<jupyter_start><jupyter_text>*First compiled: August 17, 2017.*  Compare with Monocle 2 for minimal datasets<jupyter_code>suppressMessages(library(monocle))<jupyter_output><empty_output><jupyter_text>Here, we load the data that we exported in [robustness.ipynb](robustness.ipynb).<jupyter_code>df <- read.csv('./data/krumsiek11_blobs/X.csv', header=F)
df_anno <- read.csv('./data/krumsiek11_blobs/smp.csv', header=T)
groups_colors <- as.vector(read.csv('data/krumsiek11_blobs/add/aga_groups_colors.csv', header=F)$V1)
names(groups_colors) = c(0:(length(groups_colors)-1))
cds <- newCellDataSet(t(as.matrix(df)), phenoData = new("AnnotatedDataFrame", data = df_anno))<jupyter_output><empty_output><jupyter_text>Reconstructing the tree for the minimal example We try to reconstruct the tree with different combinations of parameters.  Using max_components = 2<jupyter_code>previous_time <- proc.time()[3]
cds2 <- reduceDimension(cds, verbose = F, max_components = 2) 
cds2 <- orderCells(cds2)
proc.time()[3] - previous_time<jupyter_output>Warning message in if (reduction_method == "DPT") {:
“the condition has length > 1 and only the first element will be used”<jupyter_text>Coloring using the reference clustering "groups".<jupyter_code>plot_complex_cell_trajectory(cds2, color_by = 'as.factor(aga_groups)', show_branch_points = T,
                             cell_size = 0.5, cell_link_size = 0.3, root_states = c(0)) + scale_color_manual(values = groups_colors)<jupyter_output><empty_output><jupyter_text>Using max_components = 10<jupyter_code>previous_time <- proc.time()[3]
cds2 <- reduceDimension(cds, verbose = F, max_components = 10) 
cds2 <- orderCells(cds2)
proc.time()[3] - previous_time
plot_complex_cell_trajectory(cds2, color_by = 'as.factor(aga_groups)', show_branch_points = T,
                             cell_size = 0.5, cell_link_size = 0.3, root_states = c(0)) + scale_color_manual(values = groups_colors)<jupyter_output><empty_output><jupyter_text>Consider the simple tree<jupyter_code>df <- read.csv('./data/krumsiek11/X.csv', header=F)
df_anno <- read.csv('./data/krumsiek11/smp.csv', header=T)
groups_colors <- as.vector(read.csv('data/krumsiek11/add/groups_colors.csv', header=F)$V1)
names(groups_colors) = c(0:(length(groups_colors)-1))
cds <- newCellDataSet(t(as.matrix(df)), phenoData = new("AnnotatedDataFrame", data = df_anno))<jupyter_output><empty_output><jupyter_text>Using max_components = 2<jupyter_code>previous_time <- proc.time()[3]
cds2 <- reduceDimension(cds, verbose = F, max_components = 2) 
cds2 <- orderCells(cds2)
proc.time()[3] - previous_time
plot_complex_cell_trajectory(cds2, color_by = 'as.factor(groups)', show_branch_points = T,
                             cell_size = 0.5, cell_link_size = 0.3, root_states = c(0)) + scale_color_manual(values = groups_colors)<jupyter_output><empty_output><jupyter_text>Using max_components = 5<jupyter_code>previous_time <- proc.time()[3]
cds2 <- reduceDimension(cds, verbose = F, max_components = 5) 
cds2 <- orderCells(cds2)
proc.time()[3] - previous_time
plot_complex_cell_trajectory(cds2, color_by = 'as.factor(groups)', show_branch_points = T,
                             cell_size = 0.5, cell_link_size = 0.3, root_states = c(0)) + scale_color_manual(values = groups_colors)<jupyter_output><empty_output><jupyter_text>Session Info<jupyter_code>sessionInfo()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13479160508424476, 'fraction_numerical': 0.02364765001477978, 'mean_word_length': 5.962962962962963, 'pattern_counts': {'":': 0, '<': 56, '<?xml version=': 0, '>': 37, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '9', 'dir': 'jupyter-structured-clean-dedup', 'id': '286307', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 1282, 'n_tokens_neox': 1209, 'n_words': 201, 'path': 'comparisons/simulated_data/monocle2.ipynb', 'repo_name': 'evanbiederstedt/paga', 'size': '3383'}