARPC3" "OAZ1" "MT-CO1" "FCER2"
[31] "BANK1" "HVCN1" "MT-CO3" "PTPRCAP" "CYB561A3" "LAPTM5"
[37] "FAU" "BLK" "P2RX5" "EAF2" "TSC22D3" "SPIB"
[43] "IRF8" "POLD4" "RPLP1" "HLA-DOB" "FAM26F" "PPAPDC1B"
[49] "RPS23" "PDLIM1"
[1] "factor 9"
[1] "CD8B" "RP11-291B21.2" "S100B" "RPS29"
[5] "HCST" "CD8A" "RPL36A" "RPS21"
[9] "RPL38" "RPS28" "CD7" "CD3D"
[13] "RPL37A" "RPL37" "RPL34" "RGS10"
[17] "MT-CO2" "RPL41" "NOSIP" "CARS"
[21] "MT-ND2" "CTSW" "RPL27" "RPS26"
[25] "RPS24" "GZMM" "RPL23" "RPL22"
[29] "RPS10" "CD3E" "RPL36" "CPA5"
[33] "MT-ND4" "TMA7" "EEF1B2" "RPS25"
[37] "GYPC" "RPL31" "NPM1" "TOMM7"
[41] "CD27" "RPL36AL" "MT-CYB" "PFDN5"
[45] "RPS13" "RPL35A" "RPS20" "HSPB1"
[49] "COMMD6" "RPL24"
[1] "factor 10"
[1] "SPINK2" "KIAA0125" "CD74" "HOPX" "LST1" "PRSS1"
[7] "IGJ" "HLA-DRA" "AIF1" "ZFAS1" "SNHG7" "ITM2C"
[13] "GSTP1" "TSC22D1" "ACY3" "PRSS3" "GPX1" "HLA-DPB1"
[19] "HOXA9" "C9orf89" "CALM2" "DNTT" "GNG11" "SOX4"
[25] "HLA-DPA1" "ATP5E" "MZB1" "ATP5L" "KLF6" "CIRBP"
[31] "CD99" "ENO1" "ADA" "FOS" "RPS24" "RNASEH2B"
[37] "CTSD" "HLA-DRB1" "ID2" "ACTG1" "CXXC5" "SPNS3"
[43] "CD34" "GSN" "NDUFA4" "KIAA0087" "PLP2" "PNISR"
[49] "DENND6B" "CAPG"
[1] "factor 11"
[1] "BTG1" "GIMAP7" "TMEM66" "LDHB" "DDX5" "PABPC1"
[7] "SELL" "NOSIP" "CD3E" "JUNB" "CCR7" "CD7"
[13] "CD27" "LEF1" "RPL4" "LTB" "GYPC" "LIMD2"
[19] "HSPA8" "TXNIP" "GPSM3" "EEF1A1" "EEF2" "TRAF3IP3"
[25] "PIK3IP1" "CNBP" "AES" "GLTSCR2" "ARHGDIB" "EEF1D"
[31] "LCK" "MAL" "HNRNPA1" "NPM1" "ITM2B" "HNRNPA2B1"
[37] "EVL" "GIMAP4" "GIMAP5" "EIF3L" "CORO1A" "LAT"
[43] "EIF4A2" "CALM3" "RGS10" "LITAF" "GIMAP1" "CCNI"
[49] "ZFP36L2" "RPL5"
[1] "factor 12"
[1] "GZMK" "CCL5" "IL32" "DUSP1" "GZMA" "NKG7"
[7] "JUN" "DUSP2" "LYAR" "HCST" "NFKBIA" "CST7"
[13] "ZFP36" "HLA-B" "B2M" "HLA-C" "FOS" "S100A4"
[19] "BTG1" "CXCR4" "HLA-A" "CTSW" "MALAT1" "ID2"
[25] "EIF1" "RPL36AL" "CD69" "TMSB4X" "ZFP36L2" "SH3BGRL3"
[31] "RPS29" "H3F3B" "TSC22D3" "S100A6" "GZMM" "MT-ND2"
[37] "IL7R" "RPL23A" "KLF6" "PFN1" "PPP1R15A" "RPS27"
[43] "CD99" "ARL4C" "KLRG1" "CALM1" "PTPRCAP" "RPS26"
[49] "RPL41" "NCR3"
[1] "factor 13"
[1] "S100A8" "S100A9" "FTL" "TMSB4X" "RPLP1" "TMSB10"
[7] "S100A4" "RPS14" "RPS9" "FTH1" "RPS2" "RPL13"
[13] "S100A6" "RPL13A" "MT-CO1" "FAU" "TYROBP" "RPS28"
[19] "HLA-B" "COX4I1" "RPL34" "MT-ND1" "CYBA" "TSPO"
[25] "RPLP2" "RPL26" "MT-CO2" "RPS24" "RPL41" "SERF2"
[31] "RPS15" "RPL28" "UBA52" "GPX1" "CD14" "RPL19"
[37] "AIF1" "RPL37A" "PFDN5" "JUND" "LINC00936" "RPL12"
[43] "EIF1" "ATP5E" "S100A12" "MYL6" "RPS19" "OAZ1"
[49] "MT-ATP6" "IER2"
[1] "factor 14"
[1] "TPSAB1" "LMO4" "S100A6" "APOC1"
[5] "CD63" "HDC" "CNRIP1" "RP11-620J15.3"
[9] "PRSS57" "S100A4" "ALOX5AP" "MS4A3"
[13] "EXD3" "SOX4" "C4orf48" "GATA2"
[17] "HPGD" "RPS21" "LINC00152" "SEC61B"
[21] "RPL37A" "TMEM258" "RPL38" "RPL23"
[25] "RPS24" "SRGN" "CLC" "POLR2L"
[29] "PPIB" "UQCRQ" "TIMP1" "MTDH"
[33] "FCER1A" "CSF2RB" "LTC4S" "FKBP2"
[37] "SAT1" "MT-ND3" "FBXO7" "NDUFB11"
[41] "ATPIF1" "SMIM1" "BACE2" "COX7C"
[45] "GLUL" "MT-ATP6" "MT-ND1" "PNMT"
[49] "ATP5L" "C12orf57"
[1] "factor 15"
[1] "MPO" "ELANE" "IGLL1" "AZU1"
[5] "PRSS57" "C1QTNF4" "CFD" "PRTN3"
[9] "CLEC11A" "C19orf77" "HSPB1" "MT-ND3"
[13] "RP11-620J15.3" "IGFBP2" "PLAC8" "MGST1"
[17] "SYNGR1" "RPL38" "VAMP5" "MT-ATP6"
[21] "RPS21" "NEAT1" "MCL1" "CTSG"
[25] "MRP63" "TIMM13" "ROMO1" "PPIB"
[29] "TMEM258" "IGFBP7" "NAP1L1" "SNRPF"
[33] "ATP5G2" "RPL37A" "SNRPD1" "ATOX1"
[37] "DDX21" "CSF3R" "CEBPD" "RASGRP2"
[41] "RNF181" "AC002454.1" "JUND" "TIMM8B"
[45] "RP11-304L19.5" "CEBPA" "H1FX" "MGST2"
[49] "RPL23" "A1BG"
[1] "factor 16"
[1] "CLC" "RPS20" "RPL37A" "EXD3"
[5] "JUND" "SLC45A3" "RAB37" "RPL24"
[9] "ACTN4" "RPL37" "HOXA5" "GATA2"
[13] "SHFM1" "CPT1A" "RUNX1" "SAMD1"
[17] "COA3" "TMEM258" "RPS11" "CTD-3064H18.1"
[21] "MT-ND3" "ILF3" "MT-ND1" "MT-ATP6"
[25] "SLC27A5" "RPS24" "APOC2" "CAPNS1"
[29] "LTC4S" "ZNF738" "FAM53C" "NMT2"
[33] "SAR1A" "INPPL1" "VGLL4" "KPNB1"
[37] "FURIN" "CTCF" "AFF1" "LSM14B"
[41] "LMO4" "MKNK2" "SLC25A29" "METRNL"
[45] "AC004381.6" "RAB11FIP1" "DNAJC21" "MAST4"
[49] "CXCL3" "C2orf43"
[1] "factor 17"
[1] "MT-ND2" "MT-CO2" "RPL37A" "RPL34" "RPL37" "RPL38" "RPS24"
[8] "RPS21" "RPS28" "MT-ND4" "MT-ATP6" "RPS29" "MT-CYB" "RPL27"
[15] "RPL26" "RPL35A" "RPS27A" "RPLP2" "RPS23" "RPS27" "RPL36"
[22] "MT-CO3" "RPS15A" "RPL36A" "RPL35" "RPS20" "RPS26" "RPL31"
[29] "RPLP1" "TOMM7" "MT-ND1" "RPL27A" "RPL30" "RPS12" "FAU"
[36] "RPL28" "RPL24" "RPS11" "RPL23A" "RPL23" "RPS15" "RPS19"
[43] "UBA52" "RPL7" "RPL36AL" "RPS14" "RPL21" "MT-ND5" "RPS18"
[50] "MALAT1"
[1] "factor 18"
[1] "ZNF683" "CCL5" "CD52" "S100A4" "SH3BGRL3" "GZMH"
[7] "GNLY" "MALAT1" "CTSW" "NKG7" "HCST" "CD8A"
[13] "LYAR" "IL32" "S100A6" "HLA-C" "DUSP1" "TMSB4X"
[19] "HLA-B" "CD99" "LGALS1" "B2M" "IFITM2" "S100A10"
[25] "FGFBP2" "HLA-A" "CYBA" "JUN" "PFN1" "CD3G"
[31] "CD3D" "PPP1R15A" "DUSP2" "PPP2R5C" "UBB" "NCR3"
[37] "CD8B" "PTPRC" "CST7" "HOPX" "PTPRCAP" "FLNA"
[43] "TPST2" "C12orf75" "CLIC1" "GUK1" "MYL6" "KLRC2"
[49] "CITED2" "EML4" I tried backfitting but it gave a memory error. We should look into that.
#fit2 = flash_backfit(fit1)
sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.4.4 flashier_1.0.7 magrittr_2.0.3 ebnm_1.1-2 Matrix_1.6-4
[6] stringr_1.5.1 purrr_1.0.2 tidyr_1.3.0 dplyr_1.1.4
loaded via a namespace (and not attached):
[1] Rcpp_1.0.12 horseshoe_0.2.0 invgamma_1.1 lattice_0.22-5
[5] rprojroot_2.0.4 digest_0.6.33 utf8_1.2.4 truncnorm_1.0-9
[9] R6_2.5.1 evaluate_0.23 highr_0.10 pillar_1.9.0
[13] rlang_1.1.2 rstudioapi_0.15.0 irlba_2.3.5.1 whisker_0.4.1
[17] jquerylib_0.1.4 rmarkdown_2.25 splines_4.2.1 Rtsne_0.17
[21] munsell_0.5.0 mixsqp_0.3-54 compiler_4.2.1 httpuv_1.6.13
[25] xfun_0.41 pkgconfig_2.0.3 SQUAREM_2021.1 htmltools_0.5.7
[29] tidyselect_1.2.0 tibble_3.2.1 workflowr_1.7.1 fansi_1.0.6
[33] crayon_1.5.2 withr_3.0.0 later_1.3.2 grid_4.2.1
[37] jsonlite_1.8.8 gtable_0.3.4 lifecycle_1.0.4 git2r_0.33.0
[41] scales_1.3.0 cli_3.6.2 stringi_1.8.3 cachem_1.0.8
[45] farver_2.1.1 fs_1.6.3 promises_1.2.1 bslib_0.6.1
[49] generics_0.1.3 vctrs_0.6.5 trust_0.1-8 tools_4.2.1
[53] glue_1.6.2 softImpute_1.4-1 parallel_4.2.1 fastmap_1.1.1
[57] yaml_2.3.8 colorspace_2.1-0 ashr_2.2-63 deconvolveR_1.2-1
[61] knitr_1.45 sass_0.4.8