Figure 1
Last updated: 2021-12-17
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Read datassets
datasets <- readr::read_csv(here::here("data", "datasets.csv"), col_types = readr::cols())
datasets$datatype <- factor(datasets$datatype, levels = c("technical-control", "cell line", "heterogeneous"))
datasets <- datasets %>% arrange(datatype)Read and process poisson GLM results
QVALUE_THRESHOLD <- 1e-2
ncells <- "1000"
residual_type <- "quantile"
nb_column <- paste0("qval_ssr", "_", residual_type)
ssr_column <- paste0("ssr", "_", residual_type, "_normalized")
root_dir <- here::here("output/snakemake_output/poisson_glm_output", ncells)
fits <- GetFits(root_dir, qval_col = nb_column)
fits_dfx <- fits[["fits"]]
frac_dfx <- fits[["frac_df"]]
cell_attrs <- fits[["cell_attrs"]]
gene_attrs <- fits[["gene_attrs"]]
fits_df <- ProcessFits(fits_dfx, datasets, nb_column)
frac_df <- left_join(frac_dfx, datasets, by = "key")Fraction of non-poisson genes
frac_df_subset <- frac_df[, c(
"sample_name", "datatype",
"median_gene_avg_umi", "median_cell_total_umi", "nonpoisson_fraction"
)] %>% arrange(nonpoisson_fraction)
frac_df_subset <- frac_df_subset %>% mutate(across(where(is.numeric), round, 3))
kbl(frac_df_subset, booktabs = T) %>%
kable_styling(latex_options = "striped")| sample_name | datatype | median_gene_avg_umi | median_cell_total_umi | nonpoisson_fraction |
|---|---|---|---|---|
| TechCtrl1 (ChromiumV1) | technical-control | 0.007 | 2264.5 | 0.001 |
| TechCtrl2 (ChromiumV1) | technical-control | 0.007 | 2165.0 | 0.004 |
| TechCtrl (inDrops) | technical-control | 0.365 | 32905.0 | 0.005 |
| PBMC-r2 (Seq-Well) | heterogeneous | 0.009 | 521.0 | 0.007 |
| HEK-m (ChromiumV2_sn) | cell line | 0.171 | 4967.0 | 0.008 |
| PBMC-r1 (inDrops) | heterogeneous | 0.010 | 798.0 | 0.009 |
| PBMC68k (ChromiumV1) | heterogeneous | 0.011 | 1513.0 | 0.010 |
| HEK-m (inDrops) | cell line | 0.108 | 2943.0 | 0.010 |
| PBMC-r1 (Drop-seq) | heterogeneous | 0.012 | 1554.5 | 0.015 |
| PBMC-r2 (CEL-seq2) | heterogeneous | 0.069 | 5917.0 | 0.016 |
| PBMC-r2 (ChromiumV2) | heterogeneous | 0.021 | 2850.5 | 0.016 |
| PBMC-r1 (Seq-Well) | heterogeneous | 0.011 | 1200.0 | 0.017 |
| HEK-r1 (inDrops) | cell line | 0.042 | 3132.0 | 0.017 |
| PBMC-r1 (ChromiumV2A) | heterogeneous | 0.016 | 2384.0 | 0.018 |
| PBMC-r2 (inDrops) | heterogeneous | 0.018 | 1690.0 | 0.019 |
| PBMC-r1 (ChromiumV2B) | heterogeneous | 0.019 | 3286.0 | 0.020 |
| PBMC-r1 (CEL-seq2) | heterogeneous | 0.071 | 6848.0 | 0.020 |
| HEK-r2 (inDrops) | cell line | 0.052 | 3973.0 | 0.026 |
| 3T3-r1 (inDrops) | cell line | 0.051 | 2255.0 | 0.027 |
| PBMC-r2 (Drop-seq) | heterogeneous | 0.017 | 2486.5 | 0.027 |
| 3T3-r2 (Drop-seq) | cell line | 0.080 | 3434.0 | 0.031 |
| Cortex-r2 (ChromiumV2) | heterogeneous | 0.042 | 3980.5 | 0.037 |
| HEK-r1 (Drop-seq) | cell line | 0.053 | 6015.5 | 0.038 |
| 3T3-r1 (Drop-seq) | cell line | 0.072 | 3152.5 | 0.038 |
| HEK-r2 (Drop-seq) | cell line | 0.048 | 6312.5 | 0.040 |
| 3T3-r2 (inDrops) | cell line | 0.107 | 4666.5 | 0.042 |
| Cortex-r2 (sci-RNA-seq) | heterogeneous | 0.029 | 3501.5 | 0.045 |
| PBMC-r1 (ChromiumV3) | heterogeneous | 0.029 | 5839.5 | 0.046 |
| Cortex-r1 (DroNc-seq) | heterogeneous | 0.043 | 3013.5 | 0.049 |
| 3T3-r1 (sci-RNA-seq) | cell line | 0.188 | 6609.5 | 0.062 |
| PBMC (ChromiumV3) | heterogeneous | 0.050 | 6992.0 | 0.063 |
| 3T3 (ChromiumV3) | cell line | 0.072 | 16184.5 | 0.066 |
| Cortex-r2 (DroNc-seq) | heterogeneous | 0.050 | 3094.0 | 0.067 |
| Cortex-r1 (ChromiumV2) | heterogeneous | 0.065 | 7132.5 | 0.071 |
| HEK-r1 (sci-RNA-seq) | cell line | 0.220 | 11490.0 | 0.071 |
| Cortex-r1 (sci-RNA-seq) | heterogeneous | 0.031 | 4421.0 | 0.074 |
| Bone Marrow (CITE-seq) | heterogeneous | 0.093 | 12585.0 | 0.111 |
| HEK (ChromiumV3) | cell line | 0.069 | 41054.5 | 0.115 |
| HEK-m (Drop-seq) | cell line | 0.109 | 1907.5 | 0.118 |
| HEK-m (MARS-seq) | cell line | 0.235 | 11207.5 | 0.131 |
| Fetal (sci-RNA-seq3) | heterogeneous | 0.030 | 6975.5 | 0.134 |
| 3T3-r2 (ChromiumV2) | cell line | 0.185 | 14057.0 | 0.142 |
| 3T3-r1 (ChromiumV2) | cell line | 0.151 | 11834.0 | 0.143 |
| 3T3-r2 (sci-RNA-seq) | cell line | 0.161 | 9199.0 | 0.151 |
| HEK-r2 (sci-RNA-seq) | cell line | 0.096 | 13298.0 | 0.153 |
| HEK-r2 (CEL-seq2) | cell line | 0.389 | 43670.0 | 0.159 |
| HEK-r1 (CEL-seq2) | cell line | 0.615 | 52973.0 | 0.161 |
| HEK-r1 (ChromiumV2) | cell line | 0.108 | 25193.5 | 0.162 |
| HEK-r2 (ChromiumV2) | cell line | 0.096 | 23444.0 | 0.169 |
| 3T3-r1 (CEL-seq2) | cell line | 0.616 | 34291.0 | 0.172 |
| HEK-m (mcSCRB-seq) | cell line | 0.121 | 3266.5 | 0.175 |
| 3T3-r2 (CEL-seq2) | cell line | 1.045 | 53036.0 | 0.220 |
| HEK (Smart-seq3) | cell line | 0.286 | 106996.0 | 0.233 |
| HEK-m (CEL-seq2) | cell line | 0.788 | 60592.5 | 0.242 |
| PBMC (Smart-seq3) | heterogeneous | 0.032 | 9487.5 | 0.396 |
| HEK-m (ddSeq) | cell line | 0.205 | 5304.5 | 0.572 |
| HEK-m (ChromiumV2) | cell line | 0.710 | 73333.5 | 0.616 |
| Fibroblasts (Smart-seq3) | heterogeneous | 0.526 | 197151.0 | 0.628 |
| HEK-m (Quartz-Seq2) | cell line | 1.576 | 167199.0 | 0.647 |
dir.create(here::here("output", "tables"), showWarnings = F)
print(xtable(frac_df_subset, type = "latex", digits=3), include.rownames = FALSE, file = here::here("output/tables/fraction_nonpoisson.tex"))Medium to high mean non-poisson genes
medium_high_gene_pois <- list()
for (sample_name in unique(fits_df$sample_name)) {
df <- fits_df[fits_df$sample_name == sample_name, ]
med_abundance <- df[(df$mean > 1),]# & (df$mean <= 10), ]
med_abundance_nonpois <- med_abundance[med_abundance$gene_type != "Pass", ]
ratio <- dim(med_abundance_nonpois)[1] / dim(med_abundance)[1]
medium_high_gene_pois[[sample_name]] <- data.frame(ratio = ratio, n_med_high_nonpoisson = dim(med_abundance_nonpois)[1], n_med_high = dim(med_abundance)[1], n_all = dim(df)[1])
}
medium_high_gene_pois <- bind_rows(medium_high_gene_pois, .id = "sample_name")
kbl(medium_high_gene_pois, booktabs = T) %>%
kable_styling(latex_options = "striped")| sample_name | ratio | n_med_high_nonpoisson | n_med_high | n_all |
|---|---|---|---|---|
| TechCtrl1 (ChromiumV1) | 0.1200000 | 18 | 150 | 20167 |
| TechCtrl2 (ChromiumV1) | 0.5869565 | 81 | 138 | 20838 |
| TechCtrl (inDrops) | 0.0248205 | 159 | 6406 | 25255 |
| 3T3 (ChromiumV3) | 0.5779857 | 1297 | 2244 | 21920 |
| 3T3-r1 (CEL-seq2) | 0.4772430 | 2915 | 6108 | 15457 |
| 3T3-r1 (ChromiumV2) | 0.8497268 | 1555 | 1830 | 15390 |
| 3T3-r1 (Drop-seq) | 0.6440072 | 360 | 559 | 13904 |
| 3T3-r1 (inDrops) | 0.8231293 | 242 | 294 | 13337 |
| 3T3-r1 (sci-RNA-seq) | 0.5419847 | 852 | 1572 | 12806 |
| 3T3-r2 (CEL-seq2) | 0.5329213 | 3966 | 7442 | 14620 |
| 3T3-r2 (ChromiumV2) | 0.7731538 | 1895 | 2451 | 15127 |
| 3T3-r2 (Drop-seq) | 0.5073314 | 346 | 682 | 13977 |
| 3T3-r2 (inDrops) | 0.5365854 | 484 | 902 | 12354 |
| 3T3-r2 (sci-RNA-seq) | 0.8871124 | 1831 | 2064 | 15608 |
| HEK (ChromiumV3) | 0.6261718 | 3273 | 5227 | 26012 |
| HEK (Smart-seq3) | 0.7987632 | 7621 | 9541 | 27097 |
| HEK-r1 (CEL-seq2) | 0.5012410 | 4241 | 8461 | 19628 |
| HEK-r1 (ChromiumV2) | 0.8884120 | 3105 | 3495 | 21809 |
| HEK-r1 (Drop-seq) | 0.5241277 | 706 | 1347 | 19149 |
| HEK-r1 (inDrops) | 0.6119048 | 257 | 420 | 17091 |
| HEK-r1 (sci-RNA-seq) | 0.5590200 | 1757 | 3143 | 17299 |
| HEK-r2 (CEL-seq2) | 0.5554204 | 4109 | 7398 | 20294 |
| HEK-r2 (ChromiumV2) | 0.9317073 | 3056 | 3280 | 22655 |
| HEK-r2 (Drop-seq) | 0.6626617 | 717 | 1082 | 20614 |
| HEK-r2 (inDrops) | 0.6451613 | 400 | 620 | 17862 |
| HEK-r2 (sci-RNA-seq) | 0.9238095 | 2813 | 3045 | 23608 |
| HEK-m (CEL-seq2) | 0.7002563 | 6831 | 9755 | 21665 |
| HEK-m (ChromiumV2) | 0.9770573 | 9071 | 9284 | 21669 |
| HEK-m (ChromiumV2_sn) | 0.1847826 | 153 | 828 | 16502 |
| HEK-m (ddSeq) | 0.9952525 | 2306 | 2317 | 18354 |
| HEK-m (Drop-seq) | 0.9874804 | 631 | 639 | 15794 |
| HEK-m (inDrops) | 0.3333333 | 174 | 522 | 13118 |
| HEK-m (MARS-seq) | 0.8183222 | 1590 | 1943 | 18091 |
| HEK-m (mcSCRB-seq) | 0.9375000 | 1125 | 1200 | 13760 |
| HEK-m (Quartz-Seq2) | 0.9835809 | 11921 | 12120 | 21535 |
| Cortex-r1 (ChromiumV2) | 0.7914110 | 903 | 1141 | 22947 |
| Cortex-r1 (DroNc-seq) | 0.9146758 | 268 | 293 | 21965 |
| Cortex-r1 (sci-RNA-seq) | 0.9776248 | 568 | 581 | 22188 |
| Cortex-r2 (ChromiumV2) | 0.8120301 | 324 | 399 | 22920 |
| Cortex-r2 (DroNc-seq) | 0.9171843 | 443 | 483 | 21183 |
| Cortex-r2 (sci-RNA-seq) | 0.9878788 | 326 | 330 | 22492 |
| Fibroblasts (Smart-seq3) | 0.9940432 | 10680 | 10744 | 24003 |
| PBMC-r1 (CEL-seq2) | 0.4236902 | 372 | 878 | 19767 |
| PBMC-r1 (ChromiumV2A) | 0.9073171 | 186 | 205 | 21706 |
| PBMC-r1 (ChromiumV2B) | 0.8710801 | 250 | 287 | 21922 |
| PBMC-r1 (ChromiumV3) | 0.8287938 | 426 | 514 | 22851 |
| PBMC-r1 (Drop-seq) | 0.8833333 | 159 | 180 | 21292 |
| PBMC-r1 (inDrops) | 0.9696970 | 64 | 66 | 17972 |
| PBMC-r1 (Seq-Well) | 0.9624060 | 128 | 133 | 21785 |
| PBMC-r2 (CEL-seq2) | 0.4237288 | 275 | 649 | 19705 |
| PBMC-r2 (ChromiumV2) | 0.8448276 | 196 | 232 | 22283 |
| PBMC-r2 (Drop-seq) | 0.9475983 | 217 | 229 | 23577 |
| PBMC-r2 (inDrops) | 0.9693252 | 158 | 163 | 19143 |
| PBMC-r2 (Seq-Well) | 0.9264706 | 63 | 68 | 15088 |
| PBMC68k (ChromiumV1) | 0.8364780 | 133 | 159 | 19955 |
| PBMC (ChromiumV3) | 0.7335025 | 578 | 788 | 17853 |
| PBMC (Smart-seq3) | 0.9746622 | 1154 | 1184 | 28768 |
| Fetal (sci-RNA-seq3) | 0.7037037 | 19 | 27 | 46844 |
| Bone Marrow (CITE-seq) | 0.9523810 | 240 | 252 | 17009 |
print(xtable(medium_high_gene_pois, digits=3), include.rownames = FALSE, digits=3, file = here::here("output/tables/medium_high_expr_nonpois_fraction.tex"))Plot fraction of non-poisson vs gene abundance
nonpoismeanwise <- GetMeanwiseNonPois(fits_df)
nonpoismeanwise <- left_join(nonpoismeanwise, datasets, by = "key")
nonpoismeanwise_df <- nonpoismeanwise[complete.cases(nonpoismeanwise), ]
nonpoismeanwise_df$sample_name <- factor(nonpoismeanwise_df$sample_name, levels = datasets$sample_name)
nonpoismeanwise_filtered <- nonpoismeanwise_df[nonpoismeanwise_df$n_total_genes >= 10, ]
plot.dots <- ggplot(nonpoismeanwise_filtered, aes(sample_name, mean_quantile,
color = nonpoisson_fraction,
size = nonpoisson_fraction
)) +
geom_point() +
scale_color_viridis_c(
direction = -1,
breaks = c(0.25, 0.5, 0.75, 1),
name = "Non-poisson fraction"
) +
scale_size(
name = "Non-poisson fraction",
breaks = c(0.25, 0.5, 0.75, 1),
range = c(0.5, 5)
) +
theme_pubr(base_size = 10) +
theme(legend.position = "right", legend.direction = "horizontal") +
guides(color = guide_legend(), size = guide_legend()) +
guides(x = guide_axis(angle = 90)) +
xlab("") +
ylab("Gene mean") +
theme(legend.position = c(0.8, 1.06))
plot.dots
| Version | Author | Date |
|---|---|---|
| 400797a | Saket Choudhary | 2021-07-06 |
nonpoismeanwise_filtered_subset <- nonpoismeanwise_filtered[, c("sample_name", "mean_quantile", "nonpoisson_fraction")]
nonpoismeanwise_filtered_subset2 <- tidyr::spread(nonpoismeanwise_filtered_subset, mean_quantile, nonpoisson_fraction)
kbl(nonpoismeanwise_filtered_subset2, booktabs = T) %>%
kable_styling(latex_options = "striped")| sample_name | <0.01 | >0.01 | >0.1 | >1 | >5 | >10 | >25 | >50 | >100 |
|---|---|---|---|---|---|---|---|---|---|
| TechCtrl1 (ChromiumV1) | 0.0001579 | NA | NA | 0.0083333 | 0.1000000 | 0.7000000 | NA | NA | NA |
| TechCtrl2 (ChromiumV1) | 0.0003051 | 0.0001568 | 0.0214168 | 0.5000000 | NA | 0.9285714 | NA | NA | NA |
| TechCtrl (inDrops) | NA | NA | 0.0002956 | 0.0051442 | 0.0369069 | 0.1264822 | 0.4102564 | 0.6734694 | 0.9285714 |
| 3T3 (ChromiumV3) | 0.0015412 | 0.0064665 | 0.0675734 | 0.4989396 | 0.9889503 | 1.0000000 | 1.0000000 | 1.0000000 | NA |
| 3T3-r1 (CEL-seq2) | 0.0071702 | 0.0073620 | 0.1178304 | 0.3713582 | 0.8368984 | 0.9785933 | 1.0000000 | 1.0000000 | 1.0000000 |
| 3T3-r1 (ChromiumV2) | 0.0039175 | 0.0120156 | 0.1991565 | 0.8229234 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | NA |
| 3T3-r1 (Drop-seq) | 0.0018663 | 0.0010408 | 0.0565152 | 0.6059406 | 1.0000000 | 1.0000000 | NA | NA | NA |
| 3T3-r1 (inDrops) | 0.0008782 | 0.0010646 | 0.0478577 | 0.7944664 | 1.0000000 | 1.0000000 | NA | NA | NA |
| 3T3-r1 (sci-RNA-seq) | 0.0015835 | 0.0024155 | 0.0617210 | 0.5055021 | 0.9873418 | 1.0000000 | 1.0000000 | NA | NA |
| 3T3-r2 (CEL-seq2) | 0.0091116 | 0.0083042 | 0.0990764 | 0.3896331 | 0.7749627 | 0.9550225 | 1.0000000 | 1.0000000 | 1.0000000 |
| 3T3-r2 (ChromiumV2) | 0.0081105 | 0.0076309 | 0.1400062 | 0.7306202 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | NA |
| 3T3-r2 (Drop-seq) | 0.0012251 | 0.0006667 | 0.0386980 | 0.4577922 | 0.9534884 | 1.0000000 | NA | NA | NA |
| 3T3-r2 (inDrops) | 0.0009862 | 0.0007578 | 0.0272644 | 0.4793492 | 0.9710145 | 1.0000000 | NA | NA | NA |
| 3T3-r2 (sci-RNA-seq) | 0.0043967 | 0.0082210 | 0.2092370 | 0.8754677 | 1.0000000 | 1.0000000 | 1.0000000 | NA | NA |
| HEK (ChromiumV3) | 0.0027539 | 0.0061889 | 0.0778216 | 0.5137038 | 0.9701493 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 |
| HEK (Smart-seq3) | 0.0119471 | 0.0116812 | 0.1745152 | 0.7025160 | 0.9428066 | 0.9871324 | 1.0000000 | 1.0000000 | 1.0000000 |
| HEK-r1 (CEL-seq2) | NA | 0.0085905 | 0.0954722 | 0.3765109 | 0.7530529 | 0.9382911 | 1.0000000 | 1.0000000 | 1.0000000 |
| HEK-r1 (ChromiumV2) | 0.0048426 | 0.0168411 | 0.1570022 | 0.8579235 | 1.0000000 | 1.0000000 | 1.0000000 | NA | 1.0000000 |
| HEK-r1 (Drop-seq) | 0.0007688 | 0.0027243 | 0.0353954 | 0.4715581 | 1.0000000 | 1.0000000 | 1.0000000 | NA | NA |
| HEK-r1 (inDrops) | 0.0005487 | 0.0011022 | 0.0197816 | 0.5421348 | 1.0000000 | 1.0000000 | NA | NA | NA |
| HEK-r1 (sci-RNA-seq) | 0.0047764 | 0.0069048 | 0.0778780 | 0.5189962 | 0.9710145 | 1.0000000 | NA | NA | NA |
| HEK-r2 (CEL-seq2) | 0.0051769 | 0.0078385 | 0.0873059 | 0.4496459 | 0.9004065 | 0.9888641 | 1.0000000 | 1.0000000 | 1.0000000 |
| HEK-r2 (ChromiumV2) | 0.0060403 | 0.0195405 | 0.2029444 | 0.9128744 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | NA |
| HEK-r2 (Drop-seq) | 0.0012235 | 0.0034984 | 0.0587045 | 0.6209761 | 1.0000000 | 1.0000000 | 1.0000000 | NA | NA |
| HEK-r2 (inDrops) | 0.0008364 | 0.0025515 | 0.0270036 | 0.5903166 | 1.0000000 | 1.0000000 | NA | NA | NA |
| HEK-r2 (sci-RNA-seq) | 0.0055562 | 0.0186279 | 0.2636884 | 0.9166966 | 1.0000000 | 1.0000000 | NA | NA | NA |
| HEK-m (CEL-seq2) | NA | 0.0181054 | 0.1380288 | 0.6144760 | 0.9327381 | 0.9932660 | 1.0000000 | 1.0000000 | 1.0000000 |
| HEK-m (ChromiumV2) | 0.1739130 | 0.1944444 | 0.6612867 | 0.9702465 | 0.9977238 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 |
| HEK-m (ChromiumV2_sn) | NA | NA | 0.0053493 | 0.1718171 | 0.7333333 | NA | NA | NA | NA |
| HEK-m (ddSeq) | 0.2278481 | 0.4828010 | 0.9124015 | 0.9945972 | 1.0000000 | 1.0000000 | NA | NA | NA |
| HEK-m (Drop-seq) | 0.0038835 | 0.0125822 | 0.2866858 | 0.9855856 | 1.0000000 | 1.0000000 | NA | NA | NA |
| HEK-m (inDrops) | NA | NA | 0.0078245 | 0.2494331 | 0.6909091 | 1.0000000 | NA | NA | NA |
| HEK-m (MARS-seq) | NA | 0.0101597 | 0.2094223 | 0.7965318 | 0.9916667 | 1.0000000 | 1.0000000 | NA | NA |
| HEK-m (mcSCRB-seq) | NA | 0.0066553 | 0.3362846 | 0.9304267 | 1.0000000 | 1.0000000 | NA | NA | NA |
| HEK-m (Quartz-Seq2) | 0.1176471 | 0.1485840 | 0.5538624 | 0.9636866 | 0.9993586 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 |
| Cortex-r1 (ChromiumV2) | 0.0014268 | 0.0094961 | 0.1243224 | 0.7659784 | 1.0000000 | 1.0000000 | NA | NA | NA |
| Cortex-r1 (DroNc-seq) | 0.0013364 | 0.0070822 | 0.2039531 | 0.9094203 | 1.0000000 | NA | NA | NA | NA |
| Cortex-r1 (sci-RNA-seq) | 0.0016481 | 0.0116785 | 0.2611836 | 0.9734694 | 1.0000000 | 1.0000000 | NA | NA | NA |
| Cortex-r2 (ChromiumV2) | 0.0010207 | 0.0062638 | 0.1050808 | 0.7905028 | 1.0000000 | 1.0000000 | NA | NA | NA |
| Cortex-r2 (DroNc-seq) | 0.0019376 | 0.0071465 | 0.2034228 | 0.9124726 | 1.0000000 | NA | NA | NA | NA |
| Cortex-r2 (sci-RNA-seq) | 0.0006347 | 0.0088618 | 0.2202040 | 0.9862069 | 1.0000000 | NA | NA | NA | NA |
| Fibroblasts (Smart-seq3) | 0.0450250 | 0.2737935 | 0.7973094 | 0.9859772 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.0000000 |
| PBMC-r1 (CEL-seq2) | 0.0009934 | 0.0012436 | 0.0168625 | 0.3147139 | 0.9594595 | 1.0000000 | NA | NA | NA |
| PBMC-r1 (ChromiumV2A) | 0.0003506 | 0.0041029 | 0.1083984 | 0.8461538 | 0.9800000 | 1.0000000 | NA | NA | NA |
| PBMC-r1 (ChromiumV2B) | 0.0004798 | 0.0023103 | 0.0792907 | 0.7988827 | 0.9756098 | 1.0000000 | 1.0000000 | NA | NA |
| PBMC-r1 (ChromiumV3) | 0.0013785 | 0.0125000 | 0.1441478 | 0.7710526 | 0.9772727 | 1.0000000 | 1.0000000 | NA | NA |
| PBMC-r1 (Drop-seq) | 0.0000798 | 0.0019028 | 0.1227169 | 0.8618421 | 1.0000000 | NA | NA | NA | NA |
| PBMC-r1 (inDrops) | 0.0003344 | 0.0026207 | 0.1887417 | 0.9666667 | NA | NA | NA | NA | NA |
| PBMC-r1 (Seq-Well) | 0.0006708 | 0.0037138 | 0.2056680 | 0.9586777 | NA | NA | NA | NA | NA |
| PBMC-r2 (CEL-seq2) | NA | 0.0005705 | 0.0127430 | 0.3000000 | 0.9508197 | 1.0000000 | NA | NA | NA |
| PBMC-r2 (ChromiumV2) | 0.0003797 | 0.0011815 | 0.0609636 | 0.7445255 | 0.9756098 | 1.0000000 | NA | NA | NA |
| PBMC-r2 (Drop-seq) | 0.0009164 | 0.0041455 | 0.2072072 | 0.9215686 | 1.0000000 | 1.0000000 | NA | NA | NA |
| PBMC-r2 (inDrops) | 0.0007112 | 0.0051697 | 0.1436637 | 0.9557522 | 1.0000000 | 1.0000000 | NA | NA | NA |
| PBMC-r2 (Seq-Well) | 0.0002448 | NA | 0.1003344 | 0.9180328 | NA | NA | NA | NA | NA |
| PBMC68k (ChromiumV1) | 0.0002984 | 0.0024876 | 0.0626072 | 0.7368421 | 0.9772727 | 1.0000000 | NA | NA | NA |
| PBMC (ChromiumV3) | 0.0016126 | 0.0098021 | 0.1115682 | 0.6650485 | 0.9464286 | 1.0000000 | 1.0000000 | 1.0000000 | NA |
| PBMC (Smart-seq3) | 0.0311889 | 0.3590994 | 0.8952417 | 0.9704433 | 1.0000000 | 1.0000000 | 1.0000000 | NA | NA |
| Fetal (sci-RNA-seq3) | 0.1162671 | 0.6176148 | 0.9902142 | NA | NA | NA | NA | NA | NA |
| Bone Marrow (CITE-seq) | 0.0086455 | 0.0720512 | 0.5624242 | 0.9315068 | 0.9354839 | 1.0000000 | 1.0000000 | NA | NA |
print(xtable(nonpoismeanwise_filtered_subset2, type = "latex", digits=3), digits=3, include.rownames = FALSE, file = here::here("output/tables/nonpoisson_meanwise.tex"))Plot residual variance
sample_names <- c(
"TechCtrl (inDrops)", "TechCtrl1 (ChromiumV1)",
"HEK (ChromiumV3)",
"PBMC (Smart-seq3)"
)
fits_df_subset <- fits_df[fits_df$sample_name %in% sample_names, ]
plot.glmresid4 <- PlotGLMResiduals(fits_df_subset, ssr_column, 4)
plot.glmresid4
| Version | Author | Date |
|---|---|---|
| 400797a | Saket Choudhary | 2021-07-06 |
Distribution of means of poisson and non-poisson genes
plot.kde <- PlotKDEPois(fits_df)
plot.kde
| Version | Author | Date |
|---|---|---|
| 400797a | Saket Choudhary | 2021-07-06 |
dir.create(here::here("output", "figures"), showWarnings = F, recursive = T)
ggsave(here::here("output", "figures", paste0("01_PoissonKDE_ncells_", ncells, "_residtype_", residual_type, ".pdf")), width = 12, height = 10)Plot fraction of non-poisson
plot.poisvsumi <- PlotPoisVsUMI.cell_line(frac_df)
plot.poisvsumix <- plot.poisvsumi + theme(legend.position = c(0.5, 1.0))
plot.poisvsumix
| Version | Author | Date |
|---|---|---|
| 400797a | Saket Choudhary | 2021-07-06 |
Downsampling
sample_name <- "PBMC__Smart-seq3"
root_dir <- here::here(paste0("output/snakemake_output/", sample_name, "_sampled_counts/poisson_glm_output/", ncells))
downsampled_umis <- c(
10000, 7500, 5000,
2000, 1000
)
names(downsampled_umis)<- c(
"10,000", "7,500", "5,000",
"2,000", "1,000"
)
sampled_dataset_keys <- paste0(sample_name, "_sampled_", downsampled_umis)
sampled_counts <- list()
for (name in downsampled_umis) {
seu <- readRDS(here::here("data", "sampled_counts", paste0(sample_name, "_sampled_counts"), paste0(sample_name, "_sampled_", name, ".rds")))
sampled_counts[[as.character(name)]] <- GetAssayData(seu, assay = "RNA", slot = "counts")
}
sampled <- readRDS(here::here("data", "rds_filtered", paste0(sample_name, ".rds")))
counts <- GetAssayData(sampled, assay = "RNA", slot = "counts")
counts_totalumis <- colSums2(counts)
generated_poissons <- list()
PredictPoisson <- function(counts_df) {
predicted_poisson <- dpois(counts_df$counts, lambda = mean(counts_df$counts))
return(predicted_poisson)
}
goi <- c("RPS19", "TPT1")
names(goi) <- goi
PredictPoisson <- function(counts_df) {
predicted_poisson <- dpois(counts_df$counts, lambda = mean(counts_df$counts))
return(predicted_poisson)
}
med_libsize <- "8,288" #(median(colSums2(counts)))
for (gene_name in names(goi)) {
gene <- goi[[gene_name]]
c1 <- "5000"
c2 <- "1000"
counts_goi_full <- counts[gene, ]
counts_goi_full_subset <- counts_goi_full[counts_goi_full > 1]
counts_goi_5k <- sampled_counts[[c1]][gene, ]
counts_goi_1k <- sampled_counts[[c2]][gene, ]
counts_df_full <- data.frame(counts = counts_goi_full_subset)
counts_df_5k <- data.frame(counts = counts_goi_5k)
counts_df_1k <- data.frame(counts = counts_goi_1k)
labels <- c(
paste0("Original ", med_libsize, " UMI/cell"),
paste0("Sampled ", so_formatter(as.integer(c1)), " UMI/cell"),
paste0("Sampled ", so_formatter(as.integer(c2)), " UMI/cell")
)
counts_df_full$predicted_poisson <- PredictPoisson(counts_df_full)
counts_df_full$sampletype <- labels[1]
counts_df_5k$predicted_poisson <- PredictPoisson(counts_df_5k)
counts_df_5k$sampletype <- labels[2]
counts_df_1k$predicted_poisson <- PredictPoisson(counts_df_1k)
counts_df_1k$sampletype <- labels[3]
merged_counts <- rbind(counts_df_full, counts_df_5k, counts_df_1k)
merged_counts$sampletype <- factor(merged_counts$sampletype, levels = labels)
generated_poissons[[gene_name]] <- merged_counts
}
generated_poissons_df <- bind_rows(generated_poissons, .id = "gene")
plot.genespois <- ggplot(generated_poissons_df, aes(x = counts)) +
geom_histogram(aes(y = ..density..), binwidth = 1, colour = "gray", fill = "white") +
geom_line(aes(counts, predicted_poisson), color = "red") +
facet_wrap(gene ~ sampletype, scales = "free", ncol = 3) +
xlab("Counts") +
ylab("Density") +
scale_y_continuous(breaks = c(0.00, 0.10, 0.20))
plot.genespois
| Version | Author | Date |
|---|---|---|
| 400797a | Saket Choudhary | 2021-07-06 |
ncells <- "1000"
residual_type <- "quantile"
nb_column <- paste0("qval_ssr", "_", residual_type)
ssr_column <- paste0("ssr", "_", residual_type, "_normalized")
results_df <- GetFits.sampled(root_dir, nb_column)
fits_df.sampledx <- results_df$fits
frac_df.sampled <- results_df$frac_df
fits_df.sampled <- ProcessFits.sampled(fits_df.sampledx, nb_column)
fits_df.sampled$umi <- stringr::str_split_fixed(fits_df.sampled$key, pattern = "_", n = 5)[, 5]
frac_df.sampled$umi <- stringr::str_split_fixed(frac_df.sampled$key, pattern = "_", n = 5)[, 5]
fits_df.sampled$umi <- factor(fits_df.sampled$umi, levels = as.character(downsampled_umis), labels = paste0("Sampled ", names(downsampled_umis), " UMI/cell"))
frac_df.sampled$umi <- factor(frac_df.sampled$umi, levels = as.character(downsampled_umis), labels = paste0("Sampled ", names(downsampled_umis), " UMI/cell"))
fits_df.sampled$sample_name <- fits_df.sampled$umi
fits_df.sampled$datatype <- "heterogeneous"
fits_df.sampled$datatype <- factor(fits_df.sampled$datatype, levels = c("technical-control", "cell line", "heterogeneous"))
fits_df.sampled.subset <- fits_df.sampled[!is.na(fits_df.sampled[, ssr_column]), ]plot.glmresiddownsample <- PlotGLMResiduals.sampled(fits_df.sampled, y = ssr_column, ncol = 5) + ylab(paste0("Variance of ", residual_type, " residuals"))
plot.glmresiddownsample
| Version | Author | Date |
|---|---|---|
| 400797a | Saket Choudhary | 2021-07-06 |
frac_df.sampled.subset <- frac_df.sampled[, c(
"umi", "median_cell_total_umi", "median_gene_total_umi",
"median_gene_avg_umi", "nonpoisson_fraction"
)]
kbl(frac_df.sampled.subset, booktabs = T) %>%
kable_styling(latex_options = "striped")| umi | median_cell_total_umi | median_gene_total_umi | median_gene_avg_umi | nonpoisson_fraction |
|---|---|---|---|---|
| Sampled 10,000 UMI/cell | 9998 | 29 | 0.0366162 | 0.4672177 |
| Sampled 7,500 UMI/cell | 7500 | 28 | 0.0280000 | 0.3952419 |
| Sampled 5,000 UMI/cell | 5000 | 23 | 0.0230000 | 0.2820475 |
| Sampled 2,000 UMI/cell | 2000 | 14 | 0.0140000 | 0.0224812 |
| Sampled 1,000 UMI/cell | 999 | 11 | 0.0110000 | 0.0047246 |
print(xtable(frac_df.sampled.subset, type = "latex", digits=3), include.rownames = FALSE, file=here::here("output", "tables", "frac_nonpoisson_sampled.tex"))plot.poisvsumi <- PlotPoisVsUMI.cell_line(frac_df) + # theme(legend.position = "top")
theme(legend.position = c(0.5, 1.04))
layout <- "
AAAA
BBBC
DDDD
EEEE
"
plot.dots +
plot.glmresid4 + plot.poisvsumi +
plot.genespois + plot.glmresiddownsample +
plot_layout(design = layout, tag_level = "new") + plot_annotation(tag_levels = "A") & theme(plot.tag = element_text(face = "bold"))
| Version | Author | Date |
|---|---|---|
| 400797a | Saket Choudhary | 2021-07-06 |
ggsave(here::here("output", "figures", "01_Figure1.pdf"), width = 12, height = 15)sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] xtable_1.8-4 sparseMatrixStats_1.4.2 MatrixGenerics_1.4.3
[4] matrixStats_0.61.0 SeuratObject_4.0.4 Seurat_4.0.5
[7] scattermore_0.7 reshape2_1.4.4 readr_2.1.1
[10] RColorBrewer_1.1-2 patchwork_1.1.1 kableExtra_1.3.4
[13] here_1.0.1 ggridges_0.5.3 ggpubr_0.4.0
[16] ggplot2_3.3.5 dplyr_1.0.7 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] backports_1.4.1 systemfonts_1.0.3 plyr_1.8.6
[4] igraph_1.2.9 lazyeval_0.2.2 splines_4.1.2
[7] listenv_0.8.0 digest_0.6.29 htmltools_0.5.2
[10] fansi_0.5.0 magrittr_2.0.1 tensor_1.5
[13] cluster_2.1.2 ROCR_1.0-11 tzdb_0.2.0
[16] globals_0.14.0 vroom_1.5.7 svglite_2.0.0
[19] spatstat.sparse_2.0-0 colorspace_2.0-2 rvest_1.0.2
[22] ggrepel_0.9.1 xfun_0.28 crayon_1.4.2
[25] jsonlite_1.7.2 spatstat.data_2.1-0 survival_3.2-13
[28] zoo_1.8-9 glue_1.5.1 polyclip_1.10-0
[31] gtable_0.3.0 webshot_0.5.2 leiden_0.3.9
[34] car_3.0-12 future.apply_1.8.1 abind_1.4-5
[37] scales_1.1.1 DBI_1.1.1 rstatix_0.7.0
[40] miniUI_0.1.1.1 Rcpp_1.0.7 viridisLite_0.4.0
[43] reticulate_1.22 spatstat.core_2.3-2 bit_4.0.4
[46] htmlwidgets_1.5.4 httr_1.4.2 ellipsis_0.3.2
[49] ica_1.0-2 farver_2.1.0 pkgconfig_2.0.3
[52] sass_0.4.0 uwot_0.1.11 deldir_1.0-6
[55] utf8_1.2.2 labeling_0.4.2 tidyselect_1.1.1
[58] rlang_0.4.12 later_1.3.0 munsell_0.5.0
[61] tools_4.1.2 generics_0.1.1 broom_0.7.10
[64] evaluate_0.14 stringr_1.4.0 fastmap_1.1.0
[67] yaml_2.2.1 goftest_1.2-3 knitr_1.36
[70] bit64_4.0.5 fs_1.5.2 fitdistrplus_1.1-6
[73] purrr_0.3.4 RANN_2.6.1 pbapply_1.5-0
[76] future_1.23.0 nlme_3.1-152 whisker_0.4
[79] mime_0.12 xml2_1.3.3 compiler_4.1.2
[82] rstudioapi_0.13 plotly_4.10.0 png_0.1-7
[85] ggsignif_0.6.3 spatstat.utils_2.3-0 tibble_3.1.6
[88] bslib_0.3.1 stringi_1.7.6 highr_0.9
[91] lattice_0.20-45 Matrix_1.4-0 vctrs_0.3.8
[94] pillar_1.6.4 lifecycle_1.0.1 spatstat.geom_2.3-1
[97] lmtest_0.9-39 jquerylib_0.1.4 RcppAnnoy_0.0.19
[100] data.table_1.14.2 cowplot_1.1.1 irlba_2.3.5
[103] httpuv_1.6.3 R6_2.5.1 promises_1.2.0.1
[106] KernSmooth_2.23-20 gridExtra_2.3 parallelly_1.29.0
[109] codetools_0.2-18 MASS_7.3-54 assertthat_0.2.1
[112] rprojroot_2.0.2 withr_2.4.3 sctransform_0.3.2.9008
[115] mgcv_1.8-38 parallel_4.1.2 hms_1.1.1
[118] grid_4.1.2 rpart_4.1-15 tidyr_1.1.4
[121] rmarkdown_2.11 carData_3.0-4 Rtsne_0.15
[124] git2r_0.29.0 shiny_1.7.1
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] xtable_1.8-4 sparseMatrixStats_1.4.2 MatrixGenerics_1.4.3
[4] matrixStats_0.61.0 SeuratObject_4.0.4 Seurat_4.0.5
[7] scattermore_0.7 reshape2_1.4.4 readr_2.1.1
[10] RColorBrewer_1.1-2 patchwork_1.1.1 kableExtra_1.3.4
[13] here_1.0.1 ggridges_0.5.3 ggpubr_0.4.0
[16] ggplot2_3.3.5 dplyr_1.0.7 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] backports_1.4.1 systemfonts_1.0.3 plyr_1.8.6
[4] igraph_1.2.9 lazyeval_0.2.2 splines_4.1.2
[7] listenv_0.8.0 digest_0.6.29 htmltools_0.5.2
[10] fansi_0.5.0 magrittr_2.0.1 tensor_1.5
[13] cluster_2.1.2 ROCR_1.0-11 tzdb_0.2.0
[16] globals_0.14.0 vroom_1.5.7 svglite_2.0.0
[19] spatstat.sparse_2.0-0 colorspace_2.0-2 rvest_1.0.2
[22] ggrepel_0.9.1 xfun_0.28 crayon_1.4.2
[25] jsonlite_1.7.2 spatstat.data_2.1-0 survival_3.2-13
[28] zoo_1.8-9 glue_1.5.1 polyclip_1.10-0
[31] gtable_0.3.0 webshot_0.5.2 leiden_0.3.9
[34] car_3.0-12 future.apply_1.8.1 abind_1.4-5
[37] scales_1.1.1 DBI_1.1.1 rstatix_0.7.0
[40] miniUI_0.1.1.1 Rcpp_1.0.7 viridisLite_0.4.0
[43] reticulate_1.22 spatstat.core_2.3-2 bit_4.0.4
[46] htmlwidgets_1.5.4 httr_1.4.2 ellipsis_0.3.2
[49] ica_1.0-2 farver_2.1.0 pkgconfig_2.0.3
[52] sass_0.4.0 uwot_0.1.11 deldir_1.0-6
[55] utf8_1.2.2 labeling_0.4.2 tidyselect_1.1.1
[58] rlang_0.4.12 later_1.3.0 munsell_0.5.0
[61] tools_4.1.2 generics_0.1.1 broom_0.7.10
[64] evaluate_0.14 stringr_1.4.0 fastmap_1.1.0
[67] yaml_2.2.1 goftest_1.2-3 knitr_1.36
[70] bit64_4.0.5 fs_1.5.2 fitdistrplus_1.1-6
[73] purrr_0.3.4 RANN_2.6.1 pbapply_1.5-0
[76] future_1.23.0 nlme_3.1-152 whisker_0.4
[79] mime_0.12 xml2_1.3.3 compiler_4.1.2
[82] rstudioapi_0.13 plotly_4.10.0 png_0.1-7
[85] ggsignif_0.6.3 spatstat.utils_2.3-0 tibble_3.1.6
[88] bslib_0.3.1 stringi_1.7.6 highr_0.9
[91] lattice_0.20-45 Matrix_1.4-0 vctrs_0.3.8
[94] pillar_1.6.4 lifecycle_1.0.1 spatstat.geom_2.3-1
[97] lmtest_0.9-39 jquerylib_0.1.4 RcppAnnoy_0.0.19
[100] data.table_1.14.2 cowplot_1.1.1 irlba_2.3.5
[103] httpuv_1.6.3 R6_2.5.1 promises_1.2.0.1
[106] KernSmooth_2.23-20 gridExtra_2.3 parallelly_1.29.0
[109] codetools_0.2-18 MASS_7.3-54 assertthat_0.2.1
[112] rprojroot_2.0.2 withr_2.4.3 sctransform_0.3.2.9008
[115] mgcv_1.8-38 parallel_4.1.2 hms_1.1.1
[118] grid_4.1.2 rpart_4.1-15 tidyr_1.1.4
[121] rmarkdown_2.11 carData_3.0-4 Rtsne_0.15
[124] git2r_0.29.0 shiny_1.7.1