README.md

In stemangiola/ARMET: Higher-Order Deconvolution Survival Analyses

sccomp - Outlier-aware and count-based compositional analysis of single-cell data ================ Stefano Mangiola

Installation

devtools::install_github("stemangiola/ARMET")

Usage

library(ARMET)

## Loading required package: Rcpp

## Warning: replacing previous import 'tidyr::extract' by 'rstan::extract' when
## loading 'ARMET'

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

data("test_mixture")
data("no_hierarchy_reference")

 estimates = 
    test_mixture |>
    convoluted_glm(
   ~ factor_of_interest,
   .sample = sample,
   .transcript = symbol,
   .abundance = count,
   reference = no_hierarchy_reference 
  )

## Warning in setup_convolved_lm_NON_hierarchical(.data, .formula = .formula, :
## tidybulk says: the data does not have the same number of transcript per sample.
## The data set is not rectangular.

## Warning in aggregate_duplicated_transcripts_bulk(.data, .sample = !!.sample, :
## tidybulk says: for aggregation, factors and logical columns were converted to
## character

## Converted to characters

## factorfactorlogical

## Warning in warning_if_data_is_not_rectangular(.data, !!.sample, !!.transcript, :
## tidybulk says: the data does not have the same number of transcript per sample.
## The data set is not rectangular.

## No group or design set. Assuming all samples belong to one group.

## Warning in warning_if_data_is_not_rectangular(.data, !!.sample, !!.transcript, :
## tidybulk says: the data does not have the same number of transcript per sample.
## The data set is not rectangular.

## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess

## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#tail-ess

## # A tibble: 0 × 11
## # … with 11 variables: par <chr>, mean <dbl>, se_mean <dbl>, sd <dbl>,
## #   2.5% <dbl>, 25% <dbl>, 50% <dbl>, 75% <dbl>, 97.5% <dbl>, n_eff <dbl>,
## #   Rhat <dbl>

## Warning: Expected 5 pieces. Additional pieces discarded in 126 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

## Warning: Expected 5 pieces. Additional pieces discarded in 126 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

## Joining, by = c("Q", "sample")

## Warning: Expected 5 pieces. Additional pieces discarded in 246 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

## Warning: Expected 5 pieces. Additional pieces discarded in 42 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion

## Warning: Expected 5 pieces. Additional pieces discarded in 42 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion

 estimates

## # A tibble: 21 × 5
##    cell_type `.median_(Inte…` .median_factor_… `.sd_(Intercep…` .sd_factor_of_i…
##    <fct>                <dbl>            <dbl>            <dbl>            <dbl>
##  1 endothel…           -0.717          -0.248             0.406            0.456
##  2 epitheli…           -0.582          -0.313             0.418            0.480
##  3 fibrobla…           -0.677          -0.292             0.458            0.486
##  4 mast_cell           -0.804          -0.299             0.485            0.444
##  5 b_memory             2.04            2.30              0.396            0.397
##  6 b_naive              5.93           -0.215             0.230            0.296
##  7 eosinoph…            0.284           0.705             0.486            0.504
##  8 monocyte            -0.321          -0.0574            0.447            0.482
##  9 neutroph…           -0.165           0.130             0.482            0.556
## 10 nk_resti…           -0.391          -0.0737            0.431            0.486
## # … with 11 more rows

stemangiola/ARMET documentation built on July 9, 2022, 1:25 a.m.